ECJ clarifies Database Directive scope in screen scraping case

EC on the legal protection of databases (Database Directive) in a case concerning the extraction of data from a third party’s website by means of automated systems or software for commercial purposes (so called 'screen scraping').

Flight data extracted

The case, Ryanair Ltd vs. PR Aviation BV, C-30/14, is of interest to a range of companies such as price comparison websites. It stemmed from  Dutch company PR Aviation operation of a website where consumers can search through flight data of low-cost airlines  (including Ryanair), compare prices and, on payment of a commission, book a flight. The relevant flight data is extracted from third-parties’ websites by means of ‘screen scraping’ practices.

Ryanair claimed that PR Aviation’s activity:

• amounted to infringement of copyright (relating to the structure and architecture of the database) and of the so-called sui generis database right (i.e. the right granted to the ‘maker’ of the database where certain investments have been made to obtain, verify, or present the contents of a database) under the Netherlands law implementing the Database Directive;

• constituted breach of contract. In this respect, Ryanair claimed that a contract existed with PR Aviation for the use of its website. Access to the latter requires acceptance, by clicking a box, of the airline’s general terms and conditions which, amongst others, prohibit unauthorized ‘screen scraping’ practices for commercial purposes.

Ryanair asked Dutch courts to prohibit the infringement and order damages. In recent years the company has been engaged in several legal cases against web scrapers across Europe.

The Local Court, Utrecht, and the Court of Appeals of Amsterdam dismissed Ryanair’s claims on different grounds. The Court of Appeals, in particular, cited PR Aviation’s screen scraping of Ryanair’s website as amounting to a “normal use” of said website within the meaning of the lawful user exceptions under Sections 6 and 8 of the Database Directive, which cannot be derogated by contract (Section 15).

Ryanair appealed

Ryanair appealed the decision before the Netherlands Supreme Court (Hoge Raad der Nederlanden), which decided to refer the following question to the ECJ for a preliminary ruling: “Does the application of [Directive 96/9] also extend to online databases which are not protected by copyright on the basis of Chapter II of said directive or by a sui generis right on the basis of Chapter III, in the sense that the freedom to use such databases through the (whether or not analogous) application of Article[s] 6(1) and 8, in conjunction with Article 15 [of Directive 96/9] may not be limited contractually?.”

The ECJ’s ruling

The ECJ (without the need of the opinion of the advocate general) ruled that the Database Directive is not applicable to databases which are not protected either by copyright or by the sui generis database right. Therefore, exceptions to restricted acts set forth by Sections 6 and 8 of the Directive do not prevent the database owner from establishing contractual limitations on its use by third parties. In other words, restrictions to the freedom to contract set forth by the Database Directive do not apply in cases of unprotected databases. Whether Ryanair’s website may be entitled to copyright or sui generis database right protection needs to be determined by the competent national court.

The ECJ’s decision is not particularly striking from a legal standpoint. Yet, it could have a significant impact on the business model of price comparison websites, aggregators, and similar businesses. Owners of databases that could not rely on intellectual property protection may contractually prevent extraction and use (“scraping”) of content from their online databases. Thus, unprotected databases could receive greater protection than the one granted by IP law.

Antitrust implications

However, the lawfulness of contractual restrictions prohibiting access and reuse of data through screen scraping practices should be assessed under an antitrust perspective. In this respect, in 2013 the Court of Milan ruled that Ryanair’s refusal to grant access to its database to the online travel agency Viaggiare S.r.l. amounted to an abuse of dominant position in the downstream market of information and intermediation on flights (decision of June 4, 2013 Viaggiare S.r.l. vs Ryanair Ltd). Indeed, a balance should be struck between the need to compensate the efforts and investments made by the creator of the database with the interest of third parties to be granted with access to information (especially in those cases where the latter are not entitled to copyright protection).

Additionally, web scraping triggers other issues which have not been considered by the ECJ’s ruling. These include, but are not limited to trademark law (i.e., whether the use of a company’s names/logos by the web scraper without consent may amount to trademark infringement), data protection (e.g., in case the scraping involves personal data), or unfair competition.

Source: http://www.globallegalpost.com/blogs/global-view/ecj-clarifies-database-directive-scope-in-screen-scraping-case-128701/

Data Scraping - Increasing Accessibility by Scraping Information From PDF

You may have heard about data scraping which is a method that is being used by computer programs in extracting data from an output that comes from another program. To put it simply, this is a process which involves the automatic sorting of information that can be found on different resources including the internet which is inside an html file, PDF or any other documents. In addition to that, there is the collection of pertinent information. These pieces of information will be contained into the databases or spreadsheets so that the users can retrieve them later.

Most of the websites today have text that can be accessed and written easily in the source code. However, there are now other businesses nowadays that choose to make use of Adobe PDF files or Portable Document Format. This is a type of file that can be viewed by simply using the free software known as the Adobe Acrobat. Almost any operating system supports the said software. There are many advantages when you choose to utilize PDF files. Among them is that the document that you have looks exactly the same even if you put it in another computer so that you can view it. Therefore, this makes it ideal for business documents or even specification sheets. Of course there are disadvantages as well. One of which is that the text that is contained in the file is converted into an image. In this case, it is often that you may have problems with this when it comes to the copying and pasting.

This is why there are some that start scraping information from PDF. This is often called PDF scraping in which this is the process that is just like data scraping only that you will be getting information that is contained in your PDF files. In order for you to begin scraping information from PDF, you must choose and exploit a tool that is specifically designed for this process. However, you will find that it is not easy to locate the right tool that will enable you to perform PDF scraping effectively. This is because most of the tools today have problems in obtaining exactly the same data that you want without personalizing them.

Nevertheless, if you search well enough, you will be able to encounter the program that you are looking for. There is no need for you to have programming language knowledge in order for you to use them. You can easily specify your own preferences and the software will do the rest of the work for you. There are also companies out there that you can contact and they will perform the task since they have the right tools that they can use. If you choose to do things manually, you will find that this is indeed tedious and complicated whereas if you compare this to having professionals do the job for you, they will be able to finish it in no time at all. Scraping information from PDF is a process where you collect the information that can be found on the internet and this does not infringe copyright laws.

Source: http://ezinearticles.com/?Increasing-Accessibility-by-Scraping-Information-From-PDF&id=4593863

Web Scraping: working with APIs

APIs present researchers with a diverse set of data sources through a standardised access mechanism: send a pasted together HTTP request, receive JSON or XML in return. Today we tap into a range of APIs to get comfortable sending queries and processing responses.

These are the slides from the final class in Web Scraping through R: Web scraping for the humanities and social sciences

This week we explore how to use APIs in R, focusing on the Google Maps API. We then attempt to transfer this approach to query the Yandex Maps API. Finally, the practice section includes examples of working with the YouTube V2 API, a few ‘social’ APIs such as LinkedIn and Twitter, as well as APIs less off the beaten track (Cricket scores, anyone?).

I enjoyed teaching this course and hope to repeat and improve on it next year. When designing the course I tried to cram in everything I wish I had been taught early on in my PhD (resulting in information overload, I fear). Still, hopefully it has been useful to students getting started with digital data collection, showing on the one hand what is possible, and on the other giving some idea of key steps in achieving research objectives.

Download the .Rpres file to use in Rstudio here

A regular R script with code-snippets only can be accessed here

Slides from the first session here

Slides from the second session here

Slides from the third session here

Source: http://www.r-bloggers.com/web-scraping-working-with-apis/

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.

Source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416

Scraping the Royal Society membership list

To a data scientist any data is fair game, from my interest in the history of science I came across the membership records of the Royal Society from 1660 to 2007 which are available as a single PDF file. I’ve scraped the membership list before: the first time around I wrote a C# application which parsed a plain text file which I had made from the original PDF using an online converting service, looking back at the code it is fiendishly complicated and cluttered by boilerplate code required to build a GUI. ScraperWiki includes a pdftoxml function so I thought I’d see if this would make the process of parsing easier, and compare the ScraperWiki experience more widely with my earlier scraper.

