46377078
A Portable Method of Integrating SLG Resolution into Prolog Systems
42187803
Incremental Techniques for Efficient Normalization of Nonlinear Rewrite Systems
In [8] we described a nonlinear pattern-matching algorithm with the best known worst-case and optimal average-case time complexity. In this paper we first report on some experiments conducted on our algorithm. Based on these experiments we believe that our algorithm is useful in speeding up normalization of nonlinear rewrite systems even when it has a small number (≥5) of rewrite rules.
42876448
Optimal Speedups for Parallel Pattern Matching in Trees
Tree pattern matching is a fundamental operation that is used in a number of programming tasks such as code optimization in compilers, symbolic computation, automatic theorem proving and term rewriting. An important special case of this operation is linear tree pattern matching in which an instance of any variable in the pattern occurs at most once. If n and m are the number of nodes in the subject and pattern tree respectively and if no restriction is placed on the structure of the trees, then the fastest known sequential algorithm for linear tree pattern matching requires O(nm) time in the worst case.
10316561
Variable-depth trie index optimization: theory and experimental results
We develop an efficient approach to Trie index optimization. A Trie is a data structure used to index a file having a set of attributes as record identifiers. In the proposed methodology, a file is horizontally partitioned into subsets of records using a Trie index whose depth of indexing is allowed to vary. The retrieval of a record from the file proceeds by “stepping through” the index to identify a subset of records in the file in which a binary search is performed. This paper develops a taxonomy of optimization problems underlying variable-depth Trie index construction. All these problems are solvable in polynomial time, and their characteristics are studied. Exact algorithms and heuristics for their solution are presented. The algorithms are employed in CRES-an expert system for editing written narrative material, developed for the Department of the Navy. CRES uses several large-to-very-large dictionary files for which Trie indexes are constructed using these algorithms. Computational experience with CRES shows that search and retrieval using variable-depth Trie indexes can be as much as six times faster than pure binary search. The space requirements of the Tries are reasonable. The results show that the variable-depth Tries constructed according to the proposed algorithms are viable and efficient for indexing large-to-very-large files by attributes in practical applications.
5663050
Term Matching on Parallel Computers
Term matching is an important problem that arises very often in term rewriting and in functional and equational programming. We present a new parallel algorithm for the term-matching problem on the EREW (exclusive read, exclusive write) model of parallel computation. Our algorithm assumes a string representation of the two terms as its input. The string representation is first transformed into two labeled ordered trees, and term matching is then performed on these two trees. If n is the length of the input terms, then for any constant ϵ (0 < ϵ ≤ 1) our algorithm uses O(n1-ϵ) processors and takes O(nϵlogn) time. If ϵ = 0, the same algorithm will run in O(log2 n) time. The only other known parallel algorithm for this problem, due to Dwork, Kanellakis, and Stockmeyer, requires O(n2) processors and takes either O(logn) or O(log2n) time. However, their algorithm uses the stronger CREW (concurrent read, exclusive write) model of parallel computation and assumes a DAG (directed acyclic graph) representation of the two terms as its input. On the CRCW (concurrent read, concurrent write) model our algorithm requires n processors and O(log n) time.
16348991
Adaptive Pattern Matching
Pattern matching is an important operation used in many applications such as functional programming, rewriting, and rule-based expert systems. By preprocessing the patterns into a DFA-like automaton, we can rapidly select the matching pattern(s) in a single scan of the relevant portions of the input term. This automaton is typically based on left-to-right traversal of the patterns. By adapting the traversal order to suit the set of input patterns, it is possible to considerably reduce the space and matching time requirements of the automaton. The design of such adaptive automata is the focus of this paper. We first formalize the notion of an adaptive traversal. We then present several strategies for synthesizing adaptive traversal orders aimed at reducing space and matching time complexity. In the worst case, however, the space requirements can be exponential in the size of the patterns. We show this by establishing an exponential lower bounds on space that is independent of the traversal order used. We then discuss an orthogonal approach to space minimization based on direct construction of optimal dag automata. Finally, our work brings forth the impact of typing in pattern matching. In particular, we show that several important problems (e.g., lazy pattern matching in ML) are computationally hard in the presence of type disciplines, whereas they can be solved efficiently in the untyped setting.
61774575
A simple method to test the stability of 2-D recursive digital filters of second order
28166654
Distributed mission training: teams, virtual reality, and real-time networking
Abstract : Distributed Mission Training (DMT) is a revolutionary team-training paradigm currently evolving at the US Department of Defense, especially in the Air Force. The objective of DMT is to concurrently train people in team efforts involving coordination, communication, and decision making. The teams may not necessarily be collocated and could be engaged in independent as well as coordinated tasks at remote sites. The attached papers are a collection of DMT articles written by personnel of the Air Force Research Laboratory's Warfighter Training Research Division, in Mesa AZ, and published in the September 1999 issue of COMMUNICATIONS of the ACM.
60708157
Multicriteria jobshop scheduling
Abstract Jobshop scheduling is one of the important classes of problems in production planning. The jobshop represents a common manufacturing environmental, in which a set of machines is used to process a stream of jobs arriving randomly. The stochastic arrival pattern of the jobs, their processing sequences, and processing times render the scheduling of jobs in a jobshop a complex and difficult task. Furthermore, the scheduling task involves multiple conflicting objectives such as the minimization of the flow time, lateness and the number of late jobs among others. In this research, we develop a framework for the efficient scheduling of jobs in the presence of these multiple objectives. The motivation for this research arises from the good performance of the interactive man-machine scheduling systems in achieving significant levels of attainment in the set of objectives, simultaneously. We develop scheduling strategies similar to the man-machine approach in this research.
13648716
EARS: An online bibliographic search and retrieval system based on ordered explosion
Abstract Ergonomics Abstracts Retrieval System (EARS) is an online bibliographic information search and retrieval system using the hierarchical subject classification of the Ergonomics Abstracts . EARS is designed using an inverted file organization and is implemented on CDC-Cyber. The data base of abstracts is organized using a fixed-length record format, where each logical record corresponds to a variable number of fixed-length physical records. Accordingly, an index for the identification of physical records from the logical records is used. The data base is inverted on a three-level hierarchical classification scheme and postings files are used for data base inversion. The data base is accessed after selectively traversing a 4-layer structure of indexes and postings files. EARS provides facilities to perform combinations of searches, limited searches, and certain editing functions. The system is currently used extensively by the Western New York Human Factors research community. The logical and physical designs of EARS, its interactive operational features, and its current expansions are described in this paper.
773323
Intelligent computer integrated manufacturing (I-CIM): Research perspectives
Abstract Manufacturing automation has progressed through various stages from simple data transfer to the intelligence-intensive systems. The future of CIM relies heavily on intelligence-intensive systems because manufacturing is no longer confined to one local site and manufacturing systems have become complex because of their global nature. In this article, the authors study the future manufacturing environment as a collaborative effort. The essential characteristics-the requirements for integration from a process and communication perspective-are identified as are steps in the process requiring further study. Finally, Intelligent-Computer Integrated Manufacturing (I-CIM) scenarios are presented for specific problems.