Data Mining Services

You will get all solutions regarding data mining from many companies in India. You can consult a variety of companies for data mining services and considering the variety is beneficial to customers. These companies also offer web research services which will help companies to perform critical business activities.

Very competitive prices for commodities will be the results where there is competition among qualified players in the data mining, data collection services and other computer-based services. Every company willing to cut down their costs regarding outsourcing data mining services and BPO data mining services will benefit from the companies offering data mining services in India. In addition, web research services are being sourced from the companies.

Outsourcing is a great way to reduce costs regarding labor, and companies in India will benefit from companies in India as well as from outside the country. The most famous aspect of outsourcing is data entry. Preference of outsourcing services from offshore countries has been a practice by companies to reduce costs, and therefore, it is not a wonder getting outsource data mining to India.

For companies which are seeking for outsourcing services such as outsource web data extraction, it is good to consider a variety of companies. The comparison will help them get best quality of service and businesses will grow rapidly in regard to the opportunities provided by the outsourcing companies. Outsourcing does not only provide opportunities for companies to reduce costs but to get labor where countries are experiencing shortage.

Outsourcing presents good and fast communication opportunity to companies. People will be communicating at the most convenient time they have to get the job done. The company is able to gather dedicated resources and team to accomplish their purpose. Outsourcing is a good way of getting a good job because the company will look for the best workforce. In addition, the competition for the outsourcing provides a rich ground to get the best providers.

In order to retain the job, providers will need to perform very well. The company will be getting high quality services even in regard to the price they are offering. In fact, it is possible to get people to work on your projects. Companies are able to get work done with the shortest time possible. For instance, where there is a lot of work to be done, companies may post the projects onto the websites and the projects will get people to work on them. The time factor comes in where the company will not have to wait if it wants the projects completed immediately.

Outsourcing has been effective in cutting labor costs because companies will not have to pay the extra amount required to retain employees such as the allowances relating to travels, as well as housing and health. These responsibilities are met by the companies that employ people on a permanent basis. The opportunity presented by the outsourcing of data and services is comfort among many other things because these jobs can be completed at home. This is the reason why the jobs will be preferred more in the future.


Source: http://ezinearticles.com/?Data-Mining-Services&id=4733707

Is Web Scraping Relevant in Today's Business World?

Different techniques and processes have been created and developed over time to collect and analyze data. Web scraping is one of the processes that have hit the business market recently. It is a great process that offers businesses with vast amounts of data from different sources such as websites and databases.

It is good to clear the air and let people know that data scraping is legal process. The main reason is in this case is because the information or data is already available in the internet. It is important to know that it is not a process of stealing information but rather a process of collecting reliable information. Most people have regarded the technique as unsavory behavior. Their main basis of argument is that with time the process will be over flooded and therefore lead to parity in plagiarism.

We can therefore simply define web scraping as a process of collecting data from a wide variety of different websites and databases. The process can be achieved either manually or by the use of software. The rise of data mining companies has led to more use of the web extraction and web crawling process. Other main functions such companies are to process and analyze the data harvested. One of the important aspects about these companies is that they employ experts. The experts are aware of the viable keywords and also the kind of information which can create usable statistic and also the pages that are worth the effort. Therefore the role of data mining companies is not limited to mining of data but also help their clients be able to identify the various relationships and also build the models.

Some of the common methods of web scraping used include web crawling, text gripping, DOM parsing, and expression matching. The latter process can only be achieved through parsers, HTML pages or even semantic annotation. Therefore there are many different ways of scraping the data but most importantly they work towards the same goal. The main objective of using web scraping service is to retrieve and also compile data contained in databases and websites. This is a must process for a business to remain relevant in the business world.

The main questions asked about web scraping touch on relevance. Is the process relevant in the business world? The answer to this question is yes. The fact that it is employed by large companies in the world and has derived many rewards says it all. It is important to note that many people regarded this technology as a plagiarism tool and others consider it as a useful tool that harvests the data required for the business success.

Using of web scraping process to extract data from the internet for competition analysis is highly recommended. If this is the case, then you must be sure to spot any pattern or trend that can work in a given market.


Source: http://ezinearticles.com/?Is-Web-Scraping-Relevant-in-Todays-Business-World?&id=7091414

Data Mining's Importance in Today's Corporate Industry

A large amount of information is collected normally in business, government departments and research & development organizations. They are typically stored in large information warehouses or bases. For data mining tasks suitable data has to be extracted, linked, cleaned and integrated with external sources. In other words, it is the retrieval of useful information from large masses of information, which is also presented in an analyzed form for specific decision-making.