The membership list is laid out quite simply, as shown in the image below, each member (or Fellow) record spans two lines with the member name in the left most column on the first line and information on their birth date and the day they died, the class of their Fellowship and their election date on the second line.

Later in the document we find that information on the Presidents of the Royal Society is found on the same line as the Fellow name and that Royal Patrons are formatted a little differently. There are also alias records where the second line points to the primary record for the name on the first line.

pdftoxml converts a PDF into an xml file, wherein each piece of text is located on the page using spatial coordinates, an individual line looks like this:

<text top="243" left="135" width="221" height="14" font="2">Abbot, Charles, 1st Baron Colchester </text>

This makes parsing columnar data straightforward you simply need to select elements with particular values of the “left” attribute. It turns out that the columns are not in exactly the same positions throughout the whole document, which appears to have been constructed by tacking together the membership list A-J with that of K-Z, but this can easily be resolved by accepting a small range of positions for each column.

Attempting to automatically parse all 395 pages of the document reveals some transcription errors: one Fellow was apparently elected on 16th March 197 – a bit of Googling reveals that the real date is 16th March 1978. Another fellow is classed as a “Felllow”, and whilst most of the dates of birth and death are separated by a dash some are separated by an en dash which as far as the code is concerned is something completely different and so on. In my earlier iteration I missed some of these quirks or fixed them by editing the converted text file. These variations suggest that the source document was typed manually rather than being output from a pre-existing database. Since I couldn’t edit the source document I was obliged to code around these quirks.

ScraperWiki helpfully makes putting data into a SQLite database the simplest option for a scraper. My handling of dates in this version of the scraper is a little unsatisfactory: presidential terms are described in terms of a start and end year but are rendered 1st January of those years in the database. Furthermore, in historical documents dates may not be known accurately so someone may have a birth date described as “circa 1782? or “c 1782?, even more vaguely they may be described as having “flourished 1663-1778? or “fl. 1663-1778?. Python’s default datetime module does not capture this subtlety and if it did the database used to store dates would need to support it too to be useful – I’ve addressed this by storing the original life span data as text so that it can be analysed should the need arise. Storing dates as proper dates in the database, rather than text strings means we can query the database using date based queries.

ScraperWiki provides an API to my dataset so that I can query it using SQL, and since it is public anyone else can do this too. So, for example, it’s easy to write queries that tell you the the database contains 8019 Fellows, 56 Presidents, 387 born before 1700, 3657 with no birth date, 2360 with no death date, 204 “flourished”, 450 have birth dates “circa” some year.

I can count the number of classes of fellows:

select distinct class,count(*) from `RoyalSocietyFellows` group by class

Make a table of all of the Presidents of the Royal Society

select * from `RoyalSocietyFellows` where StartPresident not null order by StartPresident desc

…and so on. These illustrations just use the ScraperWiki htmltable export option to display the data as a table but equally I could use similar queries to pull data into a visualisation.

Comparing this to my earlier experience, the benefits of using ScraperWiki are:

•    Nice traceable code to provide a provenance for the dataset;

•    Access to the pdftoxml library;

•    Strong encouragement to “do the right thing” and put the data into a database;

•    Publication of the data;

•    A simple API giving access to the data for reuse by all.

My next target for ScraperWiki may well be the membership lists for the French Academie des Sciences, a task which proved too complex for a simple plain text scraper…

Source: https://scraperwiki.wordpress.com/2012/12/28/scraping-the-royal-society-membership-list/

Data Scraping Services - Login to Website Programmatically using C# for Web Scraping

In many scenario the data is available after login that you want to scrape. So to reach at the page where data is located you need to implement code in web scraper  that automatically takes usename/email and password to login into website, once login is done you can do crawling and parsing as required.

Many third party web scraping application provides functionality where you can locate login url and set login parameters and that login task will be called when scraper start and do web scraping.

Below is C# example of programmatically  login to demo login page

http://demo.webdata-scraping.com/login.php

Below is HTML code of Login form:

<form class="form-signin" id="login" method="post" role="form"> <h3 class="form-signin-heading">Please sign in</h3> <a href="#" id="flipToRecover" class="flipLink"> <div id="triangle-topright"></div> </a> <input type="email" class="form-control" name="loginEmail" id="loginEmail" placeholder="Email address" required autofocus> <input type="password" class="form-control" name="loginPass" id="loginPass" placeholder="Password" required> <button class="btn btn-lg btn-primary btn-block" name="login_submit" id="login_submit" type="submit">Sign in</button> </form>

<form class="form-signin" id="login" method="post" role="form">

            <h3 class="form-signin-heading">Please sign in</h3>

            <a href="#" id="flipToRecover" class="flipLink">

              <div id="triangle-topright"></div>

            </a>

            <input type="email" class="form-control" name="loginEmail" id="loginEmail" placeholder="Email address" required autofocus>

            <input type="password" class="form-control" name="loginPass" id="loginPass" placeholder="Password" required>

            <button class="btn btn-lg btn-primary btn-block" name="login_submit" id="login_submit" type="submit">Sign in</button>

</form>

In this code you can notice there is ID for email input box that is id=”loginEmail”  and password input box that is id=”loginPass”

so by taking this ID we will use below two method of webBrowser control and fill the value of each input box using following code

webBrowser1.Document.GetElementById("loginEmail").InnerText =textBox1.Text.ToString(); webBrowser1.Document.GetElementById("loginPass").InnerText = textBox2.Text.ToString();

webBrowser1.Document.GetElementById("loginEmail").InnerText =textBox1.Text.ToString();

webBrowser1.Document.GetElementById("loginPass").InnerText = textBox2.Text.ToString();

After the value filled to Email and Password input box we will just call click event of submit button which is named as Sign In

webBrowser1.Document.GetElementById("login_submit").InvokeMember("click");

webBrowser1.Document.GetElementById("login_submit").InvokeMember("click");

So this is very basic example how you can login to website programatically when you need to access data that is available after login to website.  This is very simple way in which you can work with Web Browser control but there are some other way as well using which you can do same thing.

Source: http://webdata-scraping.com/login-website-programmatically-using-c-web-scraping/

Data Extraction Services

Are you finding it tedious to perform your routine tasks as well as finding time to research for some information? Don't worry; all you have to do is outsource data extraction requirements to reliable service providers such as Hi-Tech BPO Services.

We can assist you in finding, extracting, gathering, processing and validating all the required data through our effective data extraction services. We can extract data from any given source such as websites, databases, printed documents, directories, etc.

With a whole plethora of data extraction services solutions; we are definitely a one stop solution to all your data extraction services requirements.

For utilizing our data extraction services, all you have to do is outsource data extraction requirements to us, and we will create effective strategies and extract the required data from all preferred sources. Then we will arrange all the extracted data in a systematic order.

Types of data extraction services provided by our data extraction India unit:

The data extraction India unit of Hi-Tech BPO Services can attend to all types of outsource data extraction requirements. Following are just some of the data extraction services we have delivered:

•    Data extraction from websites
•    Data extraction from databases
•    Extraction of data from directories
•    Extracting data from books
•    Data extraction from forms
•    Extracting data from printed materials

Features of Our Data Extraction Services:

•    Reliable collection of resources for data extraction
•    Extensive range of data extraction services
•    Data can be extracted from any available source be it a digital source or a hard copy source
•    Proper researching, extraction, gathering, processing and validation of data
•    Reasonably priced data extraction services
•    Quality and confidentiality ensured through various strict measures

Our data extraction India unit has the competency to handle any of your data extraction services requirements. Just provide us with your specific requirements and we will extract data accordingly from your preferred resources, if particularly specified. Otherwise we will completely rely on our collection of resources for extracting data for you.

Source: http://www.hitechbposervices.com/data-extraction.php

Data Scraping - One application or multiple?

