Artificial Intelligence
Parallel free-text search on the connection machine system
Communications of the ACM - Special issue on parallelism
TEAM: an experiment in the design of transportable natural-language interfaces
Artificial Intelligence
Description and performance analysis of signature file methods for office filing
ACM Transactions on Information Systems (TOIS)
Information retrieval by constrained spreading activation in semantic networks
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
Knowledge organization and access in a conceptual information system
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
Natural language understanding
Natural language understanding
Query processing in a multimedia document system
ACM Transactions on Information Systems (TOIS)
Knowledge-based search tactics for an intelligent intermediary system
ACM Transactions on Information Systems (TOIS)
SILOL: a simple logical-linguistic document retrieval system
Information Processing and Management: an International Journal - Special issue on natural language processing and information retrieval
Signature-based text retrieval methods: a survey
Data Engineering
Optimal Scheduling of Signature Analysis for VLSI Testing
IEEE Transactions on Computers
Retrieval of multimedia documents by pictorial content: a prototype system
International conference on Multimedia information systems '91
Hybrid access method: an extended two-level signature file approach
International conference on Multimedia information systems '91
A formal model of trade-off between optimization and execution costs in semantic query optimization
Data & Knowledge Engineering
Progress in the application of natural language processing to information retrieval tasks
The Computer Journal - Special issue on information retrieval
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Lexical ambiguity and information retrieval
ACM Transactions on Information Systems (TOIS)
Exploiting captions in retrieval of multimedia data
Information Processing and Management: an International Journal
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
Inferring depictions in natural-language captions for efficient access to picture data
Information Processing and Management: an International Journal
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
An optimal evaluation of Boolean expressions in an online query system
Communications of the ACM
Generating, integrating, and activating thesauri for concept-based document retrieval
IEEE Expert: Intelligent Systems and Their Applications
An Efficient Pictorial Database System for PSQL
IEEE Transactions on Software Engineering
An Intelligent Image Database System
IEEE Transactions on Software Engineering
AMORE: A World Wide Web image retrieval engine
World Wide Web
Load Balancing of Parallelized Information Filters
IEEE Transactions on Knowledge and Data Engineering
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We consider information retrieval when the data—for instance, multimedia—is computationally expensive to fetch. Our approach uses “information filters” to considerably narrow the universe of possibilities before retrieval. We are especially interested in redundant information filters that save time over more general but more costly filters. Efficient retrieval requires that decisions must be made about the necessity, order, and concurrent processing of proposed filters (an “execution plan”). We develop simple polynomial-time local criteria for optimal execution plans and show that most forms of concurrency are suboptimal with information filters. Although the general problem of finding an optimal execution plan is likely to be exponential in the number of filters, we show experimentally that our local optimality criteria, used in a polynomial-time algorithm, nearly always find the global optimum with 15 filters or less, a sufficient number of filters for most applications. Our methods require no special hardware and avoid the high processor idleness that is characteristic of massive-parallelism solutions to this problem. We apply our ideas to an important application, information retrieval of captioned data using natural-language understanding, a problem for which the natural-language processing can be the bottleneck if not implemented well.