Unleash the Hidden Potential of Your Business Data With Data Mining and Extraction Services

Every business, small or large, is continuously amassing data about customers, employees and nearly every process in their business cycle. Although all management staff utilize data collected from their business as a basis for decision making in areas such as marketing, forecasting, planning and trouble-shooting, very often they are just barely scratching the surface. Manual data analysis is time-consuming and error-prone, and its limited functions result in the overlooking of valuable information that improve bottom-lines. Often, the sheer quantity of data prevents accurate and useful analysis by those without the necessary technology and experience. It is an unfortunate reality that much of this data goes to waste and companies often never realize that a valuable resource is being left untapped.

Automated data mining services allow your company to tap into the latent potential of large volumes of raw data and convert it into information that can be used in decision-making. While the use of the latest software makes data mining and data extraction fast and affordable, experienced professional data analysts are a key part of the data mining services offered by our company. Making the most of your data involves more than automatically generated reports from statistical software. It takes analysis and interpretation skills that can only be performed by experienced data analysis experts to ensure that your business databases are translated into information that you can easily comprehend and use in almost every aspect of your business.

Who Can Benefit From Data Mining Services?

If you are wondering what types of companies can benefit from data extraction services, the answer is virtually every type of business. This includes organizations dealing in customer service, sales and marketing, financial products, research and insurance.

How is Raw Data Converted to Useful Information?

There are several steps in data mining and extraction, but the most important thing for you as a business owner is to be assured that, throughout the process, the confidentiality of your data is our primary concern. Upon receiving your data, it is converted into the necessary format so that it can be entered into a data warehouse system. Next, it is compiled into a database, which is then sifted through by data mining experts to identify relevant data. Our trained and experienced staff then scan and analyze your data using a variety of methods to identify association or relationships between variables; clusters and classes, to identify correlations and groups within your data; and patterns, which allow trends to be identified and predictions to be made. Finally, the results are compiled in the form of written reports, visual data and spreadsheets, according to the needs of your business.

Our team of data mining, extraction and analyses experts have already helped a great number of businesses to tap into the potential of their raw data, with our speedy, cost-efficient and confidential services. Contact us today for more information on how our data mining and extraction services can help your business.


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Business Intelligence & Data Warehousing in a Business Perspective

Business Intelligence

Business Intelligence has become a very important activity in the business arena irrespective of the domain due to the fact that managers need to analyze comprehensively in order to face the challenges.

Data sourcing, data analysing, extracting the correct information for a given criteria, assessing the risks and finally supporting the decision making process are the main components of BI.

In a business perspective, core stakeholders need to be well aware of all the above stages and be crystal clear on expectations. The person, who is being assigned with the role of Business Analyst (BA) for the BI initiative either from the BI solution providers' side or the company itself, needs to take the full responsibility on assuring that all the above steps are correctly being carried out, in a way that it would ultimately give the business the expected leverage. The management, who will be the users of the BI solution, and the business stakeholders, need to communicate with the BA correctly and elaborately on their expectations and help him throughout the process.

Data sourcing is an initial yet crucial step that would have a direct impact on the system where extracting information from multiple sources of data has to be carried out. The data may be on text documents such as memos, reports, email messages, and it may be on the formats such as photographs, images, sounds, and they can be on more computer oriented sources like databases, formatted tables, web pages and URL lists. The key to data sourcing is to obtain the information in electronic form. Therefore, typically scanners, digital cameras, database queries, web searches, computer file access etc, would play significant roles. In a business perspective, emphasis should be placed on the identification of the correct relevant data sources, the granularity of the data to be extracted, possibility of data being extracted from identified sources and the confirmation that only correct and accurate data is extracted and passed on to the data analysis stage of the BI process.
Business oriented stake holders guided by the BA need to put in lot of thought during the analyzing stage as well, which is the second phase. Synthesizing useful knowledge from collections of data should be done in an analytical way using the in-depth business knowledge whilst estimating current trends, integrating and summarizing disparate information, validating models of understanding, and predicting missing information or future trends. This process of data analysis is also called data mining or knowledge discovery. Probability theory, statistical analysis methods, operational research and artificial intelligence are the tools to be used within this stage. It is not expected that business oriented stake holders (including the BA) are experts of all the above theoretical concepts and application methodologies, but they need to be able to guide the relevant resources in order to achieve the ultimate expectations of BI, which they know best.

Identifying relevant criteria, conditions and parameters of report generation is solely based on business requirements, which need to be well communicated by the users and correctly captured by the BA. Ultimately, correct decision support will be facilitated through the BI initiative and it aims to provide warnings on important events, such as takeovers, market changes, and poor staff performance, so that preventative steps could be taken. It seeks to help analyze and make better business decisions, to improve sales or customer satisfaction or staff morale. It presents the information that manager's need, as and when they need it.

In a business sense, BI should go several steps forward bypassing the mere conventional reporting, which should explain "what has happened?" through baseline metrics. The value addition will be higher if it can produce descriptive metrics, which will explain "why has it happened?" and the value added to the business will be much higher if predictive metrics could be provided to explain "what will happen?" Therefore, when providing a BI solution, it is important to think in these additional value adding lines.