I have 30+ sources of data I scrape daily in various formats (xml, html, csv). Over the last three years Ive built 20 or so c# console applications that go out, download the data and re-format it into a database. But Im curious what other people are doing for this type of task. Are people building one tool that has a lot of variables and inputs or are people designing 20+ programs to scrape and parse this data. Everything is hard-coded into each console and run through Windows Task Manager.

Added a couple additional thoughts/details:

    Of the 30 sources, they all have unique properties, all are uploaded into individual MySQL tables and all have varying frequencies. For example, one data source is hit once a minute, another on 5 minute intervals. Majority are once an hour and once a day.

At current I download the formats (xml, csv, html), parse them into a formatted csv and put them into staging folders. Within that folder, I run an application that reads a config file specific to the folder. When a new csv is added to the folder, the application then uploads the data into the specific MySQL tables designated in the config file.

Im wondering if it is worth re-building all this into a larger complex program that is more capable of dynamically adding content+scrapes and adjusting to format changes.

Looking for outside thoughts.

5 Answers

What you are working on is basically ETL. So at a high level you need an export component (get stuff) a transform component (map to known format) and a load (take known format and put stuff somewhere). If you are comfortable being tied to a RDBMS you could use something like SQL Server SSIS packages. What I would do is create a host application that managed common aspects of the overall process (errors, and pipeline processing). Then make the specifics of the E, T, and L pluggable. A low ceremony way to get this would be to host the powershell runtime and create each seesion with common context objects that the scripts will use to communicate. You get a built in pipe and filter model for scripts and easy, safe extensibility. This design has worked extremely for my team with a similar situation.

Resist the temptation to rewrite.

However, for new code, you could plan for what you know has already happened. Write a retrieval mechanism that you can reuse through configuration. Write a translation mechanism that you can reuse (maybe in a library that you can call with very little code). Write a saving mechanism that can be called or configured.

At this point, you've written #21(+). Now, the following ones can be handled with a tiny bit of code and configuration. Yay!

(You may want to implement this in a service that handles multiple conversions, but weight the benefits of it versus the ability to separate errors in one module from the rest.)

1

It depends - if you need the scrapers to feed into a single application/database and have a uniform data format, it makes sense to have them all in a single program (possibly inheriting from a common base scraper).

If not and they are completely unrelated to each other, might as well keep them separate so changes in one have no effect on another.

Update, following edits to question:

Don't change things just for the sake of change. You have something that works, don't mess with it too much.

Since your data sources and data sinks are all separate from each other, combining them into one application will simply create a very complicated application that will be very difficult to change when needed.

Since the scrapers are separate, keep the separation as you have it now.

As sbrenton said, this most falls in with ETL. You should check out Talend Open Studio. It specializes in handling data flows like I imagine yours are as well as other things like duplicate removal, normalization of fields; tens/hundreds of drag and drop ETL components, you can also write custom code as Talend is a code generator as well, either Java or Perl are options. You can also use Talend to execute system commands. I use it for my ETL work, although not in production, in production we will use SSIS, mostly due to lots of other Microsoft products in house.

You may want to use some good scheduling library, like Quartz.NET.

In a few words, here's what you can expect:

    Your tasks are represented by classes and not processes

    You can set and forget tasks and scale across multiple servers

    You have an out-of-the-box system to actually take care of what is needed to be run when, what failed and needs to be re-run, etc. etc.

Source: http://programmers.stackexchange.com/questions/118077/data-scraping-one-application-or-multiple/118098#118098

Web scraping using Python without using large frameworks like Scrapy

scrapy-big-logoIf you need publicly available data from scraping the Internet, before creating a webscraper, it is best to check if this data is already available from public data sources or APIs. Check the site’s FAQ section or Google for their API endpoints and public data.

Even if their API endpoints are available you have to create some parser for fetching and structuring the data according to your needs.

Scrapy is a well established framework for scraping, but it is also a very heavy framework. For smaller jobs, it may be overkill and for extremely large jobs it is very slow.

So if you would like to roll up your sleeves and build your own scraper, continue reading.

Here are some basic steps performed by most webspiders:

1) Start with a URL and use a HTTP GET or PUT request to access the URL
2) Fetch all the contents in it and parse the data
3) Store the data in any database or put it into any data warehouse
4) Enqueue all the URLs in a page
5) Use the URLs in queue and repeat from process 1
Here are the 3 major modules in every web crawler:
1) Request/Response handler.
2) Data parsing/data cleansing/data munging process.
3) Data serialization/data pipelines.

Lets look at each of these modules and see what they do and how to use them.

Request/Response handler

Request/response handlers are managers who make http requests to a url or a group of urls, and fetch the response objects as html contents and pass this data to the next module. If you use Python for performing request/response url-opening process libraries such as the following are most commonly used

1) urllib(20.5. urllib – Open arbitrary resources by URL – Python v2.7.8 documentation) -Basic python library yet high-level interface for fetching data across the World Wide Web.

2) urllib2(20.6. urllib2 – extensible library for opening URLs – Python v2.7.8 documentation) – extensible library of urllib, which would handle basic http requests, digest authentication, redirections, cookies and more.

3) requests(Requests: HTTP for Humans) – Much advanced request library

which is built on top of basic request handling libraries.

Data parsing/data cleansing/data munging process

This is the module where the fetched data is processed and cleaned. Unstructured data is transformed into structured during this processing. Usually  a set of Regular Expressions (regexes) which perform pattern matching and text processing tasks on the html data are used for this processing.

In addition to regexes, basic string manipulation and search methods are also used to perform this cleaning and transformation. You must have a thorough knowledge of regular expressions and so that you could design the regex patterns.

Data serialization/data pipelines

Once you get the cleaned data from the parsing and cleaning module, the data serialization module will be used to serialize the data according to the data models that you require. This is the final module that will output data in a standard format that can be stored in databases, JSON/CSV files or passed to any data warehouses for storage. These tasks are usually performed by libraries listed below

1) pickle (pickle – Python object serialization) –  This module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure

2) JSON (JSON encoder and decoder)

3) CSV (https://docs.python.org/2/library/csv.html)

4) Basic database interface libraries like pymongo (Tutorial – PyMongo),mysqldb ( on python.org), sqlite3(sqlite3 – DB-API interface for SQLite databases)

And many more such libraries based on the format and database/data storage.

Basic spider rules

The rules to follow while building a spider are to be nice to the sites you are scraping and follow the rules in the site’s spider policies outlined in the site’s robots.txt.

Limit the  number of requests in a second and build enough delays in the spiders so that  you don’t adversely affect the site.

It just makes sense to be nice.

We will cover more techniques in future articles

Source: http://learn.scrapehero.com/webscraping-using-python-without-using-large-frameworks-like-scrapy/

How Web Data Extraction Services Impact Startups

Starting a business has its fair share of ebbs and flows – it can be extremely challenging to get a new business off the blocks, and extremely rewarding when everything goes according to plan and yields desired results. For startups, it is important to get the nuances of running a business right from day one. To succeed in an immensely competitive space, startups need to perform above and beyond expectation right from the start, and one of the factors that can be of great help during the growing years of a startup is web data extraction.

Web data extraction through crawling and scraping, a highly efficient information gathering process, can be used in many creative ways to bring about major change in the performance graph of a startup. With effective web data extraction services acquired by outsourcing to a reputed company, the business intelligence gathered and the numerous possibilities associated with it, web crawling and extraction services can indeed become the difference maker for a startup, propelling it to the heights of success.

What drives the success of web data extraction?

When it comes to figuring out the perfect, balanced web data collection methodology for startups, there are a lot of crucial factors that come into play. Some of these are associated with the technical aspects of data collection, the approach used, the time invested, and the tools involved. Others have more to do with the processing and analysis of collected information and its judicious use in formulating strategies to take things forward.

Web Crawling Services & Web Scraping Services

With the advent of highly professional web data extraction services providers, massive amounts of structured, relevant data can be gathered and stored in real time, and in time, productively used to further the business interests of a startup. As a new business owner, it is important to have a high-level knowledge of the modern and highly functional web scraping tools available for use. This will help to utilize the prowess of competent data extraction services. This in turn can assist both in the immediate and long-term revenue generation context.

