In this paper we motivate and describe materialized views, their applications, and the problems and techniques for their maintenance. We present a taxonomy of view maintenanceproblems basedupon the class of views considered, upon the resources used to maintain the view, upon the types of modi#cations to the base data that areconsidered during maintenance, and whether the technique works for all instances of databases and modi#cations. We describe some of the view maintenancetechniques proposed in the literature in terms of our taxonomy. Finally, we consider new and promising application domains that are likely to drive work in materialized views and view maintenance. 1 Introduction What is a view? A view is a derived relation de#ned in terms of base #stored# relations. A view thus de#nes a function from a set of base tables to a derived table; this function is typically recomputed every time the view is referenced. What is a materialized view? A view can be materialized by storin...

. It is widely recognized that production rules in database systems can be used to automatically maintain derived data such as views. However, writing a correct set of rules for efficiently maintaining a given view can be a difficult and ad-hoc process. We provide a facility whereby a user defines a view as an SQL select expression, from which the system automatically derives set-oriented production rules that maintain a materialization of that view. The maintenance rules are triggered by operations on the view's base tables. Generally, the rules perform incremental maintenance: the materialized view is modified according to the sets of changes made to the base tables, which are accessible through logical tables provided by the rule language. However, for some operations substantial recomputation may be required. We give algorithms that, based on key information, perform syntactic analysis on a view definition to determine when efficient maintenance is possible. 1 Introduction In rel...

We propose incorporating a production rules facility into a relational database system. Such a facility allows definition of database operations that are automatically executed whenever certain conditions are met. In keeping with the set-oriented approach of relational data manipulation languages, our production rules are also set-oriented---they are triggered by sets of changes to the database and may perform sets of changes. The condition and action parts of our production rules may refer to the current state of the database as well as to the sets of changes triggering the rules. We define a syntax for production rule definition as an extension to SQL. A model of system behavior is used to give an exact semantics for production rule execution, taking into account externally-generated operations, selftriggering rules, and simultaneous triggering of multiple rules. 1 Introduction Recently, there has been considerable interest in integrating production rules systems and database manag...

. This paper describes the implementation of a set-oriented database production rule language proposed in earlier papers. Our implementation uses the extensibility features of the Starburst database system, and rule execution is fully integrated into database query and transaction processing. 1 Introduction In database systems, a production rules facility allows definition of database operations that are executed automatically whenever certain events occur or conditions are met. Production rules in database systems can be used for enforcing integrity constraints, maintaining derived data, and building efficient knowledge-base and expert systems. In [WF89,WF90] we propose a syntax for specifying production rules in relational database systems and a semantics for rule execution. In keeping with the setoriented approach of relational data manipulation languages, our production rules are set-oriented---they are triggered by sets of changes to the database and may perform sets of changes....

We present a system that accepts a specification of key data structure constraints, then dynamically detects and repairs violations of these constraints. Our experience using our system indicates that the specifications are relatively easy to develop once one understands the data structures. Furthermore, for our set of benchmark applications, our system can e#ectively repair errors to deliver consistent data structures that allow the program to continue to operate successfully within its designed operating envelope.

Abstract. We show that production rules and persis-tent queues together provide a convenient mechanism for maintaining consistency in semantically heterogeneous multi-database environments. We describe a specification language and methods for automatically deriving production rules that maintain (1) existence dependencies, in which the presence of data in one database implies the presence of related data in another, and (2) value dependencies, in which the value of data in one database is baaed on the value of related data in another. The production rules derived from dependency specifications use persistent queues to monitor and maintain the dependencies automatically, asynchronously, incremen-tally, and correctly. 1

. Static analysis methods are given for determining whether arbitrary sets of database production rules are (1) guaranteed to terminate

Integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, alerters, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This chapter summarizes current work in active database systems; topics covered include active database rule models and languages, rule execution semantics, and implementation issues. 1 Introduction Conventional database systems are passive: they only execute queries or transactions explicitly submitted by a user or an application program. For many applications, however, it is important to monitor situations of interest, and to ...

Database researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues. 1 Introduction Database systems provide persistent storage for massive amounts of data and powerful interfaces for querying and modifying this data. Even so, most database systems are passive, si...

This paper gives methods for statically analyzing sets of active database rules to determine if the rules are (1) guaranteed to terminate, (2) guaranteed to produce a unique final database state, and (3) guaranteed to produce a unique stream of observable actions. If the analysis determines that one of these properties is not guaranteed, it isolates the rules responsible for the problem and determines criteria that, if satisfied, guarantee the property. The analysis methods are presented in the context of the Starburst Rule System. 1 Introduction Rules in active database systems allow specification of data manipulation operations that are executed automatically whenever certain events occur or conditions are met [DHW94,DW92,HW93]. Active database rules provide a general and powerful mechanism for many database features, including integrity constraint enforcement, derived data maintenance, triggers, alerters, authorization checking, and versioning. In addition, active database sy...