Data warehousing

In the context of BI, data warehousing (DW) is also a critical resource to be implemented to maximize the effectiveness of the BI process. BI and DW are two terminologies that go in line. It has come to a level where a true BI system is ineffective without a powerful DW, in order to understand the reality behind this statement, it's important to have an insight in to what DW really is.

A data warehouse is one large data store for the business in concern which has integrated, time variant, non volatile collection of data in support of management's decision making process. It will mainly have transactional data which would facilitate effective querying, analyzing and report generation, which in turn would give the management the required level of information for the decision making.

The reasons to have BI together with DW

At this point, it should be made clear why a BI tool is more effective with a powerful DW. To query, analyze and generate worthy reports, the systems should have information available. Importantly, transactional information such as sales data, human resources data etc. are available normally in different applications of the enterprise, which would obviously be physically held in different databases. Therefore, data is not at one particular place, hence making it very difficult to generate intelligent information. The level of reports expected today, are not merely independent for each department, but managers today want to analyze data and relationships across the enterprise so that their BI process is effective. Therefore, having data coming from all the sources to one location in the form of a data warehouse is crucial for the success of the BI initiative. In a business viewpoint, this message should be passed and sold to the managements of enterprises so that they understand the value of the investment. Once invested, its gains could be achieved over several years, in turn marking a high ROI.

Investment costs for a DW in the short term may look quite high, but it's important to re-iterate that the gains are much higher and it will span over many years to come. It also reduces future development cost since with the DW any requested report or view could be easily facilitated. However, it is important to find the right business sponsor for the project. He or she needs to communicate regularly with executives to ensure that they understand the value of what's being built. Business sponsors need to be decisive, take an enterprise-wide perspective and have the authority to enforce their decisions.

Process

Implementation of a DW itself overlaps with some phases of the above explained BI process and it's important to note that in a process standpoint, DW falls in to the first few phases of the entire BI initiative. Gaining highly valuable information out of DW is the latter part of the BI process. This can be done in many ways. DW can be used as the data repository of application servers that run decision support systems, management Information Systems, Expert systems etc., through them, intelligent information could be achieved. But one of the latest strategies is to build cubes out of the DW and allow users to analyze data in multiple dimensions, and also provide with powerful analytical supporting such as drill down information in to granular levels. Cube is a concept that is different to the traditional relational 2-dimensional tabular view, and it has multiple dimensions, allowing a manager to analyze data based on multiple factors, and not just two factors. On the other hand, it allows the user to select whatever the dimension he wish to choose for analyzing purposes and not be limited by one fixed view of data, which is called as slice & dice in DW terminology.

BI for a serious enterprise is not just a phase of a computerization process, but it is one of the major strategies behind the entire organizational drivers. Therefore management should sit down and build up a BI strategy for the company and identify the information they require in each business direction within the enterprise. Given this, BA needs to analyze the organizational data sources in order to build up the most effective DW which would help the strategized BI process.

High level Ideas on Implementation

At the heart of the data warehousing process is the extract, transform, and load (ETL) process. Implementation of this merely is a technical concern but it's a business concern to make sure it is designed in such a way that it ultimately helps to satisfy the business requirements. This process is responsible for connecting to and extracting data from one or more transactional systems (source systems), transforming it according to the business rules defined through the business objectives, and loading it into the all important data model. It is at this point where data quality should be gained. Of the many responsibilities of the data warehouse, the ETL process represents a significant portion of all the moving parts of the warehousing process.

Creation of a powerful DW depends on the correctness of data modeling, which is the responsibility of the database architect of the project, but BA needs to play a pivotal role providing him with correct data sources, data requirements and most importantly business dimensions. Business Dimensional modeling is a special method used for DW projects and this normally should be carried out by the BA and from there onwards technical experts should take up the work. Dimensions are perspectives specific to a business that could be used for analysis purposes. As an example, for a sales database, the dimensions could include Product, Time, Store, etc. Obviously these dimensions differ from one business to another and hence for each DW initiative those dimensions should be correctly identified and that could be very well done by a person who has experience in the DW domain and understands the business as well, making it apparent that DW BA is the person responsible.

Each of the identified dimensions would be turned in to a dimension table at the implementation phase, and the objective of the above explained ETL process is to fill up these dimension tables, which in turn will be taken to the level of the DW after performing some more database activities based on a strong underlying data model. Implementation details are not important for a business stakeholder but being aware of high level process to this level is important so that they are also on the same pitch as that of the developers and can confirm that developers are actually doing what they are supposed to do and would ultimately deliver what they are supposed to deliver.

Security is also vital in this regard, since this entire effort deals with highly sensitive information and identification of access right to specific people to specific information should be correctly identified and captured at the requirements analysis stage.

