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.
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.
32066139
Topographically discounted Internet infrastructure resources: a panel study and econometric analysis
The Internet infrastructure is increasingly utilized for the provision and delivery of a variety of services. The rapid growth and substantial demand fluctuations of the supported services are placing strain on the service providers to effectively manage their infrastructure resources. Capacity Provision Network (CPN) is a market-driven mechanism that enables the service providers to efficiently allocate, share and trade service capabilities. Central to the operation of CPN is the notion of discounted capacities which capture the underlying topology of the Internet. It serves as a practical instrument for service providers to evaluate the utility of capacity services available at remote sites vis-à-vis the capacities available locally and from the content providers. We conducted an Internet based longitudinal field experiment to empirically assess and validate the critical properties of discount factors. Our analysis reveals that discount factors exhibit markedly lower volatility than network delays, and are more stable over seasonal and temporal trend patterns. The results related to size effects suggest that sharing and trade agreements with larger content can be particularly effective. Our finding on the existence of the concavity property points to the ability of intermediation services to enhance the overall gains from resource sharing arrangements.
120648016
Theory of convex cones in multicriteria decision making
We develop the theory of convex polyhedral cones in the objective-function space of a multicriteria decision problem. The convex cones are obtained from the decision-maker's pairwise judgments of decision alternatives and are applicable to any quasiconcave utility function. Therefore, the cones can be used in any progressively articulated solution procedure that employs pairwise comparisons. The cones represent convex sets of solutions that are inferior to known solutions to a multicriteria problem. Therefore, these convex sets can be eliminated from consideration while solving the problem. We develop the underlying theory and a framework for representing knowledge about the decision-maker's preference structure using convex cones. This framework can be adopted in the interactive solution of any multicriteria problem after taking into account the characteristics of the problem and the solution procedure. Our computational experience with different multicriteria problems shows that this approach is both viable and efficient in solving practical problems of moderate size.
62235624
An efficient approach to stochastic jobshop scheduling: algorithms and empirical investigations
Abstract Jobshop scheduling is one of the important classes of problems planning. The jobshop represents a common manufacturing environment, 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 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 considering 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 to the man-machine approach in this research. The algorithms developed in this research have been extensively tested and evaluated against the traditional scheduling methods using simulation studies. The results show that the proposed approach is efficient and viable for solving practical scheduling problems in the presence of multiple objectives.
6732453
Enterprise computing environments and cost assessment
31851172
Automata-Driven Efficient Subterm Unification
Syntactic unification has widespread use in computing. There are several operations used in deductive computing such as critical pair generation, paramodulation and narrowing that require unifying a term s with every subterm of another term p. This subterm unification problem can be solved naively by repeatedly unifying s with each subterm of p in isolation. The drawback of doing unification in isolation is that commonality among subterms of p is ignored. We present an algorithm for efficient subterm unification by exploiting this commonality. The central idea used in our algorithm is to reduce the common part computation in unification into a string-matching problem and solve it efficiently using a string-matching automaton. The automaton succinctly captures the commonality between subterms of p. The string-matching approach, in conjunction with two new techniques called bidirectional-reduce and marking enables efficient unification of s with every subterm of p.
35337405
A dynamic learning model for on-line quality control using the TAGUCHI approach
Abstract An important philosophy of quality control due to Taguchi rests on the belief that, once quality is designed into the product and the process, very little inspection is necessary. Taguchi evaluates quality in terms of costs incurred, including inspection and adjustment costs apart from quality loss. Using these costs, he determines an optimal interval for periodic inspection of products. At each inspection, the first item on the process line is checked for defects. If no defects are found, the process is assumed to be under control. On the other hand, if the first item turns out to be defective, the process is immediately stopped and adjusted. The underlying assumption here is that, once a defect is observed, it is construed that the process has drifted and all subsequent items are defective. This article challenges the underlying assumption by recognizing that the observation of one defective item does not necessarily imply that the process is out of control. Noisy signals may occur as a result ...
6059279
Information systems frontiers
COMMUNICATIONS OF THE ACM January 2000/Vol. 43, No. 1 71 In this article, we focus on certain important and promising IS/IT frontiers identified from the perspectives of academia, industry, and federal research funding agencies. Our objective is to focus the collective awareness of the IS community and those in related disciplines on some of the frontier developments in IS/IT with a vision of the road ahead and point to challenges and opportunities [1]. We address the following emerging frontiers in IS/IT: the integrated circuits (IC) revolution, object technologies, knowledge and distributed intelligence, the evolving multiagent intelligent systems, satellite networks and mobile computing and the virtual corporation of the next millennium. These frontiers capture significant areas of research and development with the potential to fundamentally alter the field of IS/IT in the next millennium. Jagdish Chandra, Salvatore T. March,
8380823
Term Matching on Parallel Computers
Term matching is an important problem that arises very often in term rewriting and in functional and equational programming. In this paper, 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 ∈, (0<#x2208;≤1) our algorithm uses O(n1−∈) processors and takes O(nelog n) time. If e=0 the same algorithm will run in O(log2n) time. The only other known parallel algorithm for this problem is due to Dwork, Kanellakis and Stockmeyer that requires O(n2) processors and takes either O(log n) 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. The techniques used in our algorithm are novel and have wide applicability.
46697244
Multistage Indexing for Speeding Prolog Executions
In a previous article we proposed a new and efficient indexing technique that utilizes all the functors in the clause-heads and the goal. The salient feature of this technique is that the selected clause-head unifies (modulo nonlinearity) with the goal. As a consequence, our technique results in sharper discrimination, fewer choice points and reduced backtracking. 
 
 
 
A naive and direct implementation of our indexing algorithms considerably slowed down the execution speeds of a wide range of programs typically seen in practice. This is because it handled deep and shallow terms, terms with few indexable arguments, small and large procedures uniformly. To beneficially extend the applicability of our algorithms we need mechanisms that are ‘sensitive’ to term structures and size and complexity of procedures. We accomplish this in the v-ALS compiler by carefully decomposing our indexing process into multiple stages. The operations performed by these stages increase in complexity ranging from first argument indexing to unification (modulo nonlinearity). Further the indexing process can be terminated at any stage if it is not beneficial to continue further. 
 
 
 
We have now completed the design and implementation of v-ALS. Using it we have enhanced the performance of a broad range of programs typically encountered in practice. Our experience strongly suggests that indexing based on unification (modulo nonlinearity) is a viable idea in practice and that a broad spectrum of useful programs can realize all of its benefits.