The HiPAC project: combining active databases and timing constraints

Abstract

The HiPAC (High Performance ACtive database system) project addresses two critical problems in time-constrained data management: the handling of timing constraints in databases, and the avoidance of wasteful polling through the use of situation-action rules that are an integral part of the database and are monitored by DBMS's condition monitor. A rich knowledge model provides the necessary primitives for definition of timing constraints, situation-action rules, and precipitating events. The execution model allows various coupling modes between transactions, situation evaluations and actions, and provides the framework for correct concurrent execution of transactions and triggered actions. Different approaches to scheduling of time-constrained tasks and transactions are explored and an architecture is being designed with special emphasis on the interaction of the time-constrained, active DBMS and the operating system. Performance models are developed to evaluate the various design alternatives.

A cost driven disk scheduling algorithm for multimedia object retrieval

“…A few studies investigated scheduling issues in the Database Management System (DBMS) such as the HiPAC project [8], Carey et al [6]. The prior addressed how to support timing constraint in databases but did not consider the priority, cost and disk scheduling issues.…”

RBE: A Rule-by-example Active Database System

“…Ariel rules can have conditions based on a mix of patterns, events and transitions, where a transition in Ariel is defined as the changes of the database. HiPAC 15,16,17 uses the relational model for the overall framework and the nested transaction model as the framework for the execution of rules. Each rule in HiPAC is structured according to the event-condition-action paradigm.…”

“…The HiPAC system applies the following techniques in the rule manager: 16 (1) multiple condition optimization, (2) materialization and maintenance of intermediate results, (3) identification of readily ignorable events, (4) incremental evaluation, and (5) using knowledge of the action parts of rules. Central to the concept of condition monitoring is the graph abstraction.…”

“…Over the last decade, many researchers have proposed active database systems, 6,7,8 , such as POSTGRES, 9,10,11,12,13,14 , HiPAC, 15,16,17 , Ariel 18,19 and Starburst. 20,21 The POSTGRES system is a next-generation extensible database management system that can support abstract data types, procedure types and rule types, and business data types, for data, object and knowledge management.…”

Ode Triggers: Monitoring the Stock Market

“…More formally, event expression A & (predicate) is satisfied if predicate holds immediately after the event denoted by A is detected. Although masks can be used like conditions in the ECA model, 7,8,9 masks are more general since multiple masks may be evaluated over several transactions before a trigger's composite event is recognized. In the ECA model, each trigger detects a (composite) event that contains no conditions and then evaluates at most one condition.…”