Advantages

There are so many advantages of BI system. More presentation of analytics directly to the customer or supply chain partner will be possible. Customer scores, customer campaigns and new product bundles can all be produced from analytic structures resulting in high customer retention and creation of unique products. More collaboration within information can be achieved from effective BI. Rather than middle managers getting great reports and making their own areas look good, information will be conveyed into other functions and rapidly shared to create collaborative decisions increasing the efficiency and accuracy. The return on human capital will be greatly increased.

Managers at all levels will save their time on data analysis, and hence saving money for the enterprise, as the time of managers is equal to money in a financial perspective. Since powerful BI would enable monitoring internal processes of the enterprises more closely and allow making them more efficient, the overall success of the organization would automatically grow. All these would help to derive a high ROI on BI together with a strong DW. It is a common experience to notice very high ROI figures on such implementations, and it is also important to note that there are many non-measurable gains whilst we consider most of the measurable gains for the ROI calculation. However, at a stage where it is intended to take the management buy-in for the BI initiative, it's important to convert all the non measurable gains in to monitory values as much as possible, for example, saving of managers time can be converted in to a monitory value using his compensation.

The author has knowledge in both Business and IT. Started career as a Software Engineer and moved to work in the business analysis area of a premier US based software company.



Source: http://ezinearticles.com/?Business-Intelligence-and-Data-Warehousing-in-a-Business-Perspective&id=35640

Data Mining - Critical for Businesses to Tap the Unexplored Market

Knowledge discovery in databases (KDD) is an emerging field and is increasingly gaining importance in today's business. The knowledge discovery process, however, is vast, involving understanding of the business and its requirements, data selection, processing, mining and evaluation or interpretation; it does not have any pre-defined set of rules to go about solving a problem. Among the other stages, the data mining process holds high importance as the task involves identification of new patterns that have not been detected earlier from the dataset. This is relatively a broad concept involving web mining, text mining, online mining etc.

What Data Mining is and what it is not?

The data mining is the process of extracting information, which has been collected, analyzed and prepared, from the dataset and identifying new patterns from that information. At this juncture, it is also important to understand what it is not. The concept is often misunderstood for knowledge gathering, processing, analysis and interpretation/ inference derivation. While these processes are absolutely not data mining, they are very much necessary for its successful implementation.

The 'First-mover Advantage'

One of the major goals of the data mining process is to identify an unknown or rather unexplored segment that had always existed in the business or industry, but was overlooked. The process, when done meticulously using appropriate techniques, could even make way for niche segments providing companies the first-mover advantage. In any industry, the first-mover would bag the maximum benefits and exploit resources besides setting standards for other players to follow. The whole process is thus considered to be a worthy approach to identify unknown segments.

The online knowledge collection and research is the concept involving many complications and, therefore, outsourcing the data mining services often proves viable for large companies that cannot devote time for the task. Outsourcing the web mining services or text mining services would save an organization's productive time which would otherwise be spent in researching.

The data mining algorithms and challenges

Every data mining task follows certain algorithms using statistical methods, cluster analysis or decision tree techniques. However, there is no single universally accepted technique that can be adopted for all. Rather, the process completely depends on the nature of the business, industry and its requirements. Thus, appropriate methods have to be chosen depending upon the business operations.

The whole process is a subset of knowledge discovery process and as such involves different challenges. Analysis and preparation of dataset is very crucial as the well-researched material could assist in extracting only the relevant yet unidentified information useful for the business. Hence, the analysis of the gathered material and preparation of dataset, which also considers industrial standards during the process, would consume more time and labor. Investment is another major challenge in the process as it involves huge cost on deploying professionals with adequate domain knowledge plus knowledge on statistical and technological aspects.

The importance of maintaining a comprehensive database prompted the need for data mining which, in turn, paved way for niche concepts. Though the concept has been present for years now, companies faced with ever growing competition have realized its importance only in the recent years. Besides being relevant, the dataset from where the information is actually extracted also has to be sufficient enough so as to pull out and identify a new dimension. Yet, a standardized approach would result in better understanding and implementation of the newly identified patterns.


Source: http://ezinearticles.com/?Data-Mining---Critical-for-Businesses-to-Tap-the-Unexplored-Market&id=6745886

Data Management Services

In recent studies it has been revealed that any business activity has astonishing huge volumes of data, hence the ideas has to be organized well and can be easily gotten when need arises. Timely and accurate solutions are important in facilitating efficiency in any business activity. With the emerging professional outsourcing and data organizing companies nowadays many services are offered that matches the various kinds of managing the data collected and various business activities. This article looks at some of the benefits that accrue of offered by the professional data mining companies.

Entering of data

These kinds of services are quite significant since they help in converting the data that is needed in high ideal and format that is digitized. In internet some of this data can found that is original and handwritten. In printed paper documents and or text are not likely to contain electronic or needed formats. The best example in this context is books that need to be converted to e-books. In insurance companies they also depend on this process in processing the claims of insurance and at the same time apply to the law firms that offer support to analyze and process legal documents.

EDC

That is referred to as electronic data. This method is mostly used by clinical researchers and other related organization in medical. The electronic data and capture methods are used in the utilization in managing trials and research. The data mining and data management services are given in upcoming databases for studies. The ideas contained can easily be captured, other services being done and the survey taken.