Web Data Extraction for Startups

From the very beginning, the dynamics of startups is different from that of older, well-established businesses. The time taken by the new business entity in proving its capabilities and market position needs to be used completely and effectively. Every day of growth and learning needs to add up to make a substantial difference. In this period, every plan and strategy, every execution effort, and every move needs to be properly thought out.

In such a trying situation where there is little margin for error, it pays to have accurate, reliable, relevant and actionable business intelligence. This can put you in firm control of things by allowing you to make informed business decisions and formulate targeted, relevant and growth oriented business strategies. With powerful web crawling, the volume of data gathered is varied, accurate and relevant. This data can then be studied minutely, analyzed in detail and arranged into meaningful clusters. With this weapon in your arsenal, you can take your startup a long way with smart decisions and clever implementations.

Web data extraction is a task best handled by professionals who have had rich experience in the field. Often, in-house web scraping teams are difficult to assemble and not economically viable to maintain, especially for startups. For a better solution, you can outsource your web scraping needs to a reliable web data extraction service for data collection. This way, you can get all the relevant intelligence you need without overstraining your workforce or having to employ additional personnel to handle web scraping. The company you outsource your work to can easily scrape data from multiple sources as per your requirements, and furnish you with actionable business intelligence that can help you take a lead in a competitive market.

Different Ways for Startups to use Web Data Extraction

Web scraping can be employed for many different purposes to yield different kinds of relevant data that generate actionable insights. For a startup, the important decision is how to use this powerful technique to provide valuable information that can make a difference for the future prospects of the company. Here are some interesting possibilities when it comes to impactful web data extraction for startups –

Fishing for Social Rankings and Backlinks

One of the most important business processes for a startup is competition analysis. This is one area where web data extraction can come across as an invaluable enabler. In the past, many startups have effectively used web scraping to fish for backlinks and social rankings related to competing companies.

Backlinks are important to reach a greater mass of better-targeted audiences, which can go on to increase customer base with minimal efforts. Social ranking is also an immensely important factor, as social actions on the internet are building blocks of opinion and reputation generation in this day and age. Keeping this in mind, you can use web data extraction to scrape for social rankings and backlinks related to content generated by your competing companies. After careful analysis, it is possible to arrive at concrete conclusions regarding what your competitors are doing well, and what sells the best.

This information is gold for marketers and sales personnel, and can be used to discern exactly what needs to be done to increase social buzz, generate favorable opinion, and win over customers from your competitors. You can also use this technique to develop high authority backlinks that help with SEO, targeted reach and organic traffic for your business website. For competition analysis, web scraping is a formidable tool.

Sourcing Contact Information

Another important aspect of business that startups can never ignore is good networking. Whether it is with customers, prospective customers, industry peers, partners, or competitors, excellent networking and open, transparent communication is essential for the success of your startup. For effective communication and networking, you need a large, solid list of contact information pertaining to your exact requirements.

Scraping data from multiple web sources gives you the perfect method of achieving this. With automated, fast web scraping, you can in a short time collect a wealth of important contact information that can be leveraged in many different ways. Whether it is the formation of lasting business relationships or making potential customers aware of what you have on offer, this information has the power to propel your startup to new levels of recognition.

For Ecommerce

If you sell your products and services online and want to stay on top of the competition when it comes to variety, pricing analysis, and special deals and offers, web scraping is the way to go. For many e-commerce startups, the problem of high CTR and low conversion is a stumbling block to higher bottom lines. To remedy problems like these and to ensure better sales, it is always a good idea to have a clear insight about your competition.

Future of Retail Industry

With web data extraction, you can be always aware of what competing companies are doing in terms of pricing strategies, product diversity and special customer offers. By considering that information while evaluating and cementing your own strategies, you can always ensure that you provide better value and range of products and services than your competitors, and therefore stay ahead of the competition.

For Marketing, Brand Promotion and Advertisement

For startups, the first wave of promotion and marketing is the one that holds the key to your long-term business success. It is during this phase that the first and most important public perception of your company is formed, and the rudiments of public opinion start taking shape. For this reason, it is crucial to be on point with your marketing and promotion during the early, formative years of your business.

To achieve this, you need a clear, in-depth understanding of your target audience. You need to categorize your target audience on the basis of many factors like age, gender, demographics, income groups and tastes and preferences. Such detailed understanding can only be possible when you have a large wealth of social data pertaining to your target audience. There is no better way of achieving this than by web data extraction.

Love your brand

With the help of data extraction services, you can gather large chunks of relevant data regarding your target audience which can help you accurately evaluate the potential of each prospective customers as a possible addition to your business family. To ensure that you have a steady, early wave of customers to take your business off the blocks at a rapid pace, you need to devise marketing campaigns, promotional strategies and advertisements in accordance with the customer knowledge you drive through your web scraping efforts. This is a foolproof strategy to have marketing and promotional plans in place that achieve goals, bring in new business and provide your company with enough initial momentum to carry it through the later years of success.

To conclude, web data extraction can be a veritable tool in the hands of a startup. With the proper use and leveraging of this technique, your startup can gather the required business intelligence to shine in a competitive market and become a favorite with the customer base. Working with the right web data extraction company can be one of the most important business decisions you make as a startup owner.

Source: https://www.promptcloud.com/blog/web-data-extraction-services-for-startups/

What is Blog Scraping Service?

Blog scraping is the one of the best service to increase the traffic of the site by commenting about blogs or writing review about blogs in SEO field. Most of the Blogs will allow their reader to review or write their own comments or suggestion or ideas or thoughts in the blogs.

Nowadays in the internet world we can find the number of blogs and sites related to various topics or various products. Main concept of this service is increase traffic of website by commenting others blogs. This is very simple and easiest method. But the main difficultly we face here is getting approval from moderator of the site which may take more time or maybe we won’t get the approval.

Hence Web Scraping seo is planning to provide this blog scraping service without approval as many moderators do not have the time to read and approved each and every comment written by various visitors. We will find the High PR pages on the various blogs related to your website content and write the own comment about those blogs and provide the link of your website or anchor text. We don’t have the option or the way to track the blogs whether it is approved by moderator or not. We will give the link with comments what we have typed on the blogs as a report. It will increase the back link and increase the traffic.

What are the features of Blog scraping Service?

•    Will provide the comments or reviews to blogs which having related niche to your product.
•    Will write comments only high density or high ranking blogs.
•    Fast and More accurate promotion compared to other service
•    Understand the Blogs by reading carefully and comment accordingly
•    This service is optimized and SEO friendly.

What are the benefits of Blog scraping Service?

•    Effect of time spending for this service is very less.
•    This service is best method to increase your site traffic with minimal effect and cost.
•    Increase your web site rank in all search engines.
•    Reach your site to more number of audiences.
•    Increase your product sale.
•    Fast and more results.

What are the advantages of using this service in Web Scraping SEO?

•    Web Scraping SEO is one the top SEO service provider in the SEO Market.
•    Expert people working on Blog commenting service will always do analysis to find the high traffic blogs.
•    Web Scraping SEO will get the approval from Blogs administrator easily.
•    Provides High Quality Service with reasonable price.
•    Provides on time delivery.
•    More flexible to clients.
•    Always met the Client expectation and Provide quality service.

Frequently Asked Questions

Q: Will you provide the approval for each comment you typed on the blogs from blogsite moderator?

A: No, we are only responsible for creating comments for your website but we won’t wait for moderation approval, because Moderator is responsible for Approval, He may take time for approval that is according to Moderator’s scope. We will give only the blog links and the comments to you as a report.

Q: Do you have any system or software to track the approval of blog?

A: We don’t have any system or software to track the approval, we do comments in those top blog sites according to the matching keyword. That is only our job approval is from moderator side.

Q: Why you can’t get the approval for comments from moderator?

