The standard approach to integrating heterogeneous information sources is to build a global schema that relates all of the information in the different sources, and to pose queries directly against it. The problem is that schema integration is usually difficult, and as soon as any of the information sources change or a new source is added, the process mayhave to be repeated. The SIMS system uses an alternative approach. A domain model of the application domain is created, establishing a fixed vocabulary for describing data sets in the domain. Using this language, each available information source is described. Queries to SIMS against the collection of available information sources are posed using terms from the domain model, and reformulation operators are employed to dynamically select an appropriate set of information sources and to determine how to integrate the available information to satisfy a query. This approach results in a system that is more flexible than existing ones, more easily scalable, and able to respond dynamically to newly available or unexpectedly missing information sources.

With the vast number of information resources available today, a critical problem is how to locatc retrieve and process information. It is impracticaJ to build a single unified system that combines all of these information resources. A more modular approach is to build specialized information agents where each agent provides access to a subset of these resources and can serve as an information source to other agents. In this paper wc present the architecture of the individual information agents and describe how this architecture supports a network of cooperating information agents. Wc describe how these information agents represent their knowlcdgc communicate with other agcnts dynamicaJly construct information rctricvaJ plans and learn about other agents to improve their accuracy and efficiency. Wc have aJrcady built a smaJl network of agents that have these capabilities and provide access to information for logistics planning.

New applications of information systems, such as electronic commerce and healthcare information systems, need to integrate a large number of heterogeneous databases over computer networks. Answering a query in these applications usually involves selecting relevant information sources and generating a query plan to combine the data automatically. As significant progress has been made in source selection and plan generation, the critical issue has been shifting to query optimization. This paper presents a semantic query optimization (SQO) approach to optimizing query plans of heterogeneous multidatabase systems. This approachprovides global optimization for query plans as well as local optimization for subqueries that retrieve data from individual database sources. An important feature of our local optimization algorithm is that weprove necessary and sufficient conditions to eliminate an unnecessary join in a conjunctive query of arbitrary join topology. This feature allows our...

We investigate classes of SQL queries which are syntactically correct, but certainly not intended, no matter for which task the query was written. For instance, queries that are contradictory, i.e. always return the empty set, are obviously not intended. However, current database management systems execute such queries without any warning. In this paper, we give an extensive list of conditions that are strong indications of semantic errors. Of course, questions like the satisfiability are in general undecidable, but a significant subset of SQL queries can actually be checked. We believe that future database management systems will perform such checks and that the generated warnings will help to develop code with fewer bugs in less time. 1.

Query optimizers nowadays draw upon many sources of information about the database to optimize queries. They employ runtime statistics in cost-based estimation of query plans. They employ integrity constraints in the query rewrite process. Primary and foreign key constraints have long played a role in the optimizer, both for rewrite opportunities and for providing more accurate cost predictions. More recently, other types of integrity constraints are being exploited by optimizers in commercial systems, for which certain semantic query optimization techniques have now been implemented.

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...

Many applications of knowledge discovery require the knowledge to be consistent with data. Examples include discovering rules for query optimization, database integration, decision support, etc. However, databases usually change over time and make machine-discovered knowledge inconsistent with data. Useful knowledge should be robust against database changes so that it is unlikely to become inconsistent after database changes. This paper defines this notion of robustness, describes how to estimate the robustness of Hornclause rules in closed-world databases, and describes how the robustness estimation can be applied in rule discovery systems. Introduction Databases are evolving entities. Knowledge discovered from one database state may become invalid or inconsistent with a new database state. Manyapplications require discovered knowledge to be consistent with the data. Examples are the problem of learning for database query optimization, database integration, knowledge d...

Many applications of knowledge discovery and data mining such as rule discovery for semantic query optimization, database integration and decision support, require the knowledge to be consistent with data. However, databases usually change over time and makemachine-discovered knowledge inconsistent. Useful knowledge should be robust against database changessothatitisunlikely to become inconsistentafter database changes. This paper defines this notion of robustness in the context of relational databases that contain multiple relations and describes how robustness of first-order Horn-clause rules can be estimated and applied in knowledge discovery.Our experiments show that the estimation approach can accurately predict the robustness of a rule.

The traditional role of integrity constraints is to protect the integrity of data. But integrity constraints can and do play other roles in databases; for example, they can be used for query optimization. In this role, they do not need to model the domain; it is sufficient that they describe regularities that are true about the data currently stored in a database. In this paper we describe two algorithms for finding such regularities (in the syntactic form of check constraints) and discuss some of their applications in DB2.

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 : : : : : : : : : : : : : : : : : : : ...