Multidatabase query optimization

Ph.D. - Doctoral Progra

Vertex Separators for Partitioning a Graph

Finite Element Method (FEM) is a well known technique extensively studied for spatial and temporal modeling of environmental processes, weather prediction computations, and intelligent signal processing for wireless sensors. The need for huge computational power arising in such applications to simulate physical phenomenon correctly mandates the use of massively parallel computers to distribute the workload evenly. In this study, a novel heuristic algorithm called Line Graph Bisection which partitions a graph via vertex separators so as to balance the workload amongst the processors and to minimize the communication overhead is proposed. The proposed algorithm is proved to be computationally feasible and makes cost-effective parallel implementations possible to speed up the solution process

A heuristic approach for optimization of path expressions

Abstract. The object-oriented database management systems store references to objects (implicit joins, precomputed joins), and use path expressions in query languages. One way of executing path expressions is pointer chasing of precomputed joins. However it has been previously shown that converting implicit joins to explicit joins during the optimization phase may yield better execution plans. A path expression is a linear query, therefore, considering all possible join sequences within a path expression is polynomial in the number of classes involved. Yet, when the implicit joins are converted to explicit joins in a query involving multiple path expressions bound to the same bind variable, the query becomes a star query and thus considering all possible joins is exponential in the numberofpathsinvolved. This implies that there is a need for improvement by using heuristic in optimizing queries involving multiple path expressions. A heuristic based approach for optimizing queries involving multiple path expressions is described in this paper. First, given the cost and the selectivities of path expressions by considering a path expression as a unit of processing, we provide an algorithm that gives the optimum execution order of multiple path expressions bound to the same bind variable. For this purpose, we derive the formulas for the selectivities of path expressions. Then by using this ordering as a basis we provide a general heuristic approach for optimizing queries involving multiple path expressions. Two optimizers are developed to compare the performance of the heuristic based approach suggested in this paper with the performance of an optimizer based on an exhaustive search strategy. The exhaustive optimizer is generated through Volcano Optimizer Generator (VOG). The results of the experiments indicate that the heuristic based optimizer has a superior performance with the increasing number of path expressions.

Comparison of Clustering Algorithms in a Single User Environment through OO7 Benchmark

In this paper, we present a new clustering algorithm called the High Fan Out algorithm and then give the performance comparison of the High Fan Out (HFO) algorithm, Kernighan-Lin based algorithms, and the Probability Ranking Partitioning algorithm for a persistent C++(C**) implementation in a single user environment where the global request stream follows a pattern most of the time. The global request stream is obtained through OO7 Benchmark. It is shown that HFO algorithm performs the best when object sizes are uniform and the cache sizes are relatively large. We conclude with a table that indicates the best clustering algorithm to be used depending on the characteristics of the database application at hand and the restrictions imposed by the computer system. It is also indicated that, the performance of a clustering algorithm can not be based solely on the communication cost, or on the amount of internal fragmentation. On the contrary both of the measures should be taken into account..

Task assignment in tree-like hierarchical structures

Many large organizations, such as corporations, are hierarchical by nature. In hierarchical organizations, each entity, except the root, is a sub-part of another entity. In this paper, we study the task assignment problem to the entities of a tree-like hierarchical organization. The inherent tree structure introduces an interesting and challenging constraint to the standard assignment problem. Given a tree rooted at a designated node, a set of tasks, and a real-valued function denoting the weight of assigning a node to a task, the Maximum Weight Tree Matching (MWTM) problem aims at finding a maximum weight matching in such a way that no tasks are left unassigned, and none of the ancestors of an already assigned node is allowed to engage in an assignment. When a task is assigned to an entity in a hierarchical organization, the whole entity including its children becomes responsible from the execution of that particular task. In other words, if an entity has been assigned to a task, neither its descendants nor its ancestors can be assigned to any task. In the paper, we formally introduce MWTM, and prove its NP-hardness. We also propose and experimentally validate an effective heuristic solution based on iterative rounding of a linear programming relaxation for MWTM