A: I can clearly answer this one, Because nowadays everyone is busy particularly the blogsite Moderators for that reason our comments got approved late. But we are not going to wait for that because we have a lot of works to do, But I assure you, that with the final reports that contains how many sites we have uploaded with your comments in MS Excel format will reach you.

Q: How do you select the blogs for commenting?

A: We are going to select top ranking blog sites related to your keywords, According to the benefits of your product we will give proper and attractive comments carefully.

Source: http://www.Web Scrapingseo.com/blog-scraping-service.aspx

Web Scraping: Startups, Services & Market

I got recently interested in startups using web scraping in a way or another and since I find the topic very interesting I wanted to share with you some thoughts. [Note that I’m not an expert. To correct me / share your knowledge please use the comment section]

Web scraping is everything but a new technique. However with more and more data shared on internet (from user generated content like social networks & review websites to public/government data and the growing number of online services) the amount of data collected and the use cases possible are increasing at an incredible pace.

We’ve entered the age of “Big Data” and web scraping is one of the sources to feed big data engines with fresh new data, let it be for predictive analytics, competition monitoring or simply to steal data.

From what I could see the startups and services which are using “web scraping” at their core can be divided into three categories:

•    the shovel sellers (a.k.a we sell you the technology to do web scraping)

•    the shovel users (a.k.a we use web scraping to extract gold and sell it to our users)

•    the shovel police (a.k.a the security services which are here to protect website owners from these bots)

The shovel sellers

From a technology point of view efficient web scraping is quite complicated. It exists a number of open source projects (like Beautiful Soup) which enable anyone to get up and running a web scraper by himself. However it’s a whole different story when it has to be the core of your business and that you need not only to maintain your scrapers but also to scale them and to extract smartly the data you need.

This is the reason why more and more services are selling “web scraping” as a service. Their job is to take care about the technical aspects so you can get the data you need without any technical knowledge. Here some examples of such services:

    Grepsr
    Krakio
    import.io
    promptcloud
    80legs
    Proxymesh (funny service: it provides a proxy rotator for web scraping. A shovel seller for shovel seller in a way)
    scrapingHub
    mozanda

The shovel users

It’s the layer above. Web scraping is the technical layer. What is interesting is to make sense of the data you collect. The number of business applications for web scraping is only increasing and some startups are really using it in a truly innovative way to provide a lot of value to their customers.

Basically these startups take care of collecting data then extract the value out of it to sell it to their customers. Here some examples:

Sales intelligence. The scrapers screen marketplaces, competitors, data from public markets, online directories (and more) to find leads. Datanyze, for example, track websites which add or drop javascript tags from your competitors so you can contact them as qualified leads.

Marketing. Web scraping can be used to monitor how your competitors are performing. From reviews they get on marketplaces to press coverage and financial published data you can learn a lot. Concerning marketing there is even a growth hacking class on udemy that teaches you how to leverage scraping for marketing purposes.

Price Intelligence. A very common use case is price monitoring. Whether it’s in the travel, e-commerce or real-estate industry monitoring your competitors’ prices and adjusting yours accordingly is often key. These services not only monitor prices but with their predictive algorithms they can give you advice on where the puck will be. Ex: WisePricer, Pricing Assistant.

Economic intelligence, Finance intelligence etc. with more and more economical, financial and political data available online a new breed of services, which collect and make sense of it, are rising. Ex: connotate.

The shovel police

Web scraping lies in a gray area. Depending on the country or the terms of service of each website, automatically collecting data via robots can be illegal. Whatever the laws say it becomes crucial for some services to try to block these crawlers to protect themselves. The IT security industry has understood it and some startups are starting to tackle this problem. Here are 3 services which claim to provide solutions to stop bots from crawling your website:

•    Distil
•    ScrapeSentry
•    Fireblade

From a market point of view

A couple of points on the market to conclude:

•    It’s hard to assess how big the “web scraping economy” is since it is at the intersection of several big industries (billion dollars): IT security, sales, marketing & finance intelligence. This technique is of course a small component of these industries but is likely to grow in the years to come.

•    A whole underground economy also exists since a lot of web scraping is done through “botnets” (networks of infected computers)

•    It’s a safe bet to say that more and more SaaS (like Datanyze pr Pricing Assistant) will find innovative applications for web scraping. And more and more startups will tackle web scraping from the security point of view.

•    Since these startups are often entering big markets through a niche product / approach (web scraping is a not the solution to everything, there are more a feature) they are likely to be acquired by bigger players (in the security, marketing or sales tools industries). The technological barrier are there.

Source: http://clementvouillon.com/article/web-scraping-startups-services-market/

Earn Money From Price Comparison Through Web Scraping

Many individuals discover the pot of gold just within their reach. They have realized that there is money in the web. Cyber technology has blessed mankind with so many benefits that makes money very possible by just some clicks on the mouse and keyboard. Building a price comparison website is an effective way of helping clients find their desired products while you as the owner earn money at the same time.

Building price comparison websites

There is indeed much money in building price comparison websites but it is not an easy task especially for a novice in maintaining a website of one’s own. Since this entails some serious programming and ample familiarity with data feeds, you have to have a good working plan. In addition, what you are venturing into is greater than the usual blogs about just anything that you can think of. Furthermore, you are stepping into the vast field of electronic marketing, therefore you must be ready.

The first point of consideration is to identify which products or services are you going to include in your website. Choose a product or service that you and a majority of clients are mostly interested in. Suppose you want you to choose sports as your theme then you can include items and prices of sports gear, clothing such as uniforms, training videos, books, and other safety stuff. You need to do some research and even a survey to determine whether the goods and services you are promoting on your website are in demand and are what most people want to know. Moreover, it is on this stage that you may need the help of experts and veterans in the field of building to be assured that you are on the right track.

In addition, be willing to change in case your chosen category is not gaining readership or visitors. Then evaluate whether you need to expand or to be more specific in your description of the products and the comparison of the prices. Make your site prominent by search engine optimization (SEO) and make sure to acknowledge also that not too many people visit a site that is not free.

Helping visitors choose the best product/services

Good marketing strategy starts with knowing who your target audience are. There is indeed a need to do a lot of planning and research in order to understand your client’s needs and preferences. Moreover, knowing them thoroughly leads to achieving 100% consumer satisfaction. When you have provided everything they need to know about certain products, they would not need to seek elsewhere which will also gain you more regular visitors. Remember that your audience are members of communities and social networks such that there is a great possibility that they would spread the word around about the good services you are offering.

If there is a need to conduct a survey in addition to research, you should resort to it. In this manner you can discover what goods and services are not yet completely exhausted by the other websites or web creators. Ample knowledge about your potential visitors and consumers will surely make you effectively provide them with adequate statistics for their needs.

Your site will then look like a complete guidebook for them that will give them the best value for their money. Therefore, it must be thoroughly filled with product details, uses, options, and prices.

Making money as affiliate of eCommerce websites

Maintaining a price comparison website gives you less worry about getting paid or having your products bought and sold because income comes in through advertising and affiliate sales. Affiliate marketing is a way of earning money online by serving as a publisher for promotion of products, services or sites of businesses. The affiliate receives rewards from businesses for each visitor or client that comes to the business website or buys its product through the efforts of the advertising and promotion that is made by the affiliate. This is the online version of the concept of agent or referral fee sales channel. Aside from website owners, bloggers as well as members of community forums can also serve as affiliates. The affiliate earns money in three ways: through pay per link; pay per sale and pay per lead.

Trust in the reliability of the product - You should have a personal belief or confidence in the product you are promoting not only because it makes you sound more convincing, but also because you need to maintain your clients and establish credibility in your blog or website. In other words, don’t just pick any product. If you cannot use them personally, they should at least have several positive reviews and no negative ones.

Maintain credibility with readers and fellow bloggers - Befriend your readers and your co-bloggers by answering their queries sincerely and quickly. Your friendly attitude can win you their trust which is a very vital element of affiliate marketing.

Do reviews - In addition to publishing price comparison, you can gain more visitors by writing about the product and do proper SEO (Search Engine Optimization). So the expected happens, the more prominent the product becomes online, the higher will be your income.