Data changing

This is the process of converting data found in one format to another. Data extraction process often involves mining data from an existing system, formatting it, cleansing it and can be installed to enhance both availability and retrieving of information easily. Extensive testing and application are the requirements of this process. The service offered by data mining companies includes SGML conversion, XML conversion, CAD conversion, HTML conversion, image conversion.

Managing data service

In this service it involves the conversion of documents. It is where one character of a text may need to be converted to another. If we take an example it is easy to change image, video or audio file formats to other applications of the software that can be played or displayed. In indexing and scanning is where the services are mostly offered.

Data extraction and cleansing

Significant information and sequences from huge databases and websites extraction firms use this kind of service. The data harvested is supposed to be in a productive way and should be cleansed to increase the quality. Both manual and automated data cleansing services are offered by data mining organizations. This helps to ensure that there is accuracy, completeness and integrity of data. Also we keep in mind that data mining is never enough.

Web scraping, data extraction services, web extraction, imaging, catalog conversion, web data mining and others are the other management services offered by data mining organization. If your business organization needs such services here is one that can be of great significance that is web scraping and data mining



Source: http://ezinearticles.com/?Data-Management-Services&id=7131758

Text Data Mining Can Be Profitable

There are billions of search terms performed on the internet every year,and the companies which make use of this vast amount of information are the ones who will be able to market effectively in the future. It is here that text data mining comes into its own, a technique which enables researchers to find patterns within groups of text which will enable them to make predictions as to how customers or other groups of people will act in the future. This article will take a look at text data mining and how we can help various groups of people to find the best things in the data analysis.

It is always a good idea to do some study of the text mining techniques before going on to text mining implementation, and this can be said to be especially true of the insurance industry where not only text mining but also generic data mining using in statistics can be a great help in determining profitability and also showing actuaries how to make future calculations.

Consultancy is an important part of text data mining, and the text mining consultant can bring a huge amount of knowledge to a company whatever the service or services that are providing, particularly if he has an extensive knowledge of text data mining technology and can help to build a system around it.

Of course it is not only commercial applications that can use text mining, because it also has used in security, in that it can help to track criminal intent on the Internet. There are also applications in the biomedical world, in order to help find clusters of data in the right way. But it is in the online world and in the field of marketing that text mining is being used extensively, particularly in customer relationship management [CRM] techniques, where the tools are among some of the most advanced.

Knowing how text mining algorithms work is essential for any consultant who works in this field, because it is an important tool in the marketing technique possibilities. By understanding how text data mining can help an organization a consultant or marketer can make great strides in profitability and this is something that most organizations would be glad for.



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Data Entry - Build Your Data Entry Company and Earn Big

Times are tough and to meet your needs it is not enough to work hard. It is very important that you should learn to work smart as well. Working home based as a data entry provider will allow you to earn money to pay your bills and face other financial obligations but have you ever thought of forming your own team of data entry specialists so that your earnings will double? The thought of starting a company is frightening. It would mean going to a bank for a loan, renting or building an office, procurement of office equipment and other fixtures, hiring, training of staffs, completion of government requirements and most of all it will monopolize a great deal of your family time. You do not want to subject yourself to such an ordeal right? Before you give the idea a brush off why not finish reading this article first and you might be surprised.

Starting-up a small data entry business is easy. First, you may form your staffs by employing family members and friends who wants to earn extra money or work full-time. You may hire your spouse, retired parents, children who are in college and in need of extra cash, jobless friends, and neighbors. You can even post a job lead to various job sites in the internet that you are looking for people to form your dream team. I suggest that you try checking Odesk web site. Be daring, get service providers offshore such as Philippines and India. These countries are known to be rich in intellectual capital and you can pay as low as a dollar per hour for labor.

There is no need for you to rent or build an office. Your staffs will communicate to you via the internet every day. Your staffs will use their personal computers and will pay their own internet subscription and electric bills.

The job do not require high educational attainment and lengthy trainings. To get the edge in this industry you may enroll yourself in a data entry program available in the country. One of the reputed programs in the country is the National Data Entry. The program will require you to pay a onetime fee which is below one hundred dollars. The fee will cover your training materials and the cost of keeping and maintaining the program web site. After your training you can transfer your skills and knowledge to your dream team.

Life in this world is ever changing and everything is possible. We all need to open our arms to possibilities and embrace every opportunity that will allow us to widen our existential horizons. If we limit ourselves to the safe and easy side in life, we will always get what we already have.

Putting up your own business will not only emancipate you from the daily grind of being employed. As you are earning money you are also helping others because you provided them the opportunity to work for a living and improve their quality of life.


Source: http://ezinearticles.com/?Data-Entry---Build-Your-Data-Entry-Company-and-Earn-Big&id=3239488

What is Data Mining? Why Data Mining is Important?

