We propose a new client-side data-caching scheme for relational databases with a central server and multiple clients. Data are loaded into each client cache based on queries executed on the central database at the server. These queries are used to form predicates that describe the cache contents. A subsequent query at the client may be satisfied in its local cache if we can determine that the query result is entirely contained in the cache. This issue is called cache completeness. A separate issue, cache currency, deals with the effect on client caches of updates committed at the central database. We examine the various performance tradeoffs and optimization issues involved in addressing the questions of cache currency and completeness using predicate descriptions and suggest solutions that promote good dynamic behavior. Lower query-response times, reduced message traffic, higher server throughput, and better scalability are some of the expected benefits of our approach over commonly used relational server-side and object ID-based or page-based client-side caching.

/pdf/a-predicate-based-caching-scheme-for-client-server-database-59rgoi5sqd.pdf

A predicate-based caching scheme for client-server database architectures

A critical problem in building an information mediator is how to translate a domain-level query into an efficient query plan for accessing the required data. We have built a flexible and efficient information mediator, called SIMS. This system 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 optimization to minimize the overall execution time. This paper describes these three basic components of the query processing in SIMS.

/pdf/query-processing-in-the-sims-information-mediator-2cd040wsy1.pdf

Query processing in the SIMS information mediator

Datalog programs, i.e., Prolog programs without function symbols, are considered It is assumed that a variable appearing in the head of a rule must also appear in the body of the rule. The input of a program is a set of ground atoms (which are given in addition to the program's rules) and, therefore, can be viewed as an assignment of relations to some of the program's predicates. Two programs are equivalent if they produce the same result for all possible assignments of relations to the extensional predicates (i.e., the predicates that do not appear as heads of rules). Two programs are uniformly equivalent if they produce the same result for all possible assignments of initial relations to all the predicates (i.e., both extensional and intentional). The equivalence problem for Datalog programs is known to be undecidable. It is shown that uniform equivalence is decidable, and an algorithm is given for minimizing a Datalog program under uniform equivalence. A technique for removing parts of a program that are redundant under equivalence (but not under uniform equivalence) is developed. A procedure for testing uniform equivalence is also developed for the case in which the database satisfies some constraints.

Optimizing datalog programs

We propose a new client-side data caching scheme for relational databases with a central server and multiple clients. Data is loaded into a client cache based on queries, which are used to form predicates describing the cache contents. A subsequent query at the client may be satisfied in its local cache if we can determine that the query result is entirely contained in the cache. This issue is called 'cache completeness'. On the other hand, 'cache currency deals with the effect of updates at the central database on the client caches. We examine various performance and optimization issues involved in addressing the questions of cache currency and completeness using predicate descriptions. Expected benefits of our approach over commonly used object 1D-based caching include lower query response times, reduced message traffic, higher server throughput, and better scalability. >

/pdf/a-predicate-based-caching-scheme-for-client-server-database-595mx16p8s.pdf

The problem of database query optimization is to select an efficient way to process a query expressed in logical terms from among the alternative ways it can be carried out in the physical database. This thesis presents a new approach to this problem, called semantic query optimization. The goal of semantic query optimization is to produce a semantically equivalent query that is less expensive to process than the original query.
Semantic query optimization actually transforms the original query into a new one by means of a process of inference. The transformations are limited to those that yield a semantically equivalent query, one that is guaranteed to produce the same answer as the original query in any permitted state of the database. This guarantee is achieved because the knowledge used to transform a query is the same knowledge used to insure the semantic integrity of the data stored in the database. Thus, semantic query optimization brings together the apparently separate research areas of query processing the database integrity.
The thesis also addresses an important issue in current automatic planning research: production not just of a correct solution but of a "good" one, by means of an efficient problem solver. Semantic query optimization advances the notion of a problem reformulation step for problem-solving programs. In this step, equivalent statements of the original problem are sought, one of which may have a better solution than the original problem. This method avoids explicit and possibly costly analysis of efficiency factors during planning itself.
Semantic query optimization can also be viewed as one aspect of intelligent database mediation. It applies knowledge of a problem domain and of the capabilities and limitations of the database to pose the most effective and easily processed queries to solve a user's problem.
The thesis formally defines transformations that preserve semantic equivalence for queries in the relational calculus. In addition, it identifies several classes of cost-reducing query transformations for relational database queries, and provides quantitative estimates of the improvements they can produce, based upon widely accepted models of query processing.
The thesis also discusses the design and implementation of a system that carries out semantic query optimization for an important class of relational database queries. The system is called QUIST, standing for QUery Improvement through Semantic Transformation.
The QUIST system has analyzed a range of queries for which different transformations apply. For these queries, QUIST obtains substantial reductions in the cost of processing at a negligible cost for the analysis itself.

http://www.dtic.mil/dtic/tr/fulltext/u2/a108735.pdf

Query optimization by semantic reasoning

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Query optimization by semantic reasoning