Data mining is the automated analysis of large information sets to find patterns and trends that might otherwise go undiscovered. It is largely used in several applications such as understanding consumer research marketing, product analysis, demand and supply analysis, telecommunications and so on. Data Mining is based on mathematical algorithm and analytical skills to drive the desired results from the huge database collection.

It can be technically defined as the automated mining of hidden information from large databases for predictive analysis. Web mining requires the use of mathematical algorithms and statistical techniques integrated with software tools.

Data mining includes a number of different technical approaches, such as:

    Clustering
    Data Summarization
    Learning Classification Rules
    Finding Dependency Networks
    Analyzing Changes
    Detecting Anomalies

The software enables users to analyze large databases to provide solutions to business decision problems. Data mining is a technology and not a business solution like statistics. Thus the data mining software provides an idea about the customers that would be intrigued by the new product.

It is available in various forms like text, web, audio & video data mining, pictorial data mining, relational databases, and social networks. Data mining is thus also known as Knowledge Discovery in Databases since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data Mining therefore has arrived on the scene at the very appropriate time, helping these enterprises to achieve a number of complex tasks that would have taken up ages but for the advent of this marvelous new technology.


Source: http://ezinearticles.com/?Data-Minings-Importance-in-Todays-Corporate-Industry&id=2057401

Data Recovery Services - Be Wary of Cheap Prices

Data recovery is a specialized, complicated process. Proper hard drive recovery can require manipulation of data at the sector level, transplantation of internal components and various other procedures. These techniques are very involved and require not only talented, knowledgeable technicians, but also an extensive inventory of disk drives to use for parts when necessary and clean facilities to conduct the work.

Unfortunately these factors mean that, in most cases, recovery services are quite expensive. Technician training, hard drive inventories and special equipment all come with a cost.

If you search for disk recovery services, you will likely find several smaller companies that offer hard disk data recovery for a fraction of the prices usually quoted by larger, more experienced organizations. These companies often operate from small offices or, in some cases, private homes. They do not possess clean room facilities, large disk drive inventories or many other pieces of equipment necessary to successfully complete most hard drive recovery cases.

When you take into account all of the training, parts and facilities necessary, you can see how it is impossible for a company to charge $200 for a hard drive recovery and not operate at a loss.

What these companies usually do is run a software program on the hard disk drive. Sometimes, if there is no physical damage to the disk drive, this program is able to recover some of the data. However, hard disk data recovery is much more than just running software. No software can recover data from a hard drive that has failed heads, damaged platters or electrical damage. In fact, attempting to run a hard drive that is physically damaged can make the problem worse. Trying to use software to recover data from a hard drive with failed read/write heads, for example, can lead to the heads scraping the platters of the drive and leaving the data unrecoverable.

Another way these smaller companies conduct business is by forwarding data recovery cases they cannot recover to larger organizations. Of course, the smaller companies charge their clients a premium for this service. In these cases it would have actually been cheaper to use the larger company in the first place.

You will also likely find that many smaller recovery companies charge evaluation or diagnostic fees upfront. They charge these fees regardless of whether or not any data is recovered. In many cases clients desperate to recover their data pay these fees and get nothing in return but a failed recovery. Smaller data recovery services simply do not have the skills, training, facilities and parts to successfully recover most disk drives. It is more cost efficient for them to make one attempt at running a software program and then call the case unsuccessful.

Sometimes you may get lucky working with a smaller data recovery company, but in most cases you will end up paying for a failed recovery. In the worst case scenario you could end up with a damaged hard drive that is now unrecoverable by any data recovery service.

You will waste time and money working with these services. You could even lose your valuable data for good.

If your data is important enough to consider data recovery, it is important enough to seek a reputable, skilled data recovery company. All major data recovery services offer free evaluations and most do not charge clients for unsuccessful recoveries. Sometimes you only have one shot to recover data on a disk drive before the platters are seriously damaged and the data is lost for good. Taking chances with inexperienced companies is not worth the risk.


Source: http://ezinearticles.com/?Data-Recovery-Services---Be-Wary-of-Cheap-Prices&id=4706055

Data Mining For Professional Service Firms - The Marketing Mother Lode May Already Be in Your Files

No one needs to tell you about the value of information in today's world--particularly the value of information that could help grow your practice. But has it occurred to you that you probably have more information in your head and your existing files that you realize? Tap into this gold mine of data to develop a powerful and effective marketing plan that will pull clients in the door and push your profitability up.

The way to do this is with data mining, which is the process of using your existing client data and demographics to highlight trends, make predictions and plan strategies.