Searching, Collecting, Filtering and Analyzing of data define as data mining. The large amount of information can be retrieved from wide range of form such as different data relationships, patterns or any significant statistical co-relations. Today the advent of computers, large databases and the internet is make easier way to collect millions, billions and even trillions of pieces of data that can be systematically analyzed to help look for relationships and to seek solutions to difficult problems.

The government, private company, large organization and all businesses are looking for large volume of information collection for research and business development. These all collected data can be stored by them to future use. Such kind of information is most important whenever it is require. It will take very much time for searching and find require information from the internet or any other resources.

Here is an overview of data mining services inclusion:

* Market research, product research, survey and analysis
* Collection information about investors, funds and investments
* Forums, blogs and other resources for customer views/opinions
* Scanning large volumes of data
* Information extraction
* Pre-processing of data from the data warehouse
* Meta data extraction
* Web data online mining services
* data online mining research
* Online newspaper and news sources information research
* Excel sheet presentation of data collected from online sources
* Competitor analysis
* data mining books
* Information interpretation
* Updating collected data

After applying the process of data mining, you can easily information extract from filtered information and processing the refining the information. This data process is mainly divided into 3 sections; pre-processing, mining and validation. In short, data online mining is a process of converting data into authentic information.

The most important is that it takes much time to find important information from the data. If you want to grow your business rapidly, you must take quick and accurate decisions to grab timely available opportunities.

Outsourcing Web Research is one of the best data mining outsourcing organizations having more than 17 years of experience in the market research industry. To know more information about our company please contact us.


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All About PC Data Recovery

PC Data Recovery is the procedure of retrieving data from database or storage systems. You can recover data by using floppy discs, DVDs, hard drives, CDs, Memory cards etc. It helps you to recover all the corrupt or lost data in a professional, secure and fast manner. For all the businesses and IT companies, data recovery is important for saving data in an appropriate manner. The experts at the computer repair Sydney discuss some tips for it.

May be you are under a lot of mental stress and worried about how to retrieve the lost data as quickly as possible. The time for preventing your data from getting corrupt or lost has gone - the problem at hand is that of PC Data Recovery.

Firstly you could get hold of your tech savvy relatives or friends; if you are lucky enough, they will help you out and if in case you are really fortunate then they might even have data recovery software. However, if you are not lucky then you have to get your wallet out because data recovery is going to be an expensive affair. Also, just prepare yourself for a mundane and time-consuming act. Try to identify the problem with your hard disc. Either your computer fails to boot up or if it boots up, it does not show other drives. Listen carefully to your hard drive, if in case it makes some noises like that of ticking, scratching or scraping then you have to take it to the PC Data Recovery center where the experts solve your problem. As these services are time-consuming and expensive, you have to decide the worth of data that is stored in the hard disc:

If it is only a set of downloaded music or a few games then you should delete it and accept the data loss.

On the other hand, if it is some important information like a product or book that you have been working for years, then you have to take your system to a data recovery center for an evaluation- it generally costs nothing.

Therefore, if the hard drive is safe then you have a decent chance of retrieving data yourself. Firstly, you have to download some important software that will help in recovering data. Unfortunately, the reputed software are expensive, however, the good news is that many companies allow you to use them on a trial basis. Although there are some freeware versions but they are not easy to use. The execution of the further procedure depends on the set of hard drive:

    If your system has a single hard drive that is not partitioned then you have to attach hard drive to another system that has ample space to store all the lost data. This is technical so if in case you do not have any technical knowledge then get a computer savvy relative or friend to help you in PC Data Recovery.
    If in case, your computer system has a multiple drive set up and it boots up fine then all you have to do is to download the software to read the files.




Source: http://ezinearticles.com/?All-About-PC-Data-Recovery&id=3240328

Collecting Data With Web Scrapers

There is a large amount of data available only through websites. However, as many people have found out, trying to copy data into a usable database or spreadsheet directly out of a website can be a tiring process. Data entry from internet sources can quickly become cost prohibitive as the required hours add up. Clearly, an automated method for collating information from HTML-based sites can offer huge management cost savings.

Web scrapers are programs that are able to aggregate information from the internet. They are capable of navigating the web, assessing the contents of a site, and then pulling data points and placing them into a structured, working database or spreadsheet. Many companies and services will use programs to web scrape, such as comparing prices, performing online research, or tracking changes to online content.

Let's take a look at how web scrapers can aid data collection and management for a variety of purposes.

Improving On Manual Entry Methods

Using a computer's copy and paste function or simply typing text from a site is extremely inefficient and costly. Web scrapers are able to navigate through a series of websites, make decisions on what is important data, and then copy the info into a structured database, spreadsheet, or other program. Software packages include the ability to record macros by having a user perform a routine once and then have the computer remember and automate those actions. Every user can effectively act as their own programmer to expand the capabilities to process websites. These applications can also interface with databases in order to automatically manage information as it is pulled from a website.

Aggregating Information

There are a number of instances where material stored in websites can be manipulated and stored. For example, a clothing company that is looking to bring their line of apparel to retailers can go online for the contact information of retailers in their area and then present that information to sales personnel to generate leads. Many businesses can perform market research on prices and product availability by analyzing online catalogues.

