An evaluation of standard retrieval algorithms and a binary neural approach

In this paper we evaluate a selection of data retrieval algorithms for storage efficiency, retrieval speed and partial matching capabilities using a large Information Retrieval dataset. We evaluate standard data structures, for example inverted file lists and hash tables, but also a novel binary neural network that incorporates: single-epoch training, superimposed coding and associative matching in a binary matrix data structure. We identify the strengths and weaknesses of the approaches. From our evaluation, the novel neural network approach is superior with respect to training speed and partial match retrieval time. From the results, we make recommendations for the appropriate usage of the novel neural approach. (C) 2001 Elsevier Science Ltd. All rights reserved

USING QUERY-DRIVEN SIMULATIONS FOR QUERYING OUTCOMES OF BUSINESS PROCESSES

When decision makers want to know outcomes of business processes in their organizations, they often use simulations to do this. This paper describes how a new Query-Driven Simulation (QDS) approach can be used by decision makers to obtain information about future outcomes of business processes in a more declarative, flexible, and interactive way than the traditional approach of running simulations and then gathering statistics about simulation outcomes. The paper also describes the types of questions decision makers ask about outcomes of business processes and studies how easy it is to express these questions in terms of an SQL-like query language SimQL designed for Query-Driven Simulations. It also identifies the types of applications that are especially well-suited for QDS. Finally, the paper describes the Query-Driven Simulation Modeling Lifecycle and how QDS provides a feedback loop in the model development process.Information Systems Working Papers Serie

Signature Files: An Integrated Access Method for Formatted and Unformatted Databases

The signature file approach is one of the most powerful information storage and retrieval techniques which is used for finding the data objects that are relevant to the user queries. The main idea of all signature based schemes is to reflect the essence of the data items into bit pattern (descriptors or signatures) and store them in a separate file which acts as a filter to eliminate the non aualifvine data items for an information reauest. It provides an integrated access method for both formattid and formatted databases. A complative overview and discussion of the proposed signatnre generation methods and the major signature file organization schemes are presented. Applications of the signature techniques to formatted and unformatted databases, single and multiterm query cases, serial and paratlei architecture. static and dynamic environments are provided with a special emphasis on the multimedia databases where the pioneering prototype systems using signatnres yield highly encouraging results

A QUERY-DRIVEN APPROACH TO SIMULATIONS

This paper describes a Query-Driven Simulation (QDS) approach to asking questions about outcomes of business processes. In this approach a user issues a query about outcomes of simulation runs and, based on the query asked, appropriate simulations are launched and the answer to the query is determined from the outcomes of these simulations. It is argued that Query-Driven Simulations provide a more declarative, flexible, and interactive approach to asking questions about simulation outcomes than the traditional approaches of letting the end-users run simulations and gather statistics about simulation outcomes. The paper also presents a new simulation system development lifecycle based on the QDS approach.Information Systems Working Papers Serie

Optimal Chunking of Large Multidimensional Arrays for Data Warehousing

Very large multidimensional arrays are commonly used in data intensive scientific computations as well as on-line analytical processingapplications referred to as MOLAP. The storage organization of such arrays on disks is done by partitioning the large global array into fixed size sub-arrays called chunks or tiles that form the units of data transfer between disk and memory. Typical queries involve the retrieval of sub-arrays in a manner that access all chunks that overlap the query results. An important metric of the storage efficiency is the expected number of chunks retrieved over all such queries. The question that immediately arises is"what shapes of array chunks give the minimum expected number of chunks over a query workload?" The problem of optimal chunking was first introduced by Sarawagi and Stonebraker who gave an approximate solution. In this paper we develop exact mathematical models of the problem and provide exact solutions using steepest descent and geometric programming methods. Experimental results, using synthetic and real life workloads, show that our solutions are consistently within than 2.0percent of the true number of chunks retrieved for any number of dimensions. In contrast, the approximate solution of Sarawagi and Stonebraker can deviate considerably from the true result with increasing number of dimensions and also may lead to suboptimal chunk shapes

USING QUERY-DRIVEN SIMULATIONS FOR QUERYING OUTCOMES OF BUSINESS PROCESSES

When decision makers want to know outcomes of business processes in their organizations, they often use simulations to do this. This paper describes how a new Query-Driven Simulation (QDS) approach can be used by decision makers to obtain information about future outcomes of business processes in a more declarative, flexible, and interactive way than the traditional approach of running simulations and then gathering statistics about simulation outcomes. The paper also describes the types of questions decision makers ask about outcomes of business processes and studies how easy it is to express these questions in terms of an SQL-like query language SimQL designed for Query-Driven Simulations. It also identifies the types of applications that are especially well-suited for QDS. Finally, the paper describes the Query-Driven Simulation Modeling Lifecycle and how QDS provides a feedback loop in the model development process.Information Systems Working Papers Serie