Web Data Mining Software

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Data mining is the process by using certain algorithms, software and tools to retrieve, collect, analyze and report information (known as predictive analysis) from a large pool of data. Data mining is very useful today in which information is widely available. Information obtained from data mining is used for several applications for decision making related to direct marketing, e-commerce, customer relationship management, health, oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities.

Web data mining is the automated process of extracting data from the World Wide Web. Internet has extensive data on everything that can be used effectively to make intelligent decisions. However, taking and sorting through large databases is a daunting task. Therefore, there are certain data mining tools that help to make this easier. Tools can select relevant data and interpret it as needed.

There are many types of Web data mining: mining standards, verification of data and custom mining. Web data mining products can perform various functions that are very broad, including: search engine optimization and website promotion, double transformation and marketing of modular indicators for CRM, web log reporting, tracking patterns of website visitors, visitors to calculate the conversion ratio, reported the online customer behavior, analyze click-through, providing real-time log analysis, campaign tracking, street clicks, geographical setting, with a keyword search engine, report a web visitor analysis, content analysis, extract web events such as the results of campaigns, web traffic, etc.

There are several commercially available Web data mining and web mining applications using available software. Some of them are: AlterWind Log Analyzer Professional, Amadea Web Mining, ANGOSS KnowledgeWebMiner, Azure Web Log analyzer, the Blue Martini Customer Interaction System Micro Marketing module, ClickTracks, ConversionTrack from Antssoft, Datanautics, (formerly counting), eNuggets, (real- time middleware), LiveStats from DeepMetrix, Megaputer WebAnalyst, MicroStrategy Web Traffic Analysis Module, Web Analytics NetGenesis, family NetTracker, Nihuo Web Log Analyzer, prudsys ECOMMINER, Webhound SAS, SPSS Clementine Web mining, Weblog Expert 2.0 for Windows, WebTrends, suites Data Mining web traffic information, XAffinity (TM), XML Miner, 123LogAnalyzer. There is also a free version of web software such as Data Mining: AlterWind Log Analyzer Lite, Analog (from Dr. Stephen Turner), Visitator and WUM (Web Utilization Miner.)

Reference:
[1]http://EzineArticles.com/?expert=Ross_Bainbridge

Intelligent Data Mining

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Data mining has had a checkered history mainly due to technical constrains placed by limitations of software design and architecture. Most of the algorithms used in data mining are mature and have been around for over twenty years. The next challenges in data mining are not algorithmic but software design methodologies. Commonly used data mining algorithms are freely available and processes that optimize data mining computing speed are well documented.

Most early data mining software were spun off from academia and were built around an algorithm. The inability of early data mining software to integrate to external data sources and usability issues resulted in data mining being marginalized.

The cost associated with data mining is still unnecessarily high and often not cost effective. New standards in data extraction and better software platforms holds promise that the threshold barrier to entry will be reduced.
Data access standards such as OLE-DB, XML for Analysis and JSR will minimize the challenges for data access. Building a user friendly software interfaces for the end-user are the next steps in the evolution of data mining. A comparable analogy can be made with the increasing ease of use of OLAP client tools.
The J2EE and .NET software platforms offer a large spectrum of built-in APIs that enable smarter software applications.

DAT-A : Open Source Data Mining and OLAP on MySQL

DAT-A is an open source application that is built to allow intelligent data mining. By intelligent data mining, DAT-A's software architects are creating a highly decouple application that focuses the user's attention on the data mining results and not the data extraction or data modeling process. All data exchanges are in XML and SOAP to ensure interoperability.

An enterprise version is also being planned that is built on a BEA WebLogic Server that writes to a Web Services interface.
Presently MySQL does not have built-in data mining modules. DAT-A applies a data mining abstraction layer on MySQL. The business logic for controlling the data mining model and model training is written in the J2EE framework.

For the personal edition of DAT-A, the MySQL data mining application server is contained within the business logic developed on the J2EE framework layer. In the upcoming enterprise version, the business logic and data extraction controls will be hosted on BEA's WebLogic application server.

Article Source:
[1]http://www.dwreview.com/Data_mining/Intelligent_DataMining.html

Visual Reporting and Analysis

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Data visualization is increasingly an essential element of business intelligence (BI). No longer restricted to specialized applications, data visualization in the form of charts, maps, and other graphical representations is enabling business users to better understand data and use it to achieve tactical and strategic objectives. Moreover, data visualization is prompting a cultural shift toward more analytic, data-driven business and operations by empowering users to explore, in a graphically inviting medium, data that was previously available only in tabular reports.

This TDWI Best Practices Report, which is based on a Web survey of BI professionals and interviews with BI practitioners and experts, finds that data visualization is in the middle of a remarkable growth phase. It also reveals that data visualization contributes impressively to improvements in business user insight and productivity, as well as usage of dashboards (the preferred medium for data visualization). For instance, 74% of our survey respondents credit data visualization for a “very high” or “high” increase in business user insights.

But data visualization is never a plug-and-play solution, and one size does not fit all. Dashboard design and usage can and should vary by types of users (for example, executives versus front-line staff), purpose (strategic, tactical, operational), and industry and organizational culture (a healthcare organization versus a clothing manufacturer). Customization collaboration, and iteration are required for organizations to operate interactive visual reporting and analysis solutions that deliver maximum benefits.

Article Source and Further Reading:
http://tdwi.org/research/2011/01/BPR-Q1-Visual-Reporting-and-Analysis?pc=t170tl01&utm_source=webmktg&utm_medium=Text_Link&utm_campaign=t170tl01