Data Management

Managing figures and numbers is best done through spreadsheets and databases; however, information on a website formatted with HTML is not readily accessible for such purposes. While websites are excellent for displaying facts and figures, they fall short when they need to be analyzed, sorted, or otherwise manipulated. Ultimately, web scrapers are able to take the output that is intended for display to a person and change it to numbers that can be used by a computer. Furthermore, by automating this process with software applications and macros, entry costs are severely reduced.

This type of data management is also effective at merging different information sources. If a company were to purchase research or statistical information, it could be scraped in order to format the information into a database. This is also highly effective at taking a legacy system's contents and incorporating them into today's systems.

Overall, a web scraper is a cost effective user tool for data manipulation and management.



Source: http://ezinearticles.com/?Collecting-Data-With-Web-Scrapers&id=4223877

Pushing Bad Data- Google's Latest Black Eye

Google stopped counting, or at least publicly displaying, the number of pages it indexed in September of 05, after a school-yard "measuring contest" with rival Yahoo. That count topped out around 8 billion pages before it was removed from the homepage. News broke recently through various SEO forums that Google had suddenly, over the past few weeks, added another few billion pages to the index. This might sound like a reason for celebration, but this "accomplishment" would not reflect well on the search engine that achieved it.

What had the SEO community buzzing was the nature of the fresh, new few billion pages. They were blatant spam- containing Pay-Per-Click (PPC) ads, scraped content, and they were, in many cases, showing up well in the search results. They pushed out far older, more established sites in doing so. A Google representative responded via forums to the issue by calling it a "bad data push," something that met with various groans throughout the SEO community.

How did someone manage to dupe Google into indexing so many pages of spam in such a short period of time? I'll provide a high level overview of the process, but don't get too excited. Like a diagram of a nuclear explosive isn't going to teach you how to make the real thing, you're not going to be able to run off and do it yourself after reading this article. Yet it makes for an interesting tale, one that illustrates the ugly problems cropping up with ever increasing frequency in the world's most popular search engine.

A Dark and Stormy Night

Our story begins deep in the heart of Moldva, sandwiched scenically between Romania and the Ukraine. In between fending off local vampire attacks, an enterprising local had a brilliant idea and ran with it, presumably away from the vampires... His idea was to exploit how Google handled subdomains, and not just a little bit, but in a big way.

The heart of the issue is that currently, Google treats subdomains much the same way as it treats full domains- as unique entities. This means it will add the homepage of a subdomain to the index and return at some point later to do a "deep crawl." Deep crawls are simply the spider following links from the domain's homepage deeper into the site until it finds everything or gives up and comes back later for more.

Briefly, a subdomain is a "third-level domain." You've probably seen them before, they look something like this: subdomain.domain.com. Wikipedia, for instance, uses them for languages; the English version is "en.wikipedia.org", the Dutch version is "nl.wikipedia.org." Subdomains are one way to organize large sites, as opposed to multiple directories or even separate domain names altogether.

So, we have a kind of page Google will index virtually "no questions asked." It's a wonder no one exploited this situation sooner. Some commentators believe the reason for that may be this "quirk" was introduced after the recent "Big Daddy" update. Our Eastern European friend got together some servers, content scrapers, spambots, PPC accounts, and some all-important, very inspired scripts, and mixed them all together thusly...

Five Billion Served- And Counting...

First, our hero here crafted scripts for his servers that would, when GoogleBot dropped by, start generating an essentially endless number of subdomains, all with a single page containing keyword-rich scraped content, keyworded links, and PPC ads for those keywords. Spambots are sent out to put GoogleBot on the scent via referral and comment spam to tens of thousands of blogs around the world. The spambots provide the broad setup, and it doesn't take much to get the dominos to fall.

GoogleBot finds the spammed links and, as is its purpose in life, follows them into the network. Once GoogleBot is sent into the web, the scripts running the servers simply keep generating pages- page after page, all with a unique subdomain, all with keywords, scraped content, and PPC ads. These pages get indexed and suddenly you've got yourself a Google index 3-5 billion pages heavier in under 3 weeks.

Reports indicate, at first, the PPC ads on these pages were from Adsense, Google's own PPC service. The ultimate irony then is Google benefits financially from all the impressions being charged to AdSense users as they appear across these billions of spam pages. The AdSense revenues from this endeavor were the point, after all. Cram in so many pages that, by sheer force of numbers, people would find and click on the ads in those pages, making the spammer a nice profit in a very short amount of time.

Billions or Millions? What is Broken?

Word of this achievement spread like wildfire from the DigitalPoint forums. It spread like wildfire in the SEO community, to be specific. The "general public" is, as of yet, out of the loop, and will probably remain so. A response by a Google engineer appeared on a Threadwatch thread about the topic, calling it a "bad data push". Basically, the company line was they have not, in fact, added 5 billions pages. Later claims include assurances the issue will be fixed algorithmically. Those following the situation (by tracking the known domains the spammer was using) see only that Google is removing them from the index manually.

