Compter Information Systems

In this paper we present a simple dynamization method that preserves the query and storage costs of a static data structure and ensures reasonable update costs. In this method, the majority of data elements are maintained in a single data structure, and the updates are handled using “smaller” auxiliary data structures. We analyze the query, storage, and amortized update costs for the dynamic version of a static data structure in terms of a functionf, such thatf(n)<n, that bounds the sizes of the auxiliary data structures (wheren is the number of elements in the data structure). The conditions onf for minimal (with respect to asymptotic upper bounds) amortized update costs are then obtained. The proposed method is shown to be particularly suited for the cases where the merging of two data structures is more efficient than building the resultant data structure from scratch. Its effectiveness is illustrated by applying it to a class of data structures that have linear merging cost; this class consists of data structures such as Voronoi diagrams, K-d trees, quadtrees, multiple attribute trees, etc.

One practical goal of sensor deployment in the design of distributed sensor systems is to achieve an optimal monitoring and surveillance of a target region. The optimality of a sensor deployment scheme is a tradeoff between implementation cost and coverage quality levels. In this paper, we consider a probabilistic sensing model that provides different sensing capabilities in terms of coverage range and detection quality with different costs. A sensor deployment problem for a planar grid region is formulated as a combinatorial optimization problem with the objective of maximizing the overall detection probability within a given deployment cost. This problem is shown to be NP-complete and an approximate solution is proposed based on a two-dimensional genetic algorithm. The solution is obtained by the specific choices of genetic encoding, fitness function, and genetic operators such as crossover, mutation, translocation for this problem. Simulation results of various problem sizes are presented to show the benefits of this method as well as its comparative performance with a greedy sensor placement method.

The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.

A bstract-A new balanced multidimensional tree structure called a k-dimensional balanced binary tree, k a positive integer, is presented and investigated. It is shown that the data structure can be used to manage a set of n k-dimensional data items such that access, insertion, and deletion operations can be supported in Ooog n + k) time. The data structure is a multidimensional generalization of a balanced binary tree and retains much of the simplicity and efficiency of the base structure. The data structure requires O(k) single rotations for each insertion or deletion operation.

The i&motion problem for a subclass of mtanglts called r-rwtanglea is inv&igated and reduced to the balanced batched r(wricted~range searching problem as well as to the balanced batched inverse r-range searching problem. Simple algorithms for these problems are given which are space and time optimal. The algorithm given for the balanced batched r-range searching problem leads to a new algorithm for the all-points ECDF problem in 2-apace whioh is simple and optimal. Again, the balanced bat&d r-range mrohing algorithm is combined witb a known algorithm for batched range sear&@ problems, leading to a new algorithm for the r&angle inters&ion problem which is space and time optimal in the worst czsc when the given set of rectan&a contains a much higher proportion of r-rcctangl~. OElwvier North Holland, Inc., 1980 52 Vanderbilt Ave., New York, NY 10017 60 V. K. VAISHNAVI, H. P. KRIEGEL, AND D. WOOD problems. These problems deal with the computation of intersection properties among sets of planar objects such as line segments, circles, half spaces, and rectangles. Recent papers in this area are [7], [3], [5], and [8]. Bentley and Wood [5] and Vaishnavi [8] deal, in particular, with the rectangle intersection problem.

The aim of the present paper is to provide an efficient solution to the following problem: "Given a family of n rectilinear line segments in two-space report all intersections in the family with a query consisting of an arbitrary rectilinear line segment." We provide an algorithm which takes O(nlog,n) preprocessing time, o( n logzn) space and O(logzn + k) query time, where k is the number of reported intersections. This solution serves to introduce a powerful new data structure, the layered segment tree, which is of independent interest. Second it yields, by way of recent dynamization techniques, a solution to the on-line version of the above problem, that is the operations INSERT and DELETE and QUERY with a line segment are allowed. Third it also yields a new nonscanning solution to the batched version of the above problem. Finally we apply these techniques to the problem obtained by replacing "line segment" by "rectangle" in the above problem, giving an efficient solution in this case also.

The construction of optimum multiway search trees for n keys, n key weights and n+1 gap weights, is investigated. A new general optimality principle is presented, which can be “tuned” for specific applications. Moreover we consider the affects of three additional constraints, namely height, structural and node search restrictions, which lead to a number of new construction algorithms. In particular we concentrate on the construction of optimum t-ary search trees with linear and binary search within their nodes for which we obtain O(n 3t) and O(n 3log2t) time algorithms, respectively. Whether these algorithms are or are not optimal remains an important open problem, even in the binary case.

This paper develops the Smart Object paradigm and its instantiation, which provide a new conceptualization for the modeling, design, and development of an important but little researched class of information systems, Operations Support Systems (OSS). OSS is our term for systems which provide interactive support for the management of large, complex operations environments, such as manufacturing plants, military operations, and large power generation facilities. The most salient feature of OSS' is their dynamic nature. The number and kind of elements composing the system as well as the mode of control of those elements change frequently in response to the environment. The abstraction of control and the ease with which complex dynamic control behavior can be modeled and simulated is one of the important aspects of the paradigm. The framework for the Smart Object paradigm is the fusion of object-oriented design models with declarative knowledge representation and active inferencing from AI models. Additional defining concepts from data/knowledge models, semantic data models, active databases, and frame based systems, are added to the synthesis as justified by their contribution to the ability to naturally model OSS at a high level of abstraction. The model assists in declaratively representing domain data/knowledge and its structure, and task or process knowledge, in addition to modeling multilevel control and interobject coordination.

A persistent problem in the use of automated workflow management systems for inter-organizational workflows has been the need for manual redefinition of coordination points in the process models when either organization changes its processes. Coordinating communications between production-chain organizations are usually based on the notification of completion of tasks by one party which constitute pre-conditions for activity in another organization; autonomous task changes disrupt coordination. This paper describes a workflow re-coordination model and corresponding expert support system based on workflow goals which are more stable than the low-level machine, role and technique dependent activities which implement them. Following development of the model, we describe a set of three elementary disruption cases which span a large number of common workflow changes. Using these cases we demonstrate that common coordination disruption situations can be totally or partially repaired by use of an expert coordination subsystem.