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Enterprise executives understand that timely, accurate knowledge can mean improved business performance. Two technologies have been central in improving the quantitative and qualitative value of the knowledge available to decision makers: business intelligence and knowledge management. Business intelligence has applied the functionality, scalability, and reliability of modern database management systems to build ever-larger data warehouses, and to utilize data mining techniques to extract business advantage from the vast amount of available enterprise data. Knowledge management technologies, while less mature than business intelligence technologies, are now capable of combining today's content management systems and the Web with vastly improved searching and text mining capabilities to derive more value from the explosion of textual information. We believe that these systems will blend over time, borrowing techniques from each other and inspiring new approaches that can analyze data and text together, seamlessly. We call this blended technology BIKM. In this paper, we describe some of the current business problems that require analysis of both text and data, and some of the technical challenges posed by these problems. We describe a particular approach based on an OLAP (on-line analytical processing) model enhanced with text analysis, and describe two tools that we have developed to explore this approach--eClassifier performs text analysis, and Sapient integrates data and text through an OLAP-style interaction model. Finally, we discuss some new research that we are pursuing to enhance this approach.

We describe the design and implementation of QBISM (Query By Interactive, Spatial Multimedia), a prototype for querying and visualizing 3D spatial data. Our medical image application is focused on the brain mapping requirements for multimodality relationships across multiple subjects. It incorporates data describing both structure and function. It includes data structures that describe anatomy, physiology, coordinates using rendered imagery and statistical output. The system is built on top of the Starburst DBMS extended to handle spatial data types, specifically, scalar fields and arbitrary regions of space within such fields. In this paper we list the requirements of the application, discuss the logical and physical database design issues, and present timing results from our prototype. We observed that the DBMS' early spatial filtering results in significant performance savings because the system response time is dominated by the amount of data retrieved, transmitted, and rendered.

We describe Garlic, an object-oriented multimedia middleware query system. Garlic enables existing data management components, such as a relational database or a full text search engine, to be integrated into an extensible information management system that presents a common interface and user access tools. We focus in this paper on how QBIC, an image retrieval system that provides content-based image queries, can be integrated into Garlic. This results in a system in which a single query can combine visual and nonvisual data using type-specific search techniques, enabling a new breed of multimedia applications