3127403
An efficient parallel strategy for computing K-terminal reliability and finding most vital edge in 2-trees and partial 2-trees
The authors develop a parallel strategy to compute K-terminal reliability in 2-trees and partial 2-trees. They also solve the problem of finding the most vital edge with respect to K-terminal reliability in partial 2-trees. The algorithms take O(log n) time with C(m,n) processors on a CRCW PRAM, where C(m,n) is the number of processors required to find connected components of a graph with m edges and n vertices in logarithmic time.
10538824
Parallel Decomposition of Generalized Series-Parallel Graphs
Generalized series-parallel (GSP) graphs belong to the class of decomposable graphs which can be represented by their decomposition trees. Given a decomposition tree of a GSP graph, there are many graph-theoretic problems which can be solved efficiently. An efficient parallel algorithm for constructing a decomposition tree of a given GSP graph is presented. It takes O(log n) time with C(m, n) processors on a CRCW PRAM, where C(m, n) is the number of processors required to find connected components of a graph with in edges and n vertices in logarithmic time. Based on our algorithmic results, we also derive some properties for GSP graphs, which may be of interest in and of themselves.
29920941
Counting Clique Trees and Computing Perfect Elimination Schemes in Parallel
Abstract In a previous result, the authors showed that a clique tree of a chordal graph can be constructed in O(log n ) parallel computing time with O( n 3 ) processors on CRCW PRAM, where n is the number of nodes of the graph. In this paper, it will be shown that this result can be extended in two ways. First, we show that from the parallel clique tree constructing algorithm, we can derive an exact formula of counting clique trees of a labeled connected chordal graph. Next, we show that a perfect elimination scheme of a chordal graph can be computed in O(log n ) time with O( n 2 ) processors on CREW PRAM once a clique tree of the graph is given. This implies that a perfect elimination scheme of a chordal graph can be computed in O(log n ) time with O( n 3 ) processors on CRCW PRAM.
44408883
Hamiltonian-laceability of star graphs
1980858
Edge and Node Searching Problems on Trees
In this paper, we show that there is a natural correspondence between a tree's edge-search strategy and its node-search strategy. By doing so, we simplify the previous linear time algorithm for determining the edge-search number of a tree, and improve the running time of a previous algorithm for constructing an edge-search strategy (or plan) for a tree containing n vertices from O(n log n) to O(n) time.
45928902
An Efficient Parallel Strategy for Computing K-Terminal Reliability and Finding Most Vital Edges in 2-Trees and Partial 2-Trees
In this paper, we first develop a parallel algorithm for computingK-terminal reliability, denoted byR(GK), in 2-trees. Based on this result, we can also computeR(GK) in partial 2-trees using a method that transforms, in parallel, a given partial 2-tree into a 2-tree. Finally, we solve the problem of finding most vital edges with respect toK-terminal reliability in partial 2-trees. Our algorithms takeO(logn) time withC(m,n) processors on a CRCW PRAM, whereC(m,n) is the number of processors required to find the connected components of a graph withmedges andnvertices in logarithmic time.
1145137
Agent-assisted collaborative concept map
Merlin is a collaborative concept map that enables students to discuss their ideas. The agent in Merlin aims to monitor the students' content of discussion in order to provide context-sensitive help. Keywords in the students' discussion in a chat box are compared to the knowledge domain and appropriate support material is adapted to the student model. Instructional media is generated using design patterns.
44418101
Degree-constrained minimum spanning tree
Abstract In this paper the problem of a degree-constrained minimum spanning tree (DCMST) is defined. The problem is formulated as a linear 0–1 integer programming problem. A primal and a dual heuristic (construction) procedure and a branch-and-bound algorithm are proposed to construct a DCMST. These procedures are illustrated with a simple example. Some computational experience with these algorithms is also reported.
12331697
The Clio project: managing heterogeneity
Clio is a system for managing and facilitating the complex tasks of heterogeneous data transformation and integration. In Clio, we have collected together a powerful set of data management techniques that have proven invaluable in tackling these difficult problems. In this paper, we present the underlying themes of our approach and present a brief case study.
39997093
Performance of an Ant Colony Optimization (ACO) Algorithm on the Dynamic Load-Balanced Clustering Problem in Ad Hoc Networks
This paper examines the performance of a recently proposed ACO algorithm when applied to the problem of constructing load-balanced clusters in ad hoc networks with node mobility. Performance, in this context, is measured in terms of the magnitude of change in solution quality after nodes move, and reactivity. Reactivity refers to the number of cycles ACO takes to recover from any degradation in solution quality resulting from node movements. Empirical results on 16 problem instances of various sizes revealed a positive correlation
641485
Mapping XML and relational schemas with Clio
Merging and coalescing data from multiple and diverse sources into different data formats continues to be an important problem in modern information systems. Schema matching (the process of matching elements of a source schema with elements of a target schema) and schema mapping (the process of creating a query that maps between two disparate schemas) are at the heart of data integration systems. We demonstrate Clio, a semi-automatic schema mapping tool developed at the IBM Almaden Research Center. In this paper, we showcase Clio's mapping engine which allows mapping to and from relational and XML schemas, and takes advantage of data constraints in order to preserve data associations.