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Data Management, Exploration and Mining Group (DMX)
Overview
Overview
The Data Management, Exploration and Mining group focuses on solving two key problems in information management reducing the total cost of ownership of information management and enabling flexible and rich modes of interaction with stored information, while recognizing the key role the web plays in information delivery and publishing. Database management systems provide functionality that is central to developing business applications. Therefore, database management systems are increasingly being used as an important component in applications. Yet, the problem of tuning database management systems for achieving required performance is significant, and results in high cost of ownership. The goal of our research in the AutoAdmin initiative is to make database systems self-tuning and self-administering. We achieve this by enabling databases to track the usage of their systems and to gracefully adapt to application requirements. Thus, instead of applications having to track and tune databases, databases actively auto-tunes itself to be responsive to application needs. Our research has led to novel self-tuning components being included in Microsoft SQL Server. Developing rich modes of information access from data stores is the other central focus of our group. While tremendous progress has been made in data capture and storage, the technology for querying, navigating, exploring, visualizing, and summarizing large data stores is still in its infancy. Traditional approaches to data reduction and analysis break down with massive data sets. Therefore, we aim at exploiting data mining techniques, i.e., applying statistical and machine learning techniques to detect patterns in databases. Our research effort in data mining focuses on ensuring that traditional techniques are made effective over enterprise databases. In particular, traditional algorithms need to be made significantly scalable and it is also necessary to enable a seamless integration of data mining technology with Relational/OLAP database infrastructure so that database developers can exploit data mining functionality. Furthermore, we also are active in applying data mining to specific analysis scenarios, e.g., web browsing and E-tailing. Our research and collaboration with product groups has resulted in applications that will make it easy for users unfamiliar with data mining algorithms to use data mining for analysis, prediction, and reporting purposes. We work closely with Microsoft SQL Server http://www.microsoft.com/sql/ and Commerce Server http://www.microsoft.com/siteserver/commerce Finally, we are also pursuing research on data exploration that identifies techniques for flexible ways to query and browse data. Specifically, our goal is to support approximate matches and ranked search in the database context. We also like to enable data browsing and querying service that can interoperate between text, structured, and semi-structured (e.g., mail messages) data wrapped in XML. Data mining can play a central role in such a service by enabling data reduction. People
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