In other words, do what other kinds of businesses have been doing for years: Analyze your clients by industry and size of business, the type and volume of services used, the amount billed, how quickly they pay and how profitable their business is to you. With this information, you'll be able to spot trends and put together a powerful marketing plan.

To data mine effectively, your marketing department needs access to client demographics and financial information. Your accounting department needs to provide numbers on the services billed, discounts given, the amounts actually collected, and receivables aging statistics. You may identify a specific service being utilized to a greater than average degree by a particular industry group, revealing a market segment worth pursuing. Or you may find an industry group that represents a significant portion of your billed revenue, but the business is only marginally profitable because of write-offs and discounts. In this case, you may want to shift your marketing focus.

You should also look at client revenues and profitability by the age of the clients. If your percentage of new clients is high, it could mean you're not retaining a sufficient number of existing clients. If you see too few new clients, you may be in for problems when natural client attrition is not balanced by new client acquisition.

The first step in effective data mining is to get everyone in the firm using the same information system. This allows everyone in the office who needs the names and addresses of the firm's clients and contacts to have access to that data. Require everyone to record notes on conversations and meetings in the system. Of course, the system should also accommodate information that users don't want to share, such as client's private numbers or the user's personal contacts. This way, everyone can utilize the system for everything, which makes them more likely to use it completely.

Your information system can be either contact information or customer relationship management software (a variety of packages are on the market) or you can have a system custom designed. When considering software to facilitate data mining, look at three key factors:

1. Ease of use. If the program isn't easy to use, it won't get used, and will end up being just a waste of time and money.

2. Accessibility. The system must allow for data to be accessible from anywhere, including laptops, hand-held devices, from the internet or cell phones. The data should also be accessible from a variety of applications so it can be used by everyone in the office all the time, regardless of where they are.

3. Sharability. Everyone needs to be able to access the information, but you also need privacy and editing rights so you can assign or restrict what various users can see and input.

Don't overlook the issue of information security. Beyond allowing people the ability to code certain entries as private, keep in mind that anyone with access to the system as the ability to either steal information or sabotage your operation. Talk to your software vendor about various security measures but don't let too much security make the system unusable. Protect yourself contractually with noncompete and nondisclosure agreements and be sure to back up your data regularly.

Finally, expect some staffers to resist when you ask them to change from the system they've been using. You may have to sell them on the benefits outweighing the pain of making a change and learning the new system--which means you need to be totally sold on it yourself. The managing partner, or the leader of the firm, needs to be driving this initiative for it to succeed. When it does succeed, you'll be able to focus your marketing dollars and efforts in the most profitable areas with the least expense, with a tremendous positive impact on the bottom line.


Source: http://ezinearticles.com/?Data-Mining-For-Professional-Service-Firms---The-Marketing-Mother-Lode-May-Already-Be-in-Your-Files&id=4607430

Web Scraping Evolved: APIs for Turning Webpage Content into Valuable Data

While the rates in adoption of semantic standards are increasing, the majority of the web is still home to mostly unstructured data.  Search engines, like Google, remain focused on looking at the HTML markup for clues to create richer results for their users.  The creation of schema.org and similar movements has aided in the progression of the ability draw valuable content from webpages.

But even with semantic standards in place, structured data requires a parser to extract information and convert it into a data-interchange format, namely JSON or XML.  Many libraries exist for this, and in several popular coding languages.  But be warned, most of these parser libraries are far from polished.  Most are community-produced, and thus may not be complete or up to date, as standards are ever changing.  On the flip side, website owners whom don’t fully follow semantic rules, can break the parser.  And of course there are sites which contain no structured data formatting at all.   This inconsistency causes problems for intelligent data harvesting, and can be a roadblock for new business ideas and startups.


Several companies are offering an API service to make sense of this unstructured data, helping to remove these roadblocks.  For example, AlchemyAPI offers a suite of data extraction APIs including a Structured Content Scraping API, which enables structured data to be extracted based on both visual and structural traits.  Another company, DiffBot, is also taking care of the “dirty work” in the cloud, allowing entrepreneurs and developers to focus on their business instead of the semantics involved in parsing.  DiffBot stands out because of their unique approach.  Instead of looking at the data as a computer, they are looking visually, like a human would.  They first classify what type of webpage (eg. article, blog post, product, etc.) and then proceed to extract what visually appears to be relevant data for that page type (article title, most relevant image, etc).

Currently their website lists APIs for Page Classification (check out their infographic), as well as parsing Article type webpages.  Much of the web, including discussion boards, events, e-commerce data, etc. remains as potential future API offerings and it will be interesting to see which they go after next.


Source: http://blog.programmableweb.com/2012/09/13/web-scraping-evolved-apis-for-turning-webpage-content-into-valuable-data/

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