The tracking is accomplished using the "site:" command. A command that, theoretically, displays the total number of indexed pages from the site you specify after the colon. Google has already admitted there are problems with this command, and "5 billion pages", they seem to be claiming, is merely another symptom of it. These problems extend beyond merely the site: command, but the display of the number of results for many queries, which some feel are highly inaccurate and in some cases fluctuate wildly. Google admits they have indexed some of these spammy subdomains, but so far haven't provided any alternate numbers to dispute the 3-5 billion showed initially via the site: command.

Over the past week the number of the spammy domains & subdomains indexed has steadily dwindled as Google personnel remove the listings manually. There's been no official statement that the "loophole" is closed. This poses the obvious problem that, since the way has been shown, there will be a number of copycats rushing to cash in before the algorithm is changed to deal with it.

Conclusions

There are, at minimum, two things broken here. The site: command and the obscure, tiny bit of the algorithm that allowed billions (or at least millions) of spam subdomains into the index. Google's current priority should probably be to close the loophole before they're buried in copycat spammers. The issues surrounding the use or misuse of AdSense are just as troubling for those who might be seeing little return on their adverting budget this month.

Do we "keep the faith" in Google in the face of these events? Most likely, yes. It is not so much whether they deserve that faith, but that most people will never know this happened. Days after the story broke there's still very little mention in the "mainstream" press. Some tech sites have mentioned it, but this isn't the kind of story that will end up on the evening news, mostly because the background knowledge required to understand it goes beyond what the average citizen is able to muster. The story will probably end up as an interesting footnote in that most esoteric and neoteric of worlds, "SEO History."

Mr. Lester has served for 5 years as the webmaster for ApolloHosting.com and previously worked in the IT industry an additional 5 years, acquiring knowledge of hosting, design, and search engine optimization. Apollo Hosting provides website hosting [http://www.apollohosting.com], ecommerce hosting [http://www.apollohosting.com/e-commerce], vps hosting, and web design services to a wide range of customers. Established in 1999, Apollo prides itself on the highest levels of customer support.



Source: http://ezinearticles.com/?Pushing-Bad-Data--Googles-Latest-Black-Eye&id=226954

Benefits of Predictive Analytics and Data Mining Services

Predictive Analytics is the process of dealing with variety of data and apply various mathematical formulas to discover the best decision for a given situation. Predictive analytics gives your company a competitive edge and can be used to improve ROI substantially. It is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.

Predictive analytics can be helpful in answering questions like:

    Who are most likely to respond to your offer?
    Who are most likely to ignore?
    Who are most likely to discontinue your service?
    How much a consumer will spend on your product?
    Which transaction is a fraud?
    Which insurance claim is a fraudulent?
    What resource should I dedicate at a given time?

Benefits of Data mining include:

    Better understanding of customer behavior propels better decision
    Profitable customers can be spotted fast and served accordingly
    Generate more business by reaching hidden markets
    Target your Marketing message more effectively
    Helps in minimizing risk and improves ROI.
    Improve profitability by detecting abnormal patterns in sales, claims, transactions etc
    Improved customer service and confidence
    Significant reduction in Direct Marketing expenses

Basic steps of Predictive Analytics are as follows:

    Spot the business problem or goal
    Explore various data sources such as transaction history, user demography, catalog details, etc)
    Extract different data patterns from the above data
    Build a sample model based on data & problem
    Classify data, find valuable factors, generate new variables
    Construct a Predictive model using sample
    Validate and Deploy this Model

Standard techniques used for it are:

    Decision Tree
    Multi-purpose Scaling
    Linear Regressions
    Logistic Regressions
    Factor Analytics
    Genetic Algorithms
    Cluster Analytics
    Product Association

Should you have any queries regarding Data Mining or Predictive Analytics applications, please feel free to contact us. We would be pleased to answer each of your queries in detail. Email us at info@outsourcingwebresearch.com


Source: http://ezinearticles.com/?Benefits-of-Predictive-Analytics-and-Data-Mining-Services&id=4766989

Web scraping: Techniques of web data extraction

Web scraping also called web harvesting or web data extraction, is a computer software technique of extracting information from websites. Usually, such software programs simulate human exploration of the World Wide Web by either implementing low-level Hypertext Transfer Protocol (HTTP), or embedding a fully-fledged web browser, such as Internet Explorer or Mozilla Firefox.

Web scraping is closely related to web indexing, which indexes information on the web using a bot and is a universal technique adopted by most search engines. In contrast, web scraping focuses more on the transformation of unstructured data on the web, typically in HTML format, into structured data that can be stored and analyzed in a central local database or spreadsheet. Web scraping is also related to web automation, which simulates human browsing using computer software. Uses of web scraping include online price comparison, weather data monitoring, website change detection, research, web mashup and web data integration.

Web scraping is the process of automatically collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions. Web scraping, instead, favors practical solutions based on existing technologies that are often entirely ad hoc. Therefore, there are different levels of automation that existing web-scraping technologies can provide:

§  Human copy-and-paste: Sometimes even the best web-scraping technology cannot replace a human’s manual examination and copy-and-paste, and sometimes this may be the only workable solution when the websites for scraping explicitly set up barriers to prevent machine automation.