Link with friends thru social media - Your friends have friends and their friends have also friends. Just think of how powerful your social media site can be when you post your link on your account on Facebook, Twitter or MySpace and others. Since trust is built on friendship, it is easy to get clients from among your friends and their friends.

Overall, you get all pertinent information about certain products through web data mining or web scraping. All you need to do is to be keen to the needs of your clients and use web content extraction efficiently.

Source: http://www.loginworks.com/earn-money-price-comparison-web-scraping/

A Guide to Web Scraping Tools

Web Scrapers are tools designed to extract / gather data in a website via crawling engine usually made in Java, Python, Ruby and other programming languages.Web Scrapers are also called as Web Data Extractor, Data Harvester , Crawler and so on which most of them are web-based or can be installed in local desktops.

Its main purpose is to enable webmasters, bloggers, journalist and virtual assistants to harvest data from a certain website whether text, numbers, contact details and images in a structured way which cannot be done easily thru manual copy and paste method. Typically, it transforms the unstructured data on the web, from HTML format into a structured data stored in a local database or spreadsheet or automates web human browsing.

Web Scraper Usage

Web Scrapers are also being used by SEO and Online Marketing Analyst to pull out some data privately from the competitor’s website such as high targeted keywords, valuable links, emails & traffic sources that were also perform by SEOClerk, Google and many other web crawling sites.

Includes:

•    Price comparison
•    Weather data monitoring
•    Website change detection
•    Research
•    Web mash up
•    Info graphics
•    Web data integration
•    Web Indexing & rank checking
•    Analyze websites quality links

List of Popular Web Scrapers

There are hundreds of Web Scrapers today available for both commercial and personal use. If you’ve never done any web scraping before, there are basic

Web scraping tools like YahooPipes, Google Web Scrapers and Outwit Firefox extensions that it’s good to start with but if you need something more flexible and has extra functionality then,  check out the following:

HarvestMan [ Free Open Source]

HarvestMan is a web crawler application written in the Python programming language. HarvestMan can be used to download files from websites, according to a number of user-specified rules. The latest version of HarvestMan supports as much as 60 plus customization options. HarvestMan is a console (command-line) application. HarvestMan is the only open source, multithreaded web-crawler program written in the Python language. HarvestMan is released under the GNU General Public License.Like Scrapy, HarvestMan is truly flexible however, your first installation would not be easy.

Scraperwiki [Commercial]

Using a minimal programming you will be able to extract anything. Off course, you can also request a private scraper if there’s an exclusive in there you want to protect. In other words, it’s a marketplace for data scraping.

Scraperwiki is a site that encourages programmers, journalists and anyone else to take online information and turn it into legitimate datasets. It’s a great resource for learning how to do your own “real” scrapes using Ruby, Python or PHP. But it’s also a good way to cheat the system a little bit. You can search the existing scrapes to see if your target website has already been done. But there’s another cool feature where you can request new scrapers be built.  All in all, a fantastic tool for learning more about scraping and getting the desired results while sharpening your own skills.

Best use: Request help with a scrape, or find a similar scrape to adapt for your purposes.

FiveFilters.org [Commercial]   

Is an online web scraper available for commercial use. Provides easy content extraction using Full-Text RSS tool which can identify and extract web content (news articles, blog posts, Wikipedia entries, and more) and return it in an easy to parse format. Advantages; speedy article extraction, Multi-page support, has a Autodetection and  you can deploy  on the cloud server without database required.

Kimono

Produced by Kimono labs this tool lets you convert data to into apis for automated export.   Benjamin Spiegel did a great Youmoz post on how to build a custom ranking tool with Kimono, well worth checking out!

Mozenda [Commercial]

This is a unique tool for web data extraction or web scarping.Designed for easiest and fastest way of getting data from the web for everyone. It has a point & click interface and with the power of the cloud you can scrape, store, and manage your data all with Mozenda’s incredible back-end hardware. More advance, you can automate your data extraction leaving without a trace using Mozenda’s  anonymous proxy feature that could rotate tons of IP’s .

Need that data on a schedule? Every day? Each hour? Mozenda takes the hassle out of automating and publishing extracted data. Tell Mozenda what data you want once, and then get it however frequently you need it. Plus it allows advanced programming using REST API the user can connect directly Mozenda account.

Mozenda’s Data Mining Software is packed full of useful applications especially for sales people. You can do things such as “lead generation, forecasting, acquiring information for establishing budgets, competitor pricing analysis. This software is a great companion for marketing plan & sales plan creating.

Using Refine Capture tetx tool, Mozenda is smart enough to filter the text you want stays clean or get  the specific text or split them into pieces.

80Legs [Commercial]

The first time I heard about 80Legs my mind really got confused of what really this software does. 80Legs like Mozenda is a web-based data extraction  tool with customizable features:

•    Select which websites to crawl by entering URLs or uploading a seed list
•    Specify what data to extract by using a pre-built extractor or creating your own
•    Run a directed or general web crawler
•    Select how many web pages you want to crawl
•    Choose specific file types to analyze

80 legs offers customized web crawling that lets you get very specific about your crawling parameters, which tell 80legs what web pages you want to crawl and what data to collect from those web pages and also the general web crawling which can collect data like web page content, outgoing links and other data. Large web crawls take advantage of 80legs’ ability to run massively parallel crawls.

Also crawls data feeds and offers web extraction design services. (No installation needed)

ScrapeBox [Commercial]

ScrapeBox are most popular web scraping tools to SEO experts, online marketers and even spammers with its very user-friendly interface you can easily harvest data from a website;

•    Grab Emails
•    Check page rank
•    Checked high value backlinks
•    Export URLS
•    Checked Index
•    Verify working proxies
•    Powerful RSS Submission

Using thousands of rotating proxies you will be able to sneak on the competitor’s site keywords, do research on .gov sites, harvesting data, and commenting without getting blocked.

The latest updates allow the users to spin comments and anchor text to avoid getting detected by search engines.

You can also check out my guide to using Scrapebox for finding guest posting opportunities:

Scrape.it [Commercial]

Using a simple point & click Chrome Extension tool, you can extract data from websites that render in javascript. You can automate filling out forms, extract data from popups, navigate and crawl links across multiple pages, extract images from even the most complex websites with very little learning curve. Schedule jobs to run at regular intervals.

When a website changes layout or your web scraper stops working, scrape.it  will fix it automatically so that you can continue to receive data uninterrupted and without the need for you to recreate or edit it yourself.

They work with enterprises using our own tool that we built to deliver fully managed solutions for competitive pricing analysis, business intelligence, market research, lead generation, process automation and compliance & risk management requirements.

Features:

    Very easy web date extraction with Windows like Explorer interface

    Allowing you to extract text, images and files from modern Web 2.0 and HTML5 websites which uses Javascript & AJAX.

    The user could select what features they’re going to pay with

    lifetime upgrade and support at no extra charge on premium license

Scrapy [Free Open Source]

Off course the list would not be cool without Scrapy, it is a fast high-level screen scraping and web crawling framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.

Features:

•         Design with simplicity- Just writes the rules to extract the data from web pages and let Scrapy crawl the entire web site. It can crawl 500 retailers’ sites daily.

•         Ability to attach new code for extensibility without having to touch the framework core

•         Portable, open-source, 100% Python- Scrapy is completely written in Python and runs on Linux, Windows, Mac and BSD

•         Scrapy comes with lots of functionality built in.

•         Scrapy is extensively documented and has an comprehensive test suite with very good code coverage

•         Good community and commercial support

 Cons: The installation process is hard to perfect especially for beginners

Needlebase [Commercial]

Many organizations, from private companies to government agencies, store their info in a searchable database that requires you navigate a list page listing results, and a detail page with more information about each result.  Grabbing all this information could result in thousands of clicks, but as long as it fits the same formula, Needlebase can do it for you.  Point and click on example data from one page once to show Needlebase how your site is structured, and it will use that pattern to extract the information you’re looking for into a dataset.  You can query the data through Needle’s site, or you can output it as a CSV or other file format of your choice.  Needlebase can also rerun your scraper every day to continuously update your dataset.

