Gultekin Ozsoyoglu

Temporal and real-time databases: a survey

A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two previously separate areas. First we characterize the time domain; then we investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. We examine temporal and real-time DBMS implementation. Finally, we summarize major research accomplishments to date and list several unanswered research questions. >

Extending relational algebra and relational calculus with set-valued attributes and aggregate functions

In commercial network database management systems, set-valued fields and aggregate functions are commonly supported. However, the relational database model, as defined by Codd, does not include set-valued attributes or aggregate functions. Recently, Klug extended the relational model by incorporating aggregate functions and by defining relational algebra and calculus languages. In this paper, relational algebra and relational calculus database query languages (as defined by Klug) are extended to manipulate set-valued attributes and to utilize aggregate functions. The expressive power of the extended languages is shown to be equivalent. We extend the relational algebra with three new operators, namely, pack, unpack, and aggregation-by-template. The extended languages form a theoretical framework for statistical database query languages.

Auditing and Inference Control in Statistical Databases

A statistical database (SDB) may be defined as an ordinary database with the capability of providing statistical information to user queries. The security problem for the SDB is to limit the use of the SDB so(that only statistical information is available and no sequence of queries is sufficient to infer protected information about any individual. When such information is obtained, the SDB is said to be compromised.

Statistical estimators for relational algebra expressions

Present database systems process all the data related to a query before giving out responses. As a result, the size of the data to be processed becomes excessive for real-time/time-constrained environments. A new methodology is needed to cut down systematically the time to process the data involved in processing the query. To this end, we propose to use data samples and construct an approximate synthetic response to a given query. In this paper, we consider only COUNT(E) type queries, where E is an arbitrary relational algebra expression. We make no assumptions about the distribution of attribute values and ordering of tuples in the input relations, and propose consistent and unbiased estimators for arbitrary COUNT(E) type queries. We design a sampling plan based on the cluster sampling method to improve the utilization of sampled data and to reduce the cost of sampling. We also evaluate the performance of the proposed estimators.

A graph query language and its query processing

Many new database applications involve querying of graph data. We present an object-oriented graph data model, and an OQL like graph query language, GOQL. The data model and the language are illustrated in the application domain of multimedia presentation graphs. We then discuss the query processing techniques for GOQL, more specifically, the translation of GOQL, into an operator-based language, called O-Algebra, extended with operators to deal with paths and sequences. We also discuss different approaches for efficient implementation of algebra operators for paths and sequences.

Pathways database system: an integrated system for biological pathways

MOTIVATION During the next phase of the Human Genome Project, research will focus on functional studies of attributing functions to genes, their regulatory elements, and other DNA sequences. To facilitate the use of genomic information in such studies, a new modeling perspective is needed to examine and study genome sequences in the context of many kinds of biological information. Pathways are the logical format for modeling and presenting such information in a manner that is familiar to biological researchers. RESULTS In this paper we present an integrated system, called Pathways Database System, with a set of software tools for modeling, storing, analyzing, visualizing, and querying biological pathways data at different levels of genetic, molecular, biochemical and organismal detail. The novel features of the system include: (a) genomic information integrated with other biological data and presented from a pathway, rather than from the DNA sequence, perspective; (b) design for biologists who are possibly unfamiliar with genomics, but whose research is essential for annotating gene and genome sequences with biological functions; (c) database design, implementation and graphical tools which enable users to visualize pathways data in multiple abstraction levels, and to pose predetermined queries; and (d) an implementation that allows for web(XML)-based dissemination of query outputs (i.e. pathways data) to researchers in the community, giving them control on the use of pathways data. AVAILABILITY Available on request from the authors.

Controlling FD and MVD inferences in multilevel relational database systems

The authors investigate the inference problems due to functional dependencies (FD) and multivalued dependencies (MVD) in a multilevel relational database (MDB) with attribute and record classification schemes, respectively. The set of functional dependencies to be taken into account in order to prevent FD-compromises is determined. It is proven that incurring minimum information loss to prevent compromises is an NP-complete problem. An exact algorithm to adjust the attribute levels so that no compromise due to functional dependencies occurs is given. Some necessary and sufficient conditions for MVD-compromises are presented. The set of MVDs to be taken into account for controlling inferences is determined. An algorithm to prevent MVD-compromises in a relation with conflict-free MVDs is given. >

Error-constrained COUNT query evaluation in relational databases

In thk paper, we discuss the problem of estimating a COUNT(E) query having a guaranteed error bound with a certain confidence level, where E is an arbitrary relational algebra (RA) expression with Select, Join and Intersection operations. Such a~ estimate is useful in query optimization as well as in real-time databases as it takes significantly less time to obtain compared with evaluating COUNT(E) completely. To guarantee the desired error bound with the given confidence level, we use double sampling (or two-phase sampling) where in the first stage preliminary information is obtained from a small pilot sample taken from the input relations. And then, the sample size for the second stage is deter-

Example-based graphical database query languages

Several user-friendly graphical database query languages that let users formulate a query by specifying a graphically aided example on the screen are compared. One of the earliest such languages, M.M. Zloofs (1977) Query-by-Example, is described. Other languages discussed are Aggregates-by-Example, Summary-Table-by-Example, and Query-by-Statistical-Relational-Table for statistical and scientific databases; Time-by-Example for historical databases; Generalized-Query-by-Example for relational, network, and hierarchical databases; Office-by-Example, Formanager, the Natural Forms Query Language, and System-for-Business-Automation for office environments; and Picquery and Query-by-Pictorial-Example for pictorial (image) databases. The basic features, query specification and interpretation, object manipulation, query language constructs, and query processing techniques used in each of the languages are discussed.<<ETX>>

Statistical estimators for aggregate relational algebra queries

This paper discusses the estimation of COUNT(E) queries by sampling, where E is an arbitrary relational algebra expression. Consistent and unbiased statistical estimators for COUNT(E) are proposed without any assumptions on the distributions of attribute values or the orderings of tuples in the operand relatlons of E. We present a set of COUNT’(E) estimator evaluation algorithms, all based on simple random sampling, and each suitable for a different type of relational algebra expression E. To improve the efficiency, we propose three enhancements, and revise the estimator evaluation algorithms to incorporate the enhancements. One of the enhancements m the use of cluster sampling. Estimator evaluation algorithms with the enhancements have been incorporated into a prototype DBMS. We present the performance evaluation of the estimators using the implemented prototype DBMS.