Five Steps to Quality Essay Writing

No two writers think alike. Everyone is unique. For the same reason, everyone has his own manner of using language. But as far as the science of essay writing is concerned, there are some general parameters to be followed. While writing an essay, certain tips will help you to make it an excellent one.

1. A Well Balanced Essay

Ideas should not be written in a Chaotic or disorganized manner. There must be an easy and automatic flow. You are not supposed to stop an essay in the middle of a hot issue. Proceed in such a way that each and every sentence must guide you to the conclusion. The beginning, the middle and the end must be crystal clear to the readers. How you begin, how you proceed and how you end up; all have equal importance in the assessment of an essay.

A well begun stuff pushes the readers to keep on reading it. Though the middle portion of the essay bears the essence of your topic, the conclusion is not of less importance. In short, each and every part of an essay is next to nothing.

2. Too Much is Too Bad

Never go for marathon writing. Essays must not be too long. It kills the grandeur of your work. Write the relevant points using minimum number of words which are apt and attractive. Though there are no strict rules governing the length of the essays, it is always desirable to finish it with 350 words. However you are free to break this unwritten law to a certain extent, considering the seriousness of your subject matter. A topic which requires much statements and explanations can take a little more length. But keep in mind the above said words; Too much is too bad.

3. Be up-to-the-minute

No need to mention the importance of 'knowledge chase' in the process of every type of writings. All findings start when you start finding the apt source. But don't be cheated by resources which are outdated. Be accurate in selecting the right assistance.

You can surpass your fellow students by attempting something new. Go for innovation in whatever field you indulge in. Any creative writing stuff can be made exceptional by clinging on to latest information on air. It shows that you are keeping the right pace with the world around.

4. Style par excellent

Don't use unnatural and unfamiliar words. An inclination to use these types of words seems to be made-up. A highly intricate language with full of unnecessary ornamentation leads the reader to finish reading from the middle. Use natural expressions in a novel way. Don't make sentences too complicated and too polished. Let them be interactive and conversing. Make it a thorough piece of objective one.

5. A flavor of personal touch

Study an issue from a number of possible angles. After finding creative assistance from experienced hands, add your own opinion. Give a personal touch to it. As far as your assignment is concerned, what others said is only secondary. An essay should not be a collection of the opinions of great writers and orators. There should be your stamp in it. Your own feelings and outlooks make the essay solely yours. Never be under the impression that you are second to somebody. Think that you are a person of importance. Crush the psychological barrier to include your individuality in your writings. Keep in mind; you are capable of doing anything great.

Source:http://ezinearticles.com/?Five-Steps-to-Quality-Essay-Writing&id=3127797

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

An Easy Way For Data Extraction

There are so many data scraping tools are available in internet. With these tools you can you download large amount of data without any stress. From the past decade, the internet revolution has made the entire world as an information center. You can obtain any type of information from the internet. However, if you want any particular information on one task, you need search more websites. If you are interested in download all the information from the websites, you need to copy the information and pate in your documents. It seems a little bit hectic work for everyone. With these scraping tools, you can save your time, money and it reduces manual work.

The Web data extraction tool will extract the data from the HTML pages of the different websites and compares the data. Every day, there are so many websites are hosting in internet. It is not possible to see all the websites in a single day. With these data mining tool, you are able to view all the web pages in internet. If you are using a wide range of applications, these scraping tools are very much useful to you.

The data extraction software tool is used to compare the structured data in internet. There are so many search engines in internet will help you to find a website on a particular issue. The data in different sites is appears in different styles. This scraping expert will help you to compare the date in different site and structures the data for records.

And the web crawler software tool is used to index the web pages in the internet; it will move the data from internet to your hard disk. With this work, you can browse the internet much faster when connected. And the important use of this tool is if you are trying to download the data from internet in off peak hours. It will take a lot of time to download. However, with this tool you can download any data from internet at fast rate.There is another tool for business person is called email extractor. With this toll, you can easily target the customers email addresses. You can send advertisement for your product to the targeted customers at any time. This the best tool to find the database of the customers.

