Conceptual clustering in information retrieval

Clustering is used in information retrieval systems to enhance the efficiency and effectiveness of the retrieval process. Clustering is achieved by partitioning the documents in a collection into classes such that documents that are associated with each other are assigned to the same cluster. This association is generally determined by examining the index term representation of documents or by capturing user feedback on queries on the system. In cluster-oriented systems, the retrieval process can be enhanced by employing characterization of clusters. In this paper, we present the techniques to develop clusters and cluster characterizations by employing user viewpoint. The user viewpoint is elicited through a structured interview based on a knowledge acquisition technique, namely personal construct theory. It is demonstrated that the application of personal construct theory results in a cluster representation that can be used during query as well as to assign new documents to the appropriate clusters.     

Allocation of Splitting Nodes in All-Optical Wavelength-Routed Networks

In this paper, we introduce the splitter placement problem in wavelength-routed networks (SP-WRN). Given a network topology, a set of multicast sessions, and a fixed number of multicast-capable cross-connects, the SP-WRN problem entails the placement of the multicast-capable cross-connects so that the blocking probability is minimized. The SP-WRN problem is NP-complete as it includes as a subproblem the routing and wavelength assignment problem which is NP-complete. To gain a deeper insight into the computational complexity of the SP-WRN problem, we define a graph-theoretic version of the splitter placement problem (SPG), and show that even SPG is NP-complete. We develop three heuristics for the SP-WRN problem with different degrees of trade-off between computation time and quality of solution. The first heuristic uses the CPLEX general solver to solve an integer-linear program (ILP) of the problem. The second heuristic is based on a greedy approach and is called most-saturated node first (MSNF). The third heuristic employs simulated annealing (SA) with route-coordination. Through numerical examples on a wide variety of network topologies we demonstrate that: (1) no more than 50% of the cross-connects need to be multicast-capable, (2) the proposed SA heuristic provides fast near-optimal solutions, and (3) it is not practical to use general solvers such as CPLEX for solving the SP-WRN problem.     

Cost-effective implementation of multicasting in wavelength-routednetworks

Multicasting in the optical domain has been recently shown to provide substantial savings in terms of the network-wide average packet hop distance and the total number of transceivers in the network. Current proposed multicasting architectures [e.g., splitter-and-delivery (SaD)] employ power splitting mechanisms which have the side effect of high fabrication cost due to the large number of splitters and the need for optical amplifiers. We propose a low-cost novel architecture called tap-and-continue (TaC) for realizing multicasting. This architecture provides a natural evolution from current unicast cross-connects and is based on tapping devices. We prove that any multicasting session can be feasibly realized in networks employing only TaC cross-connects, and the problem of finding the optimal multiple-destination minimum cost trail in such networks is NP-complete. Therefore, we develop a 4-approximation algorithm for multiple-destination routing. Simulation results demonstrate that the TaC cross-connect provides a realistic, cost-effective approach for implementing multicasting with negligible blocking degradation especially in multifiber networks     

Enhancing Concept-Based Retrieval Based on Minimal Term Sets

There is considerable interest in bridging the terminological gap that exists between the way users prefer to specify their information needs and the way queries are expressed in terms of keywords or text expressions that occur in documents. One of the approaches proposed for bridging this gap is based on technologies for expert systems. The central idea of such an approach was introduced in the context of a system called Rule Based Information Retrieval by Computer (RUBRIC). In RUBRIC, user query topics (or concepts) are captured in a rule base represented by an AND/OR tree. The evaluation of AND/OR tree is essentially based on minimum and maximum weights of query terms for conjunctions and disjunctions, respectively. The time to generate the retrieval output of AND/OR tree for a given query topic is exponential in number of conjunctions in the DNF expression associated with the query topic. In this paper, we propose a new approach for computing the retrieval output. The proposed approach involves preprocessing of the rule base to generate Minimal Term Sets (MTSs) that speed up the retrieval process. The computational complexity of the on-line query evaluation following the preprocessing is polynomial in m. We show that the computation and use of MTSs allows a user to choose query topics that best suit their needs and to use retrieval functions that yield a more refined and controlled retrieval output than is possible with the AND/OR tree when document terms are binary. We incorporate p-Norm model into the process of evaluating MTSs to handle the case where weights of both documents and query terms are non-binary.     