OutwitHub [Free]

This Firefox extension is one of the more robust free products that exists Write your own formula to help it find information you’re looking for, or just tell it to download all the PDFs listed on a given page.  It will suggest certain pieces of information it can extract easily, but it’s flexible enough for you to be very specific in directing it.  The documentation for Outwit is especially well written, they even have a number of tutorials for what you might be looking to do.  So if you can’t easily figure out how to accomplish what you want, investing a little time to push it further can go a long way.

Best use: more text

irobotsoft [Free}

This is a free program that is essentially a GUI for web scraping. There’s a pretty steep learning curve to figure out how to work it, and the documentation appears to reference an old version of the software. It’s the latest in a long tradition of tools that lets a user click through the logic of web scraping. Generally, these are a good way to wrap your head around the moving parts of a scrape, but the products have drawbacks of their own that makes them little easier than doing the same thing with scripts.

Cons: The documentation seems outdated

Best use: Slightly complex scrapes involving multiple layers.

iMacros [Free]

The  same ethos on how microsoft macros works, iMacros automates repetitive task.Whether you choose the website, Firefox extension, or Internet Explorer add-on flavor of this tool, it can automate navigating through the structure of a website to get to the piece of info you care about. Record your actions once, navigating to a specific page, and entering a search term or username where appropriate.  Especially useful for navigating to a specific stock you care about, or campaign contribution data that’s mired deep in an agency website and lacks a unique Web address.  Extract that key piece (pieces) of info into a usable form.  Can also help convert Web tables into usable data, but OutwitHub is really more suited to that purpose.  Helpful video and text tutorials enable you to get up to speed quickly.

Best use: Eliminate repetition in navigating to a particular datapoint in a website that you’re checking up on often by recording a repeatable action that pulls the datapoint out of the clutter it’s naturally surrounded by.

InfoExtractor [Commercial]

This is a neat little web service that generates all sorts of information given a list of urls. Currently, it only works for YouTube video pages, YouTube user profile pages, Wikipedia entries, Huffingtonpost posts, Blogcatalog blog posts and The Heritage Foundation blog (The Foundry). Given a url, the tool will return structured information including title, tags, view count, comments and so on.

Google Web Scraper [Free]

A browser-based web scraper works like Firefox’s Outwit Hub, it’s designed for plain text extraction from any online pages and export to spreadsheets via Google docs. Google Web Scraper can be downloaded as an extension and you can install it in your Chrome browser without seconds. To use it: highlight a part of the webpage you’d like to scrape, right-click and choose “Scrape similar…”. Anything that’s similar to what you highlighted will be rendered in a table ready for export, compatible with Google Docs™. The latest version still had some bugs on spreadsheets.

Cons: It doesn’t work for images and sometimes it can’t perform well on huge volume of text but it’s easy and fast to use.


Tutorials:

Scraping Website Images Manually using Google Inspect Elements

The main purpose of Google Inspect Elements is for debugging like the Firefox Firebug however, if you’re flexible you can use this tool also for harvesting images in a website. Your main goal is to get the specific images like web backgrounds, buttons, banners, header images and product images which is very useful for web designers.

Now, this is a very easy task. First, you will definitely need to download and install the Google Chrome browser in your computer. After the installation do the following:

1. Open the desired webpage in Google Chrome

2. Highlight any part of the website and right click > choose Google Inspect Elements

3. In the Google Inspect Elements, go to Resources tab

4. Under Resources tab, expand all folders. You will eventually see script folders and IMAGES folders

5. In the Images folders, just use arrow keys to find the images you need to have (see the screenshot above)

6. Next, right click the images and choose Open the Image in New Tab

7. Finally, right click the image > choose Save Image As… . (save to your local folder)

You’re done!

How to Extract Links from a Web Page with OutWit Hub

In this tutorial we are going to learn how to extract links from a webpage with OutWit Hub.

Sometimes it can be useful to extract all links from a given web page. OutWit Hub is the easiest way to achieve this goal.

1. Launch OutWit Hub

If you haven’t installed OutWit Hub yet, please refer to the Getting Started with OutWit Hub tutorial.

Begin by launching OutWit Hub from Firefox. Open Firefox then click on the OutWit Button in the toolbar.

If the icon is not visible go to the menu bar and select Tools -> OutWit -> OutWit Hub

OutWit Hub will open displaying the Web page currently loaded on Firefox.


2. Go to the Desired Web Page

In the address bar, type the URL of the Website.

Go to the Page view where you can see the Web page as it would appear in a traditional browser.

Now, select “Links” from the view list.

In the “Links” widget, OutWit Hub displays all the links from the current page.

If you want to export results to Excel, just select all links using ctrl/cmd + A, then copy using ctrl/cmd + C and paste it in Excel (ctrl/cmd + V).

Source: http://www.garethjames.net/a-guide-to-web-scrapping-tools/

Social Media Crawling & Scraping services for Brand Monitoring

Crawling social media sites for extracting information is a fairly new concept – mainly due to the fact that most of the social media networking sites have cropped up in the last decade or so. But it’s equally (if not more) important to grab this ever-expanding User-Generated-Content (UGC) as this is the data that companies are interested in the most – such as product/service reviews, feedback, complaints, brand monitoring, brand analysis, competitor analysis, overall sentiment towards the brand, and so on.

Scraping social networking sites such as Twitter, Linkedin, Google Plus, Instagram etc. is not an easy task for in-house data acquisition departments of most companies as these sites have complex structures and also restrict the amount and frequency of the data that they let out to crawlers. This kind of a task is best left to an expert, such as PromptCloud’s Social Media Data Acquisition Service – which can take care of your end-to-end requirements and provide you with the desired data in a minimal turnaround time. Most of the popular social networking sites such as Twitter and Facebook let crawlers extract data only through their own API (Application Programming Interface), so as to control the amount of information about their users and their activities.

PromptCloud respects all these restrictions with respect to access to content and frequency of hitting their servers to make sure that user information is not compromised and their experience with the site is unhindered.

Social Media Scraping Experts

At PromptCloud, we have developed an expertise in crawling and scraping social media data in real-time. Such data can be from diverse sources such as – Twitter, Linkedin groups, blogs, news, reviews etc. Popular usage of this data is in brand monitoring, trend watching, sentiment/competitor analysis & customer service, among others.

Our low-latency component can extract data on the basis of specific keywords, categories, geographies, or a combination of these. We can also take care of complexities such as multiple languages as well as tweets and profiles of specific users (based on keywords or geographies). Sample XML data can be accessed through this link – demo.promptcloud.com.

Structured data is delivered via a single REST-based API and every time new content is published, the feed gets updated automatically. We also provide data in any other preferred formats (XML, CSV, XLS etc.).

If you have a social media data acquisition problem that you want to get solved, please do get in touch with us.

Source: https://www.promptcloud.com/social-media-networking-sites-crawling-service/

SEO No No! Scraping & Splogging – Content Theft!

Until recently, you could as well as might possibly not have acknowledged how you can perform the earlier mentioned. Even so, the following element could be the really cool element.

Several. Get back to ScrapeBox Add-Ons and also down load your ScrapeBox Blog Analyzer add-on. Open it upwards, and transfer the actual .txt record you merely rescued. Struck start.

ScrapeBox goes through almost every back link you merely scraped and look these phones determine if these are your site that will ScrapeBox presently facilitates placing comments in. If it is, that turns environmentally friendly. If it isn’t, that turns reddish. Soon after it really is concluded, it is possible to “clean” the list insurance agencies the idea remove unsupported websites.

Just what you’re destined to be left with is ALL of the sites the competitor has back-links via, and most importantly, they all are capable of being mentioned in employing ScrapeBox!!