However, there are some more scraping tolls are available in internet. And also some of esteemed websites are providing the information about these tools. You download these tools by paying a nominal amount.

Source:http://ezinearticles.com/?An-Easy-Way-For-Data-Extraction&id=3517104

6 Beginner Tips For Creating A Solid Social Media Marketing Strategy

Most small business owners already know that if you’re not using social media marketing, you’re simply missing the boat. Facebook, Twitter, LinkedIn, YouTube, and many newcomers to the scene represent excellent ways to get the word out about your biz. With all of these sites at your disposal and all the methods with which to use them, however, the process can seem overwhelming. Instead of getting frustrated and giving up, know that there is a method to the madness – and embrace it. Read on to learn how to develop and maintain an effective social media strategy for your small business.

1. Start With the Most Successful Sites

Facebook and Twitter continue to maintain a foothold as two of the most popular social media websites, althoughPinterest and Google Plus are moving up rapidly. Unless there’s a major shift in the social media landscape, though, those first two websites are your best bets for launching a campaign. If you’re already there, work on maximizing your effectiveness on them. If you’re not, get started now.

2. Investigate Lesser Known Sites

Have you ever heard of StumbleUpon? New social media websites are popping up all the time and you never know where your next set of customers is going to come from. The more saturated the top platforms become, the harder it’s going to be to reach an audience on them – your voice may be more easily heard in smaller rooms. Of course, don’t take that as license to veer away from Facebook and Twitter, but do investigate these other sites as time permits.

3. Place a Key Focus on Content Quality

No matter where you go with your social strategy, content is always going to be key. Never post anything less than your best work. If it’s an image on Pinterest, make sure it’s high quality. If it’s a tweet, make those few words impactful. Just because you’re limited to 140 characters doesn’t mean quality should suffer. For all posts, draw upon your industry experience to provide lesser known details and advice, and once you come up with a posting schedule, scale it back if your quality begins to suffer. This point can’t be emphasized enough.

4. Track Your Efforts

It’s essential that you monitor the progress of your social media campaign so you know what’s working and what isn’t. Even though Facebook and Twitter do work for most small businesses, they might not work for you. UseHootSuite or Google Analytics to effectively keep up on the success of each of your individual campaigns.

5. Effectively Adjust Your Strategy

Next, act on your results. If Twitter isn’t giving you the boost you expected, you might not want to abandon it, just tweak your strategy or devote less of a focus to it. If a newer player like Tumblr or Reddit doesn’t show any signs of life, eliminate it and move on to something else like Instagram or Digg. Gathering data on your social strategy is important, but that does nothing if you don’t put it to good use.

6. Always Respond to Comments

No matter where you market via social media, you can’t realize any individual site’s full potential without responding to each and every comment. Even if it’s simply acknowledging and thanking a reader for taking the time, this can have a positive effect. If you encounter negative comments, jump on them immediately. These are only blemishes on your reputation if you do nothing about them. First off, be governed by the notion that the customer is always right.

There’s nothing more off-putting than a Twitter fight between a business owner and a patron, even if you’re sure you’re correct. Take this opportunity to acknowledge any grievances and make up for them ten-fold, publicly. Turn that complaint into an asset and you’ve not only won over that customer, but all the others who read the exchange.

Final Thoughts

No social media strategy can succeed without an appropriate amount of time devoted to it. If your budget doesn’t have room to hire a social media manager, you’re going to have to wear that hat yourself. In order to free up the needed time, organize your day, schedule your most difficult projects for when you’re at your best (either morning or evening) and eliminate any unnecessary interruptions.

Stop spending time on useless phone calls from telemarketers and other folks who don’t serve your business needs. Get yourself physically fit so you perform at a high level each and every day, and take your breaks. Don’t feel bad about going out for a walk or for lunch each day, because your business is going to benefit from your refreshed mind. Social media marketing is important, but only if you’re fully up to the task.