Stability and Performance of List Scheduling With External Process Delays

Non-preemptive static priority list scheduling is a simple, low-overhead approach to scheduling precedence-constrained tasks in real-time multiprocessor systems. However, it is vulnerable to anomalous timing behavior caused by variations in task durations. Specifically, reducing the duration of one task can delay the starting time of another task. This phenomenon, called Scheduling Instability, can make it difficult or impossible to guarantee real-time deadlines. Several heuristic solutions to handle scheduling instability have been reported. This paper addresses three main limitations in the state of the art in schedule stabilization. First, each stabilization technique only applies to a narrowly defined class of systems. To alleviate this constraint, we present an Extended Scheduling Model encompassing a wide range of assumptions about the scheduling environment, and address the stability problem under this model. Second, existing stabilization methods are heuristics based on a partial understanding of the causes of instability. We therefore derive a set of General Instability Conditions which are both necessary and sufficient for instability to occur. Third, solutions to scheduling instability range from trivial constraints on the run-time dispatcher through complex transformations of the precedence graph. We present scheduling simulation results comparing the average performance of several inherently stable run-time dispatchers of widely varying levels of complexity. Results show that for representative real-time workloads, simple low-overhead dispatchers perform nearly as well as a complex minimally stabilized dispatcher. Thus, complex schedule stabilization methods may be unnecessary, or even detrimental, due to their high computational overhead.     

Wavelength-exchanging cross connects (WEX)-a new class of photonic cross-connect architectures

All-optical wavelength division multiplexing (WDM) networks are expected to realize the potential of optical technologies to implement different networking functionalities in the optical domain. A key component in WDM networks is the optical switch that provides the basic functionality of connecting input ports to output ports. Existing WDM switches make use of space switches and wavelength converters (WCs) to realize switching. However, this not only increases the size and the complexity of the switch but also bears heavily on the cost. In this paper, the authors propose a new class of photonic switch architectures called wavelength-exchanging cross connect (WEX) that provides several advantages over existing switches by enabling a single-step space switching and wavelength conversion and thus eliminating the need for a separate conversion stage. This greatly enhances the switch architecture by reducing its size and complexity. The new class of cross-connect architectures is based on the proposed concept of a wavelength-exchange optical crossbar (WOC). The WOC concept is realized using the simultaneous exchange between two optical signals. The proposed WEX architecture is highly scalable. To establish scalability, the authors present a systematic method of developing instances of the switch architectures of an arbitrary large size.     

Sequential Association Rule Mining with Time Lags

This paper presents MOWCATL, an efficient method for mining frequent association rules from multiple sequential data sets. Our goal is to find patterns in one or more sequences that precede the occurrence of patterns in other sequences. Recent work has highlighted the importance of using constraints to focus the mining process on the association rules relevant to the user. To refine the data mining process, this approach introduces the use of separate antecedent and consequent inclusion constraints, in addition to the traditional frequency and support constraints in sequential data mining. Moreover, separate antecedent and consequent maximum window widths are used to specify the antecedent and consequent patterns that are separated by either a maximal width time lag or a fixed width time lag.Multiple time series drought risk management data are used to show that our approach can be effectively employed in real-life problems. This approach is compared to existing methods to show how they complement each other to discover associations in the drought risk management domain. The experimental results validate the superior performance of our method for efficiently finding relationships between global climatic episodes and local drought conditions. Both the maximal and fixed width time lags are shown to be useful when finding interesting associations.     

Scheduling real-time divisible loads with advance reservations

Providing QoS and performance guarantee to arbitrarily divisible loads has become a significant problem for many cluster-based research computing facilities. While progress is being made in scheduling arbitrarily divisible loads, previous approaches have no support for advance reservations. However, with the emergence of Grid applications that require simultaneous access to multi-site resources, supporting advance reservations in a cluster has become increasingly important. In this paper we propose a new real-time divisible load scheduling algorithm that supports advance reservations in a cluster. The impact of advance reservations on system performance is systematically studied. Simulation results show that, with the proposed algorithm and appropriate advance reservations, the system performance could be maintained at the same level as the no reservation case. Thus, Our approach enforces the real-time agreement vis-a-vis addresses the under-utilization concerns.     

Algorithms for the boundary selection problem

User-oriented clustering schemes enable the classification of documents based upon the user's perception of the similarity between documents, rather than on some similarity function presumed by the designer to represent the user's criteria. In an earlier paper it was shown that such a classification scheme can be developed in two stages. The first stage involves the accumulation of relevance judgements provided by users,vis-à-vis past query instances, into a suitable structure. The second stage consists of cluster identification. When the structure chosen, in the first stage, for the accumulation of corelevance characteristics of documents is a straight line, the second stage can be formulated as a function optimization problem termed the Boundary Selection Problem (BSP). A branch-and-bound algorithm with a good bounding function is developed for the BSP. Although significant pruning is achieved due to the bounding function, the complexity is still high for a problem of a large size. For such a problem a heuristic that divides it into a number of subproblems, each being solved by a branch-and-bound approach, is developed. Then the overall problem is mapped to an integer knapsack problem and solved by the use of dynamic programming. The tradeoff between accuracy and complexity can be controlled, giving the user a preference of one over the other. Assuming that the heuristic which divides the overall problem introduces no errors and is given sufficient time, the branch and bound with dynamic programming (BBDP) approach will converge to the optimal solution. Two other heuristic approaches, one with the application of a polynomial dynamic programming algorithm and the other which works in a greedy way, are also proposed for the BSP and an experimental comparison of all these approaches is provided. Experimental results indicate that all proposed algorithms show better performance compared with the existing algorithm.