We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving thii problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has excellent scale-up properties with respect to the transaction size and the number of items in the database.

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Fast algorithms for mining association rules

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Fast Algorithms for Mining Association Rules in Large Databases

Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at the VLDB Conference, 1996.

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An overview of data warehousing and OLAP technology

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

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Data integration: a theoretical perspective

This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. Coverage of emerging topics such as data streams and cloud computing Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.

Principles of Distributed Database Systems

While much work has addressed the problem of maintaining materialized views, the important question of optimizing queries in the presence of materialised views has not been resolved. In this paper, we analyze the optimization question and provide a comprehensive and efficient solution. Our solution has the desirable property that it is a simple generalization of the traditional query optimization algorithm. >

Optimizing queries with materialized views

A programmable machine system and method for managing electronic data access among multiple different relational databases in a network distributed database environment. The machine is programmed so that it can construct cost-effective access strategies for any of the participating databases absent any DBMS-specific cost models. The system provides query optimization across different database management systems in a network distributed database environment based on a calibrating database relying only on typical relational database statistics and cost data is developed by running queries in the various databases against the calibrating database. A logical cost model is constructed using the resulting cost data and is used to estimate the cost of a given query based on logical characteristics of the DBMS, the relations, and the query itself. The cost of a complex query is estimated using primitive queries. Optimal query access strategies are thereby designed and used to control execution of the queries across relational databases controlled by two or more different database management systems.

Calibration of logical cost formulae for queries in a heterogeneous DBMS using synthetic database

Present relational language capabilities are insufficient to provide interoperability of databases even if they are all relational. In particular, unified multidatabaae view definitions cannot reconcile schematic discrepancies, where data in one database correspond to metadata of another. We claim that following new features are necessary: 1. Higher order expressions where variables can range over data and metadata, including database names. 2. Higher order (multidatabase) view definitions, where the number of relations or of attributes defined, is dependent on the state of the database(s). 3. Complete view updatability for the users of multidatabase views. We propose these features in the context of a Horn clause based language, called Interoperable Database Language, (IDL).

Language features for interoperability of databases with schematic discrepancies

State-of-the-art optimization approaches for relational database systems, e.g., those used in systems such as OBE, SQL/DS, and commercial INGRES. when used for queries in non-traditional database applications, suffer from two problems. First, the time complexity of their optimization algorithms, being combinatoric, is exponential in the number of relations to be joined in the query. Their cost is therefore prohibitive in situations such as deductive databases and logic oriented languages for knowledge bases, where hundreds of joins may be required. The second problem with the traditional approaches is that, albeit effective in their specific domain, it is not clear whether they can be generalized to different scenarios (e.g. parallel evaluation) since they lack a formal model to define the assumptions and critical factors on which their valiclity depends. This paper proposes a solution to these problems by presenting (i) a formal model and a precise statement of the optimization problem that delineates the assumptions and limitations of the previous approaches, and (ii) a quadratic-tinie algorithm th& determines the optimum join order for acyclic queries. The approach proposed is robust; in particular, it is shown that it remains heuristically effective for cyclic queries as well.

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Ravi Krishnamurthy

Papers

Optimizing queries with materialized views

Optimizing queries with materialized views

Calibration of logical cost formulae for queries in a heterogeneous DBMS using synthetic database

Language features for interoperability of databases with schematic discrepancies

Optimization of Nonrecursive Queries