Advances in information technology, the explosive use of the internet and data collection methods have led to an ever-increasing volume of data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines.

There are several key issues that need to be addressed by innovative approaches to find trends in and correlations between the underlying large scale of data  – some of which are listed below.

• Pattern recognition, statistics, and high performance computing to extract interesting and previously unknown information which are critical to the business from large scale data.
• Extracting meaningful content from text continues to be an important problem for information processing and management.
• Techniques to automatically cluster or categorize documents from diverse data sources.
• Providing robust and reliable infrastructure within the budgetary constraints.

Data Engineering

The key aspects of data engineering are: 
(1) Identifying valuable data patterns, 
(2) conducting effective data analysis, and 
(3) deriving useful information from meaningful sets of data.

We recognize the need of the business community for  discovering intelligent and useful information hidden in the myriad of data stores. Understanding these hidden critical information at the right time can dramatically alter business decisions.

Focus Areas

Our focus is on developing algorithms and applications for high performance distributed data mining and predictive modeling in the following areas:

• Fraud Detection.
• Integration: Mining, Warehousing and OLAP.
• Query/Constraint-based Data Mining.
• Probabilistic/Statistical Methods.
• Mining Spatial and Temporal Data.
• Trend and Periodicity Analysis.
• Parallel/Distributed/Agent Techniques.
• Data Reduction/Pre-processing.
• Post-processing.
• Collaborative Filtering/Personalization.
• Web Analytics.
• Text Mining.