§  Text grepping and regular expression matching: A simple yet powerful approach to extract information from web pages can be based on the UNIX grep command or regular expression matching facilities of programming languages (for instance Perl or Python).

§  HTTP programming: Static and dynamic web pages can be retrieved by posting HTTP requests to the remote web server using socket programming.

§  Data mining algorithms. Many websites have large collections of pages generated dynamically from an underlying structured source like a database. Data of the same category are typically encoded into similar pages by a common script or template. In data mining, a program that detects such templates in a particular information source, extracts its content and translates it into a relational form is called a wrapper. Wrapper generation algorithms assume that input pages of a wrapper induction system conform to a common template and that they can be easily identified in terms of a URL common scheme.

§  DOM parsing: By embedding a full-fledged web browser, such as the Internet Explorer or the Mozilla browser control, programs can retrieve the dynamic contents generated by client side scripts. These browser controls also parse web pages into a DOM tree, based on which programs can retrieve parts of the pages.

§  HTML parsers: Some semi-structured data query languages, such as XQuery and the HTQL, can be used to parse HTML pages and to retrieve and transform page content.

§  Web-scraping software: There are many software tools available that can be used to customize web-scraping solutions. This software may attempt to automatically recognize the data structure of a page or provide a recording interface that removes the necessity to manually write web-scraping code, or some scripting functions that can be used to extract and transform content, and database interfaces that can store the scraped data in local databases.

§  Vertical aggregation platforms: There are several companies that have developed vertical specific harvesting platforms. These platforms create and monitor a multitude of “bots” for specific verticals with no man-in-the-loop, and no work related to a specific target site. The preparation involves establishing the knowledge base for the entire vertical and then the platform creates the bots automatically. The platform's robustness is measured by the quality of the information it retrieves (usually number of fields) and its scalability (how quick it can scale up to hundreds or thousands of sites). This scalability is mostly used to target the Long Tail of sites that common aggregators find complicated or too labor intensive to harvest content from.

§  Semantic annotation recognizing: The pages being scraped may embrace metadata or semantic markups and annotations, which can be used to locate specific data snippets. If the annotations are embedded in the pages, as Microformat does, this technique can be viewed as a special case of DOM parsing. In another case, the annotations, organized into a semantic layer, are stored and managed separately from the web pages, so the scrapers can retrieve data schema and instructions from this layer before scraping the pages.


Source: http://www.thehackingarticles.com/2012/08/web-scraping-techniques-of-web-data.html#.Ua9LoqyWbDc

Web Data Extraction

The Internet as we know today is a repository of information that can be accessed across geographical societies. In just over two decades, the Web has moved from a university curiosity to a fundamental research, marketing and communications vehicle that impinges upon the everyday life of most people in all over the world. It is accessed by over 16% of the population of the world spanning over 233 countries.

As the amount of information on the Web grows, that information becomes ever harder to keep track of and use. Compounding the matter is this information is spread over billions of Web pages, each with its own independent structure and format. So how do you find the information you're looking for in a useful format - and do it quickly and easily without breaking the bank?

Search Isn't Enough

Search engines are a big help, but they can do only part of the work, and they are hard-pressed to keep up with daily changes. For all the power of Google and its kin, all that search engines can do is locate information and point to it. They go only two or three levels deep into a Web site to find information and then return URLs. Search Engines cannot retrieve information from deep-web, information that is available only after filling in some sort of registration form and logging, and store it in a desirable format. In order to save the information in a desirable format or a particular application, after using the search engine to locate data, you still have to do the following tasks to capture the information you need:

· Scan the content until you find the information.

· Mark the information (usually by highlighting with a mouse).

· Switch to another application (such as a spreadsheet, database or word processor).

· Paste the information into that application.

Its not all copy and paste

Consider the scenario of a company is looking to build up an email marketing list of over 100,000 thousand names and email addresses from a public group. It will take up over 28 man-hours if the person manages to copy and paste the Name and Email in 1 second, translating to over $500 in wages only, not to mention the other costs associated with it. Time involved in copying a record is directly proportion to the number of fields of data that has to copy/pasted.

Is there any Alternative to copy-paste?

A better solution, especially for companies that are aiming to exploit a broad swath of data about markets or competitors available on the Internet, lies with usage of custom Web harvesting software and tools.

Web harvesting software automatically extracts information from the Web and picks up where search engines leave off, doing the work the search engine can't. Extraction tools automate the reading, the copying and pasting necessary to collect information for further use. The software mimics the human interaction with the website and gathers data in a manner as if the website is being browsed. Web Harvesting software only navigate the website to locate, filter and copy the required data at much higher speeds that is humanly possible. Advanced software even able to browse the website and gather data silently without leaving the footprints of access.

The next article of this series will give more details about how such softwares and uncover some myths on web harvesting.


Source: http://ezinearticles.com/?Web-Data-Extraction&id=575212