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Decision support systems form the core of business IT infrastructures because they let companies translate business information into tangible and lucrative results. Collecting, maintaining, and analyzing large amounts of data, however, involves expensive technical challenges that require organizational commitment. Many commercial tools are available for each of the three major data warehousing tasks: populating the data warehouse from independent operational databases, storing and managing the data, and analyzing the data to make intelligent business decisions. Data cleaning relates to heterogeneous data integration, a problem studied for many years. More work must be done to develop domain-independent tools that solve the data cleaning problems associated with data warehouse development. Most data mining research has focused on developing algorithms for building more accurate models or building models faster. However, data preparation and mining model deployment present several engaging problems that relate specifically to achieving better synergy between database systems and data mining technology.

Established companies have had decades to accumulate masses of data about their customers, suppliers, products and services, and employees. Data mining, also known as knowledge discovery in databases, gives organizations the tools to sift through these vast data stores to find the trends, patterns, and correlations that can guide strategic decision making. Traditionally, algorithms for data analysis assume that the input data contains relatively few records. Current databases however, are much too large to be held in main memory. To be efficient, the data mining techniques applied to very large databases must be highly scalable. An algorithm is said to be scalable if (given a fixed amount of main memory), its runtime increases linearly with the number of records in the input database. Recent work has focused on scaling data mining algorithms to very large data sets. The authors describe a broad range of algorithms that address three classical data mining problems: market basket analysis, clustering, and classification.

Data analysis is fundamentally an iterative process in which you issue a query, receive a response, formulate the next query based on the response, and repeat. You usually don't issue a single, perfectly chosen query and get the information you want from a database; indeed, the purpose of data analysis is to extract unknown information, and in most situations, there is no one perfect query. People naturally start by asking broad, big-picture questions and then continually refine their questions based on feedback and domain knowledge. In the Control (Continuous Output and Navigation Technology with Refinement Online) project at the University of California, Berkeley, the authors are working with collaborators at IBM, Informix, and elsewhere to explore ways to improve human-computer interaction during data analysis. The Control project's goal is to develop interactive, intuitive techniques for analyzing massive data sets.

Windows NT 5.0 introduces fundamental changes to storage management. The new infrastructure in NT 5.0 can adapt to future changes in storage devices and media, brings a more powerful set of management mechanisms, and supports more base services within the system. Most of the changes are transparent to legacy applications and to users, but software producers who want to deploy advanced functionality should realize increased benefits from the underlying infrastructure. Windows NT now supports the dynamic plug-and-play of storage devices and the growth and mounting of storage volumes. It also frees the NT system from drive letter limitations and incorporates several new file system features to aid the development of enterprise-class storage management solutions. The article describes two new mechanisms present in Windows NT 5.0, the reliable change journal and the reparse points. It also presents storage management infrastructure subsystems and services that help administer storage. Both mechanisms are designed to facilitate the robust and efficient administration of an increasing volume of data whose lifetime grows over time.

Computers can now store all forms of information: records, documents, images, sound recordings, videos, scientific data, and many new data formats. Society has made great strides in capturing, storing, managing, analyzing, and visualizing this data. These tasks are generically called data management. This article sketches the evolution of data management systems. There have been six distinct phases in data management. Initially, data was manually processed. The next step used punched-card equipment and electromechanical machines to sort and tabulate millions of records. The third phase stored data on magnetic tape and used stored-program computers to perform batch processing on sequential files. The fourth phase introduced the concept of a database schema and on-line navigational access to the data. The fifth step automated access to relational databases and added distributed and client server processing. We are now in the early stages of sixth-generation systems that store richer data types, notably documents, images, voice, and video data. These sixth-generation systems are the storage engines for the emerging Internet and intranets. Early data management systems automated traditional information processing. Today they allow fast, reliable, and secure access to globally distributed data. Tomorrow's systems will access and summarize richer forms of data. It is argued that multimedia databases will be a cornerstone of cyberspace.

Pegasus, a heterogeneous multidatabase management system that responds to the need for effective access and management of shared data across in a wide range of applications, is described. Pegasus provides facilities for multidatabase applications to access and manipulate multipole autonomous heterogeneous distributed object-oriented relational, and other information systems through a uniform interface. It is a complete data management system that integrates various native and local databases. Pegasus takes advantage of object-oriented data modeling and programming capabilities. It uses both type and function abstractions to deal with mapping and integration problems. Function implementation can be defined in an underlying database language or a programming language. Data abstraction and encapsulation facilities in the Pegasus object model provide an extensible framework for dealing with various kinds of heterogeneities in the traditional database systems and nontraditional data sources. >

Database management systems evolved in response to the need for efficient maintenance of increasingly large amounts of data. The relatively slow speed of secondary storage devices holding the data is one of the main limitations of database design. Hence, the internal organization of databases has been the primary focus of research in the past. Another major influence on database research was the need to share information among a variety of users. In such an environment, strict rules governing the manipulation of data had to be imposed to preserve the integrity of the database and to guarantee privacy for each user. These needs led to the development of several basic data models. With the steadily increasing demand for user-oriented systems, however, new trends in database technology have evolved outside of the scope of the traditional data models. In this article the authors are concerned with two closely related efforts: (1) The incorporation of more semantic modeling capabilities into database models. (2) The development of better user environments, which include user-friendly interfaces and support different user views of the content and organization of the data.