Srinivasan Sundara Rajan

What Is Content Classification The term content classification is best understood in an enterprise information context, defined by the following concepts. Taxonomy is the hierarchical representation of topics of interest. For example, a basic taxonomy might consist of a class called "Transport," which might have subclasses "Air Transport" and "Land Transport." Then "Land Transport" might in turn have subclasses "Bus" and "Car." This hierarchy means that a "Car" is a type of "Land Transport," and is also a type of "Transport." Ontology defines the relationships between the topics of interest. Content classification is the process of analyzing a document and adding metadata 'tags' that describe that document that is sourced from a taxonomy or other form of controlled vocabulary. Content Classification in Enterprises Today's enterprises deal with data in which 80% is un... (more)

iPad3 vs Windows 8 - and the Winner Is...Cloud

As evident from various social media interactions, blogs, tech news sites and other Google searches, the two events that clearly brought the attention of every one happened in quick succession: iPad 3: The much-awaited next version of the popular tablet, while the official date is March 7, already facts and rumors about the likely features and look and feel have flooded the various technical sites. Windows 8: Close on its heels, the Windows 8 consumer preview that happened this week also hit the attention of the technical community. The biggest talking point of Windows 8 is its ... (more)

Data Mining and Data Virtualization

Data Mining helps organizations to discover new insights from existing data, so that predictive techniques can be applied towards various business needs. The following are the typical characteristics of data mining. Extends Business Intelligence, beyond Query, Reporting and OLAP (Online Analytical Processing) Data Mining is cornerstone for assessing the customer risk, market segmentation and prediction Data Mining is about performing computationally complex analysis techniques on very large volumes of data It combines the analysis of historical data with modeling techniques toward... (more)

Information Delivery 2.0 - Reference Architecture

Information Delivery 1.0 Issues With the enablement of new sources of data flow into the enterprise, it is time to look at the issues of Information Delivery 1.0 of the current enterprises. Disparate Data Sources, most enterprises have grown multiple database platforms and even within a platform, multiple databases for various reasons. Enterprises taking a lot of pain and effort on ETL towards synchronizing the data. Enterprises are slowly incorporating big data, unstructured data in their information delivery scope, but don't have clear means to integrate them. Rich Media conte... (more)

End-to-End Cloud Offerings for Large Enterprises

With cloud adoption becoming a de-facto option for small and medium enterprises, large enterprises are relatively slower in their adoption of cloud. The main reason is that large enterprises have a very complex existing IT setup and no single offering from various cloud providers has yet to satisfy all their needs. However we find the recent announcements and offerings from IBM provide a perfect platform for large enterprises to on board to Cloud to make their businesses more agile. Blueprint of Large Enterprises on Cloud-Enabled IT The following reference architecture provides a... (more)