How did you go about creating your social strategy? Leave your tips in the comments below!

Source: http://www.business2community.com/social-media/6-beginner-tips-creating-solid-social-media-marketing-strategy-0783129#!wIkYk

The importance of high quality product data

If advertisers want to be truly multichannel then they need to have access to, and control of, their product data.

By extracting product data from several different sources, you can fulfil any required channel marketing

application.

Data can be extracted directly from your ecommerce site, existing data feeds or an API, and can then be distributed
into hundreds of different online channels, increasing the visibility of your products in front of online consumers.

However, it all hinges on having high-quality product data that is comprehensive, accurate and consumable.

Helen Southgate, UK Managing Director, Affilinet:

Overlooking quality - especially when it comes to data – is a big mistake…The correct data will drive customer
acquisition, incremental sales and contribute towards increasing the lifetime value of a customer. The wrong data
can impact brand reputation, growth and see an advertiser lose customers to a more savvy competitor.

Product data must be comprehensive

John Ashton, Digital Marketing Executive, Notonthehighstreet.com:

Having a product data feed that we can trust - that’s the most important thing. It means that we can be confident
that whoever accesses our products is getting the best, and most relevant, information...

Comprehensive data contains all the relevant product attributes for each unique, marketable product in your
inventory.

Product attributes include: price, description, images etc. and the most effective way of extracting rich product data
from ecommerce websites is through screen scraping as this tends to be the most reliable source of merchant
product information.

If attributes are missing from a product data feed consumers will not be able to see all product information. If this
happens, the advertiser loses credibility with the consumer as well as value, because their products cannot be
integrated into any channels that require those missing attributes.

To create a comprehensive product data set, product information should be gathered from more than one source,
e.g. data feeds, APIs and directly from an ecommerce website.

This ensures that no product attributes are missing and gives advertisers access to all the product data required for
their channel marketing initiatives.

Product data must be consumable

Chris Sheen, Head of Marketing, SaleCycle:

Product data is integral to SaleCycle's business, and the solutions we provide our clients. There are so many
different data sets which can influence your marketing decisions, from simple personal details (e.g. gender)
through to advanced demographic and modelling information (e.g. Mosaic) - but it's product level data which gives
us the clearest indication of a customer's buying intentions - both now and in the future.

Your product data must be in a usable format for marketing applications, taking attribute mapping and
transformation into consideration where necessary, so that partners, e.g. digital agencies and price comparison
sites, can use your product data.

It must also be in good order so that you can take advantage of new channel opportunities as and when they arise.

Consumable data is easily accessible and contains all necessary product attributes; this makes channel integration
simple and cost-effective, giving you more control over where your data is distributed. Data can be integrated as a
data feed or as part of an API that can be interpreted by those looking to use your data.

It can also be provided on an FTP site for download.

Optimising your data

Having comprehensive, accurate and consumable data enables the correct mapping and categorisation of your
product data. This makes it easier to integrate the data into online channels even if their menu categories are not
the same as those on your website.

Having product data which is clearly structured, labelled and formatted makes this process easier and it’s also
possible to combine or split product data to create new attributes, e.g. by taking attributes such as ‘colour’ or
‘gender’ from the product title.

This can also work the other way by combining individual attributes to optimise product titles or descriptions in
paid search automation.

High-quality data

Richard McKnight, Digital Marketing Manager, Chain Reaction Cycles:

With the online space becoming more competitive each year, one of the key areas that can position your business
ahead of competitors is quality data, not only to ensure your advertising channels are working correctly but also to
ensure relevant and efficient traffic is being driven from each.

Ultimately, you should always aim to have the highest quality data possible so that you can drive incremental sales
revenue through different channels and provide a consistent consumer experience of your products across
channels.

