Data Mining

Web 2.0 has become an essential tool for the marketplace. Enabling real time feedback on products, services, etc. So much so a new job description under computer science was created to assist in analyzing the information captured via 2.0. The field of data mining. (Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD),[1] a relatively young and interdisciplinary field of computer science,[2]HYPERLINK \l "cite_note-brittanica-2"[3] is the process that attempts to discover patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[2] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[2] Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[2] (wikipedia). )

A skill that is becoming so vital to a companies Marketing, Public Relations, and Advertisement. Customer Intelligence enables marketers to rapidly explore detailed profile data including purchase behaviour and responsiveness to past marketing campaigns. Millions of records can be analyzed. Successful marketing starts with the right data because at the end of every subscriber, follower and fan is a person with their own preferences. Data Mining has made it possible to in a sense capture the thoughts and emotions of the consumers. Monitoring traffic as well .Social media systems such as blogs, photo and link sharing sites, wikis and on-line forums are estimated to produce up to one third of new Web content.

One thing that sets these "Web 2.0" sites apart from traditional Web pages and resources is that they are intertwined with other forms of networked data. Their standard hyperlinks are enriched by social networks, comments, trackbacks, advertisements, tags, RDF data and metadata. Recently work has been done on building systems that analyse these emerging social media systems to recognize spam blogs, find opinions on topics, identify communities of interest, derive trust relationships, and detect influential bloggers. This is clearly evident on sites such as www.Twitter.com a mini blogging site where your web influence is clearly tracked by what is trending.

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www.twitter.com

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