Help save that will “clean” listing with a report, import it this list involving websites you wish to touch upon, and then keep to the exact same steps you’d probably typically follow for you to touch upon websites. Inside of Ten mins you’ll have got all the comps website backlinks (which may be blocked by Public relations if you’d just like) along with you’ll be able to reply to every one of them inside a 20 min (because the list most likely won’t end up being Large).

Desire to force this specific even more?? Obviously you are doing, you’re in BHW

Each step is the same as over with the exception of one tiny issue as well as the addition of an extra step.

Instead of just employing a single foot print inside your first bounty (both from SB’s regular gui after which also the back link checker add-on) you’re likely to be using a A lot of open all of them. Here is what you do to consider this particular to a whole new amount.

Initial, you’re going to pick each of the URLs via AOL, Aol, Ask & Search engines using this footprint:

site:domainyourcompetingwith.org

That will go back ALL the at present found web pages in the area. Remove copy Web addresses along with save that will with a .TXT report.

Now, you’re planning to create the subsequent right in front of each of these URLs:

hyperlink:

Right now follow all of the steps while outlined above. Exactly what this may is actually obtain each of the backlinks to every single site of the rivals web site.

Because Google Back link Checker is simply capable of getting the first 1k Web addresses through Aol (while that’s all Google allows you to view) you could have missed out on a decent amount associated with website inbound links if they had been at night 1st 1k final results. Consequently performing the aforementioned further methods ensures that every brand new web site in the website anyone pay attention to backlinks implies a fresh and other pair of a listing of back-links that is possibly 1k back links long.

Now you understand how to locate, filtration along with take your competitors back links, stop looking at and also move and take action!

Source: https://freescrapeboxlist19.wordpress.com/

What are the ethics of web scraping?

Someone recently asked: "Is web scraping an ethical concept?" I believe that web scraping is absolutely an ethical concept. Web scraping (or screen scraping) is a mechanism to have a computer read a website. There is absolutely no technical difference between an automated computer viewing a website and a human-driven computer viewing a website. Furthermore, if done correctly, scraping can provide many benefits to all involved.

There are a bunch of great uses for web scraping. First, services like Instapaper, which allow saving content for reading on the go, use screen scraping to save a copy of the website to your phone. Second, services like Mint.com, an app which tells you where and how you are spending your money, uses screen scraping to access your bank's website (all with your permission). This is useful because banks do not provide many ways for programmers to access your financial data, even if you want them to. By getting access to your data, programmers can provide really interesting visualizations and insight into your spending habits, which can help you save money.

That said, web scraping can veer into unethical territory. This can take the form of reading websites much quicker than a human could, which can cause difficulty for the servers to handle it. This can cause degraded performance in the website. Malicious hackers use this tactic in what’s known as a "Denial of Service" attack.

Another aspect of unethical web scraping comes in what you do with that data. Some people will scrape the contents of a website and post it as their own, in effect stealing this content. This is a big no-no for the same reasons that taking someone else's book and putting your name on it is a bad idea. Intellectual property, copyright and trademark laws still apply on the internet and your legal recourse is much the same. People engaging in web scraping should make every effort to comply with the stated terms of service for a website. Even when in compliance with those terms, you should take special care in ensuring your activity doesn't affect other users of a website.

One of the downsides to screen scraping is it can be a brittle process. Minor changes to the backing website can often leave a scraper completely broken. Herein lies the mechanism for prevention: making changes to the structure of the code of your website can wreak havoc on a screen scraper's ability to extract information. Periodically making changes that are invisible to the user but affect the content of the code being returned is the most effective mechanism to thwart screen scrapers. That said, this is only a set-back. Authors of screen scrapers can always update them and, as there is no technical difference between a computer-backed browser and a human-backed browser, there's no way to 100% prevent access.

Going forward, I expect screen scraping to increase. One of the main reasons for screen scraping is that the underlying website doesn't have a way for programmers to get access to the data they want. As the number of programmers (and the need for programmers) increases over time, so too will the need for data sources. It is unreasonable to expect every company to dedicate the resources to build a programmer-friendly access point. Screen scraping puts the onus of data extraction on the programmer, not the company with the data, which can work out well for all involved.

Source: https://quickleft.com/blog/is-web-scraping-ethical/

How to Build Data Warehouses using Web Scraping

Businesses all over the world are facing an avalanche of information which needs to be collated, organized, analyzed and utilized in an appropriate fashion. Moreover, with each increasing year there is a perceived shortening of the turnaround time for businesses to take decisions based on information they have assimilated. Data Extractors, therefore, have evolved with a more significant role in modern day businesses than just mere collectors or scrapers of unstructured data. They cleanse structure and store contextual data in veritable warehouses, so as to make it available for transformation into useable information as and when the business requires. Data warehouses, therefore, are the curators of information which businesses seek to treasure and to use.

Understanding Data Warehouses

 Traditionally, Data Warehouses have been premised on the concept of getting easy access to readily available data. Modern day usage has helped it to evolve as a rich repository to store current and historical data that can be used to conduct data analysis and generate reports. As it also stores historical data, Data Warehouses are used to generate trending reports to help businesses foresee their prospects. In other words, data warehouses are the modern day crystal balls which businesses zealously pore over to foretell their future in the Industry.

Scraping Web Data for Creating Warehouses

The Web, as we know it, is a rich repository of a whole host of information. However, it is not always easy to access this information for the benefit of our businesses through manual processes. The data extractor tools, therefore, have been built to quickly and easily, scrape, cleanse and structure and store it in Data Warehouses so as to be readily available in a useable format.

Web Scraping tools are variously designed to help both programmers as well as non-programmers to retain their comfort zone while collecting data to create the data warehouses. There are several tools with point and click interfaces that ease out the process considerably. You can simply define the type of data you want and the tool will take care of the rest. Also, most tools such as these are able to store the data in the cloud and therefore do not need to maintain costly hardware or whole teams of developers to manage the repository.

Moreover, as most tools use a browser rendering technology, it helps to simulate the web viewing experience of humans thereby easing the usability aspect among business users facilitating the data extraction and storage process further.

Conclusion

The internet as we know it is stocked with valuable data most of which are not always easy to access. Web Data extraction tools have therefore gained popularity among businesses as they browse, search, navigate simulating your experience of web browsing and finally extract data fields specific to your industry and appropriate to your needs. These are stored in repositories for analysis and generation of reports. Thus evolves the need and utility of Data warehouses. As the process of data collection and organization from unstructured to structured form is automated, there is an assurance of accuracy built into the process which enhances the value and credibility of data warehouses. Web Data scraping is no doubt the value enhancers for Data warehouses in the current scenario.

Source: http://scraping-solutions.blogspot.in/2014/09/how-to-build-data-warehouses-using-web.html

Custom Web Data Scraping Service

Data scraping services help companies and individuals alike to have access to on-demand data that is scraped from the web on the basis of individual requirements. Unlike the SaaS counterparts, a data scraping service such as PromptCloud can enable you to fetch scraped data in a clean, structured data format such that you don’t need to be involved at any stage in the process except for while giving your data scraping service request and taking delivery of the final scraped data.

A data scraping service fills a very important gap in the big data context as most of the options available are either DIY (open source data scraping) or SaaS based data scraping companies which, in most cases, fail to address the majority of the needs. For instance, someone who’s looking for a service for data scraping for custom requirements, and doesn’t want anything less than 99.9% accuracy in the final scraped data, both the above options (open source or SaaS) are unfavourable. This is so because unless someone is dedicatedly looking for structure changes in the list of source pages (which happen with 40% of all websites each month), it gets almost impossible to get accuracy levels of anywhere more than 90%.

A custom service for scraping web data such as PromptCloud can be your data partner if you know what you need to scrape from the web, and can deliver the scraped data automatically to you at a desired scraping frequency (near real-time, daily, weekly, monthly etc.), in the schema containing records of choice and in a format that you desire (we do XML, CSV, XLS, JSON).

All you need to do is participate only while briefing the requirements and then taking delivery of the desired data if you use our data scraping service offering. If you wish to discuss a data scraping use case.

Source: https://www.promptcloud.com/data-scraping-service/