Having control of your product data enables you to create engaging content across various online channels
including: display advertising, marketplaces (Amazon, eBay etc.), Google Shopping, Price Comparison Search (CSEs),
behavioural retargeting and social media (Facebook).

If data is comprehensive, accurate and consumable it gives you the ability to be creative and flexible with your
data, displaying your products across multiple channels to reach the greatest number of consumers and give them
an optimised buying experience on your site.

Source: http://econsultancy.com/blog/64322-the-importance-of-high-quality-product-data

Data scraping tool for non-coding journalists launches

A tool which helps non-coding journalists scrape data from websites has launched in public beta today.

Import.io lets you extract data from any website into a spreadsheet simply by mousing over a few rows of information.

Until now import.io, which we reported on back in April, has been available in private developer preview and has been Windows only. It is now also available for Mac and is open to all.

Although import.io plans to charge for some services at a later date, there will always be a free option.

The London-based start-up is trying to solve the problem of the fact that there is "lots of data on the web, but it's difficult to get at", Andrew Fogg, founder of import.io, said in a webinar last week.

Those with the know-how can write a scraper or use an API to get at data, Fogg said. "But imagine if you could turn any website into a spreadsheet or API."

Uses for journalists

Journalists can find stories in data. For example, if I wanted to do a story on the type of journalism jobs being advertised and the salaries offered, I could research this by looking at various websites which advertise journalism jobs.

If I were to gather the data from four different jobs boards and enter the information manually into a spreadsheet it would take would take hours if not days; if I were to write a screen scraper for each of the sites it would require knowledge and would probably take a couple of hours. Using import.io I can create a single dataset from multiple sources in a few minutes.

I can then search and sort the dataset and find out different facts, such as how many unpaid internships are advertised, or how many editors are currently being sought.

How it works

When you download the import.io application you see a web browser. This browser allows you to enter a URL for any site you want to scrape data from.

To take the example of the jobs board, this is structured data, with the job role, description and salaries displayed.

The first step is to set up 'connectors' and to do this you need to teach the system where the data is on the page. This is done by hitting a 'record' button on the right of the browser window and mousing over a few examples, in this case advertised jobs. You then click 'train rows'.

It takes between two and five examples to teach import.io where all of the rows are, Fogg explained in the webinar.

The next step is to declare the type of data and add column names. For example there may be columns for 'job title', 'job description' and 'salary'. Data is then extracted into the table below the browser window.

Data from different websites can then be "mixed" into a single searchable database.

In the example used in the webinar, Fogg demonstrated how import.io could take data relating to rucksacks for sale on a shopping website. The tool can learn the "extraction pattern", Fogg explained, and apply that to to another product. So rather than mousing over the different rows of sleeping bags advertised, for example, import.io was automatically able to detect where the price and product details were on the page as it had learnt the structure from how the rucksacks were organised. The really smart bit is that the data from all products can then be automatically scraped and pulled into the spreadsheet. You can then search 'shoes' and find the data has already been pulled into your database.

When a site changes its code a screen scraper would become ineffective. Import.io has a "resilience to change", Fogg said. It runs tests twice a day and users get notified of any changes and can retrain a connector.

It is worth noting that a site that has been scraped will be able to detect that import.io has extracted the data as it will appear in the source site's web logs.

Case studies

A few organisations have already used import.io for data extraction. Fogg outlined three.

    British Red Cross

The British Red Cross wanted to create an iPhone app with data from the NHS Choices website. The NHS wanted the charity to use the data but the health site does not have an API.

By using import.io, data was scraped from the NHS site. The app is now in the iTunes store and users can use it to enter a postcode to find hospital information based on the data from the NHS site.

"It allowed them to build an API for a website where there wasn't one," Fogg said.

    Hewlett Packard

Fogg explained that Hewlett Packard wanted to monitor the prices of its laptops on retailers' websites.

