Stefano Ceri

Vertical partitioning algorithms for database design

This paper addresses the vertical partitioning of a set of logical records or a relation into fragments. The rationale behind vertical partitioning is to produce fragments, groups of attribute columns, that “closely match” the requirements of transactions. Vertical partitioning is applied in three contexts: a database stored on devices of a single type, a database stored in different memory levels, and a distributed database. In a two-level memory hierarchy, most transactions should be processed using the fragments in primary memory. In distributed databases, fragment allocation should maximize the amount of local transaction processing. Fragments may be nonoverlapping or overlapping. A two-phase approach for the determination of fragments is proposed; in the first phase, the design is driven by empirical objective functions which do not require specific cost information. The second phase performs cost optimization by incorporating the knowledge of a specific application environment. The algorithms presented in this paper have been implemented, and examples of their actual use are shown.

The Lowell database research self-assessment

Database needs are changing, driven by the Internet and increasing amounts of scientific and sensor data. In this article, the authors propose research into several important new directions for database management systems.

Distributed database design methodologies

This paper surveys methodological approaches for distributed database design. The design of distribution can be performed top-down or bottom-up; the first approach is typical of a distributed database developed from scratch, while the second approach is typical of the development of a multi-database as the aggregation of existing databases. We review the design problems and methodologies along both directions, and we describe DATAID-D, a top-down methodology for distribution design. We indicate how the methodology is part of a global approach to database design; how to collect the requirements about the distribution of data and applications; and how to progressively build the distribution of a schema. Our approach is exemplified through one case study.

Magic semi-joins

Abstract We study the properties of the magic semi-join, a new algebraic operator. In essence, a magic semi-join is the composition of a semi-join and a transitive closure. We present a theory for magic semi-joins that mirrors the theory for semi-joins; in particular, we define equivalence transformations of algebraic formulas using magic semi-joins, and we introduce the notion of full reducer program in this framework. The application of magic semi-joins is in the efficient evaluation of recursive DATALOG queries in centralized and in distributed databases.

Logic Programming and Databases: An Overview

This book deals with the integration of logic programming and databases to generate new types of systems, which extend the frontiers of computer science in an important direction and fulfil the needs of new applications. Several names are used to describe these systems:

A Review of Relational Databases and Prolog

This chapter provides an overview of the prerequisite notions that are required in order to understand this book. The reader who has a background in these topics can skip this chapter or part of it, perhaps after taking a look at the notation.

Overview of Research Prototypes for Integrating Relational Databases and Logic Programming

This chapter presents an overview of some of the research prototypes which are under development for the integration of relational databases and logic programming. We present:

Overview of Systems for Coupling Prolog to Relational Databases

This chapter presents an overview of some of the CPR systems and prototypes which have been developed for coupling Prolog to relational databases. We present: a) PRO-SQL, a system for coupling Prolog to the system SQL/DS, developed at the IBM Research Center at Yorktown Heights. b) EDUCE, a system for coupling Prolog to the database system Ingres, developed at the European Computer Industry Research Center in Munich. c) The ESTEAM interface, developed in the framework of the Esprit Project ESTEAM, for coupling generic Prolog and database systems d) BERMUDA, a prototype developed at the University of Winsconsin, for coupling Prolog to the Britton-Lee Intelligent Database Machine IDM 500. e) CGW, an architecture for coupling Prolog to a database system developed at Stanford University, and PRIMO, a prototype of an interface between ARITYPROLOG and the database system ORACLE, developed at the University of Modena, Italy. f) The QUINTUS interface between QUINTUS-PROLOG and the Unify Database System, a product developed by Quintus Computer Systems of Mountain View, California.

Proof Theory and Evaluation Paradigms of Datalog

The aim of this chapter is to show how the answer to a Datalog program can be computed. In order to achieve a computational approach to Datalog, in Sect. 7.1 we develop the proof theory of this language. We show how new facts can be inferred from an extensional database by use of a Datalog program. We then demonstrate that our proof-theoretic method is sound and complete, i.e., that it computes exactly what it should according to the model-theoretic semantics of Datalog. The proof theory of Datalog leads to a first algorithm “INFER” for processing Datalog programs. This algorithm iteratively computes new facts from facts that have already been established by using a forward chaining technique.

Extensions of Pure Datalog

The Datalog syntax we have been considering so far corresponds to a very restricted subset of first order logic and is often referred to as pure Datalog. Several extensions of pure Datalog have been proposed in the literature or are currently under investigation.