With the current explosion of data, retrieving and integrating information from various sources is a critical problem. Work in multidatabase systems has begun to address this problem, but it has primarily focused on methods for communicating between databases and requires significant effort for each new database added to the system. This paper describes a more general approach that exploits a semantic model of a problem domain to integrate the information from various information sources. The information sources handled include both databases and knowledge bases, and other information sources (e.g., programs) could potentially be incorporated into the system. This paper describes how both the domain and the information sources are modeled, shows how a query at the domain level is mapped into a set of queries to individual information sources, and presents algorithms for automatically improving the efficiency of queries using knowledge about both the domain and the informat...

With vast amounts of information available from various sources, integrating data from multiple databases is an important problem. The SIMS project attacks this problem using a variety of Artificial Intelligence techniques, including planning, knowledge representation, problem reformulation, and learning. To integrate multiple databases, the user provides a semantic model of the application domain and then uses this model to describe the contents of the available databases. Given a query, the system uses a planner to decide which databases must be queried and in what order the queries should be executed. This paper focuses on the query planning problem --- the selection of appropriate data sources and ordering the accesses to them, and on the reformulation of queries --- the use of knowledge both about the domain and the databases to modify queries to make the retrieval plans for them more efficient. 1 Introduction Most tasks performed by users of complex information systems involve...

A practical heterogeneous, distributed multidatabase system must answer queries efficiently. Conventional query optimization techniques are not adequate here because these techniques are dependent on the database structure, and rely on limited information which is not sufficient in complicated multidatabase queries. This paper presents an automated approach to reformulating query plans to improve the efficiency of multidatabase queries. This approach uses database abstractions, the knowledge about the contents of databases, to reformulate a query plan into less expensive but semantically equivalent one. We present two algorithms. The first algorithm reformulates subqueries to individual databases, the second algorithm extends the first one and reformulates the entire query plan. Empirical results show that the reformulations can provide significant savings with minimal overhead. The reformulation approach provides a global reduction in the amount of the intermediate data as well as local opt...

Semantic query optimization can dramatically speed up database query answering by knowledge intensive reformulation. But the problem of how to learn required semantic rules has not previously been solved. This paper describes an approach using an inductive learning algorithm to solve the problem. In our approach, learning is triggered by user queries and then the system induces semantic rules from the information in databases. The inductive learning algorithm used in this approach can select an appropriate set of relevant attributes from a potentially huge number of attributes in real-world databases. Experimental results demonstrate that this approach can learn sufficient background knowledge to reformulate queries and provide a 57 percent average performance improvement. 1 INTRODUCTION Speeding up a system's performance is one of the major goals of machine learning. Explanation-based learning is typically used for speedup learning, while applications of inductive learning are usual...

The query reformulation approach (also called semantic query optimization) takes advantage of the semantic knowledge about the contents of databases for optimization. The basic idea is to use the knowledge to reformulate a query into a less expensive yet equivalent query. Previous work on semantic query optimization has shown the cost reduction that can be achieved by reformulation, we further point out that when applied to distributed multidatabase queries, the reformulation approach can reduce the cost of moving intermediate data from one site to another. However, a robust and efficient method to discover the required knowledge has not yet been developed. This paper presents an example-guided, data-driven learning approach to acquire the knowledge needed in reformulation. We use example queries to guide the learning to capture the database usage pattern. In contrast to the heuristic-driven approach proposed by Siegel, the data-driven approach is more likely to learn the re...

Abstract: Semantic query optimization uses semantic knowledge to transform a query into another form that can be executed in a more efficient manner but still yields the same result as the original query. The semantic knowledge can be supplied by users or derived by the system.. In this paper, we describe the ARDOR semantic query optimizer with automatic rule derivation capabilities which, in recent field trials, has demonstrated significant reductions in query execution time. 1.

Traditionally, the emphasis on the development of logical systems for databases has been to provide a system that is both sound and complete and with definable expressiveness. This article discusses the nature and the role of logics which relax the soundness criteria in order to enhance both the expressiveness and the implemented performance of a system based on it. It will be argued that systems of co-operating complete and overcomplete logics may be constructed that together provide a sound, complete and highly expressive logic with optimum implemented performance for summary databases and that such a system may be used to improve the response of conventional relational databases.

The development of a number of new database related technologies has required various middleware extensions to relational and object-relational systems, such as the accommodation of domain based hierarchies. Although not stated explicitly and sometimes without realising it, these technologies also suggest that there needs to be a re-examination of some of the long-held precepts of database design such as the closed world assumption and the soundness of database results for some applications in some circumstances. This paper discusses some of these issues and suggests that a revision to the base capabilities of relational DBMS would circumvent the need for (or at least reduce the complexity of) much of this middleware and provide a more appropriate platform on which to develop future technologies, such as OLAP and Knowledge Discovery Systems. Keywords Inductive Databases, Data Mining, Knowledge Discovery, OLAP, Multimedia ...

In this paper we consider two related types of data dependencies that can hold in a relation: conjunctive implication rules between attribute-value pairs, and functional dependencies. We present a conceptual clustering approach that can be used, with some small modifications, for inferring a cover for both types of dependencies. The approach consists of two steps. First, a particular clustered representation of the relation, called concept (or Galois) lattice is built; then, a cover is extracted from the lattice built in the earlier step. The main emphasis of this paper is on the second step. We study the computational complexity of the proposed approach and present an experimental comparison with other methods that confirms its validity. The results of the experiments show that our algorithm for extracting implication rules from concept lattices clearly outperforms an earlier algorithm, and suggest that the overall lattice-based approach to inferring functional dependencies from relations can be seen as an alternative to traditional methods.

A software module for extracting rule schemas from rules, in the context of an intelligent learning data base system (ILDB), is described. The ILDB system employs a two level knowledge representation scheme, comprising rule schemas and rule bodies. This format allows particularly efficient deduction to be performed. An interactive tool may be used to capture knowledge in this format. Alternatively, a machine learning component may be used to induce rules only. Without the corresponding schemas, the induced rules cannot be used efficiently by the deduction engine. The extraction system described in this paper overcomes this weakness. The technique has been tested on well-known data sets. 1.