They used import.io to scrape the data from the various sites and were able monitor the prices at which the laptops were being sold in real-time.

    Recruitment site

A US recruitment firm wanted to set up a system so that when any job vacancy appeared on a competitor's website, they could extract the details and push that into their Salesforce software. The initial solution was to write scrapers, Fogg said, but this was costly and in the end they gave up. Instead they used import.io to scrape the sites and collate the data.


Source: http://www.journalism.co.uk/news/data-scraping-tool-for-non-coding-journalists-launches/s2/a554002/

ScraperWiki lets anyone scrape Twitter data without coding

The Obama administration’s open data mandate announced on Thursday was made all the better by the unveiling of the new ScraperWiki service on Friday. If you’re not familiar with ScraperWiki, it’s a web-scraping service that has been around for a while but has primarily focused on users with some coding chops or data journalists willing to pay to have someone scrape data sets for them. Its new service, though, currently in beta, also makes it possible for anyone to scrape Twitter to create a custom data set without having to write a single line of code.

Taken alone, ScraperWiki isn’t that big of a deal, but it’s part of a huge revolution that has been called the democratization of data. More data is becoming available all the time — whether from the government, corportations or even our own lives — only it’s not of much use unless you’re able to do something with it. ScraperWiki is now one of a growing list of tools dedicated to helping everyone, not just expert data analysts or coders, analyze — and, in its case, generate — the data that matters to them.

After noticing a particularly large numbers of tweets in my stream about flight delays yesterday, I thought I’d test out ScraperWiki’s new Twitter search function by gathering a bunch of tweets directed to @United. The results — from 1,697 tweets dating back to May 3 — are pretty fun to play with, if not that surprising. (Also, I have no idea how far back the tweet search will go or how long it will take using the free account, which is limited to 30 minutes of compute time a day. I just stopped at some point so I could start digging in.)

First things first, I ran my query. Here’s what the data looks like viewed in a table in the ScraperWiki app.

Next, it’s a matter of analyzing it. ScraperWiki lets you view it in a table (like above), export it to Excel or query it using SQL, and will also summarize it for you. This being Twitter data, the natural thing to do seemed to be analyzing it for sentiment. One simple way to do this right inside the ScraperWiki table is to search for a particular term that might suggest joy or anger. I chose a certain four-letter word that begins with f.

Surprisingly, I only found eight instances. Here’s my favorite: “Your Customer Service is better than a hooker. I paid a bunch of money and you’re still…” (You probably get the idea.)

But if you read my “data for dummies” post from January, you know that we mere mortals have tools at our disposal for dealing with text data in a more refined way. IBM’s Many Eyes service won’t let me score tweets for sentiment, but I can get a pretty good idea overall by looking at how words are used. For this job, though, a simple word cloud won’t work, even after filtering out common words, @united and other obvious terms. Think of how “thanks” can be used sarcastically and you can see why.

Using the customized word tree, you can see that “thanks” sometimes means “thanks.” Other times, not so much. I know it’s easy to dwell on the negative, but consider this: “worst” had 28 hits while “best” had 15. One of those was referring to Tito’s vodka and at least three were referring to skyline views. (Click here to access it and search by whatever word you want.)

Here’s a phrase net filtering the results by phrases where the word “for” connects two words.

Anyhow, this was just a fast, simple and fairly crude example of what ScraperWiki now allows users to do, and how that resulting data can be combined with other tools to analyze and visualize it. Obviously, it’s more powerful if you can code, but new tools are supposedly on the way (remember, this is just a beta version) that should make it easier to scrape data from even more sources.

In the long term, though, services like ScraperWiki should become a lot more valuable as tools for helping us generate and analyze data rather than just believe what we’re told. Want to improve your small business, put your life in context or perhaps just write the best book report your teacher has ever seen? It’s getting easier every day.


Source: http://gigaom.com/2013/05/10/scraperwiki-lets-anyone-scrape-twitter-data-without-coding/