With the vast number of information resources available today, a critical problem is how to locate, retrieve and process information. It would be impractical to build a single unified system that combines all of these information resources. A more promising approach is to build specialized information retrieval agents that provide access to a subset of the information resources and can send requests to other information retrieval agents when appropriate. In this paper we present the architecture of the individual information retrieval agents and describe how this architecture supports a network of cooperating information agents. We describe how these information agents represent their knowledge, communicate with other agents, dynamically construct information retrieval plans, and learn about other agents to improve efficiency. We have already built a small network of agents that have these capabilities and provide access to information for transportation planning.

Semantic query optimization can dramatically speed up database query answering byknowledge intensive reformulation. But the problem of how to learn the required semantic rules has not been previously solved. This chapter presents a learning approach to solving this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and attributes to construct rules from a database with many relations. The learned semantic rules are effective for optimization because they will match query patterns and reflect data regularities. Experimental results show that this approach learns sufficient rules for optimization that produces a substantial cost reduction.

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...

For semantic query optimization one needs detailed knowledge about the contents of the database. Traditional techniques use static knowledge about all possible states of the database which is already given. New techniques use knowledge only about the current state of the database which can be found by methods of knowledge discovery in databases. Databases are often very large and permanently in use. Therefore, methods of knowledge discovery are only allowed to take a small amount of the capacity of the database system. So database access has to be reduced to a minimum during the process of discovery and obviously if the database changes during the process of maintenance of the discovered knowledge. In this paper, the main effort has been put into minimizing the number of database accesses, w.r.t. discovery and maintenance. This is exemplified by the discovery of functional dependencies. We improve the inference of functional dependencies by using independencies and cardinality dependen...

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.

This paper introduces a new measurement, robustness, to measure the qualityofmachine-discovered knowledge from real-world databases that change over time. A piece of knowledge is robust if it is unlikely to become inconsistent with new database states. Robustness is different from predictive accuracy in that by the latter, the system considers only the consistency of a rule with unseen data, while by the former, the consistency after deletions and updates of existing data is also considered. Combining robustness with other utility measurements, a system can makeintelligent decisions in learning and maintenance of knowledge learned from changing databases. This paper defines robustness, then presents an estimation approach for the robustness of Horn-clause rules learned from a relational database. The estimation approach applies the Laplace law of succession, which can be efficiently computed. The estimation is based on database schemas and transaction logs. No domains...

A critical problem in building an information mediator is how to translate a domain-level queries into an efficient query plan for accessing the required data. We have built a flexible and efficient information mediator, called SIMS. SIMS takes a domain-level query and dynamically selects the appropriate information sources based on their content and availability, generates a query access plan that specifies the operations and their order for processing the data, and then performs semantic query reformulation to minimize the overall execution time. This paper describes these three basic components of the query processing in SIMS. 1 Introduction SIMS [ Arens et al., 1993 ] is an information retrieval system that provides an intelligent mediator between information sources and humans users or applications programs. Queries are expressed in a uniform language, independent of the distribution of information over sources, of the various query languages, the location of sources, etc. SIMS d...

The aim of query optimization is to produce an equivalent query which is less expensive to process than the original query. Semantic query optimization involves the use of semantic knowledge during the optimization process. The success strongly depends on the availability of this knowledge. We present a rule--based approach of semantic query optimization which overcomes the limitation of predefined knowledge by discovering constraints. We show that unnecessary distinct--keywords and group by--parts of an SQL query can be detected and removed with the use of discovered constraints. 1 Introduction Semantic query optimization (SQO) provides the same kind of transparency with respect to semantic knowledge like relational optimizers do with respect to physical representation. Since semantically equivalent queries can differ significantly in their evaluation costs, it is our major goal to free the user from finding the most effective query. Finding semantically equivalent queries requires s...

xi 1 Introduction 1 1.1 Semantic Query Optimization : : : : : : : : : : : : : : : : : : : : : : 3 1.2 High Utility Semantic Knowledge for SQO : : : : : : : : : : : : : : : 6 1.3 Learning Effective and Robust Rules : : : : : : : : : : : : : : : : : : 8 1.4 Closely Related Work : : : : : : : : : : : : : : : : : : : : : : : : : : : 10 1.5 Summary of Contributions : : : : : : : : : : : : : : : : : : : : : : : : 12 1.6 Organization of the Dissertation : : : : : : : : : : : : : : : : : : : : : 13 2 Robustness of Knowledge 15 2.1 Consistency of Rules and Database Changes : : : : : : : : : : : : : : 15 2.2 Definitions of Robustness : : : : : : : : : : : : : : : : : : : : : : : : : 18 2.3 Estimating Robustness : : : : : : : : : : : : : : : : : : : : : : : : : : 19 2.4 Templates for Estimating Robustness : : : : : : : : : : : : : : : : : : 26 2.5 Empirical Demonstration : : : : : : : : : : : : : : : : : : : : : : : : : 27 2.6 Related Uncertainty Measures : : : : : : : : : : : : : : : : : : : ...

Abstract- This study focuses on query based learning in multiagent systems which include both data management operations and coordination activities. The study is oriented on agent based and database systems with model driven approach (MDA) which provides arrangement of data within a multi-agent system by letting filter with query based learning which supports the decision mechanism within the system. It uses the similarity measure on maximum entropy approach, which is often to find out interesting and meaningful patterns from databases. At the same time, it may generate a variety of rules, such as classification rules, throughout to learning rules of the query based learning process. Keywords: Multi-agent system, Query based learning, Model driven approach, Entropy