Panel on Next Generation Database Systems Won’t Work Without Semantics!

Abstract

In the late ‘70s, while second generation DBMS products and technologies were entering the market, there was significant research activity whose aim was to make greater use of semantic information in database systems. The focus of that research was primarily on semantic data models and data modelling, including semantic query processing and integrity checking. However, few if any of the results of these efforts found their way in database technologies of the day, or database management practices. Instead, semantic issues were delegated to early phases of information system development (including requirements analysis and design), as well as application development. Today's database system technologies perform admirably well with semantically trivial operations and representations. At the same time, these technologies are being challenged in virtually every area of data management, with new applications which demand ways of dealing more explicitly with the meaning of the data being managed. For example, interoperation between applications requires that the underlying databases interoperate meaningfully. This currently requires a mammoth manual reverse engineering effort that simply cannot be sustained or funded by any large organization. The same applies to data warehouses, since they too require the correct semantic merging of data from semantically diverse sources. Errors in merging these sources can lead to significant problems of interpretation and potentially of the functions that the warehouse is designed to deliver. The effectiveness of database technologies to webrelated information gathering and management applications is likewise limited by the degree to which they can accommodate semantics of the information being sought. Along the same lines, the emergence of organizational knowledge management as the next major application of computing in organizations. © 1998, ACM. All rights reserved.