The automated discovery of knowledge in databases is becoming increasingly important as the world's wealth of data continues to grow exponentially. Knowledge-discovery systems face challenging problems from real-world databases which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. This paper addresses these problems and describes some techniques for handling them. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research.

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.

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...

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...

Abstract. In advanced XML transformer tools, XSLT rules are generated automatically after relating simple source and target XML documents. In this paper, we generalize this approach for the design of model transformations: transformation rules are derived semi-automatically from an initial prototypical set of interrelated source and target models. These initial model pairs describe critical cases of the model transformation problem in a purely declarative way. The derived transformation rules can be refined later by adding further source-target model pairs. The main advantage of the approach is that transformation designers do not need to learn a new model transformation language, instead they only use the concepts of the source and target modeling languages.

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.

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.

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.