Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today’s database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and post-relational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

This paper defines aggregates in the temporal query language TQuel and provides their rormal semantics in the tuple relational calculus. A rormal semantics (or Que! aggregates is defined in the process. Multiple aggregates; aggregates appearing in the where, when, valid, and as-or clauses; nested aggregation; and instantaneous, cumulative, and unique variants are supported. These aggregates give the user a rich set or statistical functions that range over time, while requiring minimal additions to TQuel and its semantics..:'l'bi1 work wu nppolied bJ NSF (l'&lli DCR·8402330 and by a Junior Faculty Denlopmnt Awud from the UNC. CH FoUD.datioa. The &nt aat.hor wu npport.ed ia pan by u IBM Faculty Developmnt. Award..,!;

Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.

We present a query language called GraphLog, based on a graph representation of both data and queries. Queries are graph patterns. Edges in queries represent edges or paths in the database. Regular expressions are used to qualify these paths. We characterize the expressive power of the language and show that it is equivalent to stratified linear Datalog, first order logic with transitive closure, and non-deterministic logarithmic space (assuming ordering on the domain). The fact that the latter three classes coincide was not previously known. We show how GraphLog can be extended to incorporate aggregates and path summarization, and describe briefly our current prototype implementation. 1 Introduction The literature on theoretical and computational aspects of deductive databases, and the additional power they provide in defining and querying data, has grown rapidly in recent years. Much less work has gone into the design of languages and interfaces that make this additional pow...

Westudytheproblemofecientmaintenanceofmaterializedviews thatmaycontainduplicates.Thisproblemis particularlyimportantwhenqueriesagainstsuchviewsinvolve aggregatefunctions,whichneedduplicatestoproduce correctresults.Unlikemostworkontheviewmaintenance problemthatisbasedonanalgorithmicapproach,ourapproachis algebraicandbasedonequationalreasoning.This approachhasanumberofadvantages:itisrobustandeasily extendibletonewlanguageconstructs,itproducesoutput thatcanbeusedbyqueryoptimizers,anditsimpliescorrectness proofs. Weuseanaturalextensionoftherelationalalgebra operationstobags(multisets)asourbasiclanguage.We presentanalgorithmthatpropagateschangesfrombase relationstomaterializedviews.Thisalgorithmisbased onreasoningaboutequivalenceofbag-valuedexpressions. Weprovethatitiscorrectandpreservesacertainnotion ofminimalitythatensuresthatnounnecessarytuplesare computed.Althoughitisgenerallyonlyaheuristicthat computingchangestotheviewratherthanrecomputing theviewfromscratchismoreecient,weproveresults sayingthatundernormalcircumstancesoneshouldexpect thechangepropagationalgorithmtobesignicantlyfaster andmorespaceecientthancompleterecomputingofthe view.Wealsoshowthatourapproachinteractsnicely withaggregatefunctions,allowingtheircorrectevaluation onviewsthatchange.

This paper presents the design and an mmal perfor-mance evaluation of the query ophrmzer generator designed for the EXODUS extensible database system. Algetic transformaaon rules are translated mto an executable query optmuzer. which transforms query trees and selects methods for executmg operattons accordmg to cost funcaons associ-ated with the methods The search strategy avoids exhaus-ave search and it mties Itself to take advantage of past expenence Computattonal results show that an opmzer generated for a relational system produces access plans almost as good as those produced by exhaushve search, ~rlth the search tune cut to a small fraction 1

One source of partial information in databases is the need to combine information from several databases. Even if each database is complete for some "world", the combined databases will not be, and answers to queries against such combined databases can only be approximated. In this paper we describe various situations in which a precise answer cannot be obtained for a query asked against multiple databases. Based on an analysis of these situations, we propose a classification of constructs that can be used to model approximations. A major goal is to obtain universality properties for these models of approximations. Universality properties suggest syntax for languages with approximations based on the operations which are naturally associated with them. We prove universality properties for most of the approximation constructs. Then we use them to design languages built around datatypes given by the approximation constructs. A straightforward approach results in langauges that have a numb...

gyssensQcharlie.luc.ac.be laksQcs.concordia.ca We present a multi-dimensional database model, which we believe can serve as a con-ceptual model for On-Line Analytical Pro-cessing (OLAP)-based applications. Apart from providing the functionalities necessary for OLAP-based applications, the main fea-ture of the model we propose is a clear sepa-ration between structural aspects and the con-tents. This separation of concerns allows us to define data manipulation languages in a rea-sonably simple, transparent way. In particu-lar, we show that the data cube operator can be expressed easily. Concretely, we define an algebra and a calculus and show them to be equivalent. We conclude by comparing our ap-proach to related work. The conceptual multi-dimensional database model developed here is orthogonal to its im-plementation, which is not a subject of the present paper. 1

Systems for On-Line Analytical Processing (OLAP) considerably ease the process of analyzing business data and have become widely used in industry. OLAP systems primarily employ multidimensional data models to structure their data. However, current multidimensional data models fall short in their ability to model the complex data found in some real-world application domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real-world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and dimensions, built-in support for handling change and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, which addresses all nine requirements. Along with the model, we present an associated algebra, and outline how to implement the model using relational databases.

On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complexdata found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answere...