Performance comparison of point and spatial access methods

In the past few years a large number of multidimensional point access methods, also called multiattribute index structures, has been suggested, all of them claiming good performance. Since no performance comparison of these structures under arbitrary (strongly correlated nonuniform, short "ugly") data distributions and under various types of queries has been performed, database researchers and designers were hesitant to use any of these new point access methods. As shown in a recent paper, such point access methods are not only important in traditional database applications. In new applications such as CAD/CIM and geographic or environmental information systems, access methods for spatial objects are needed. As recently shown such access methods are based on point access methods in terms of functionality and performance. Our performance comparison naturally consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in part I I spatial access methods for rectangles will be compared. In part I we present a survey and classification of existing point access methods. Then we carefully select the following four methods for implementation and performance comparison under seven different data files (distributions) and various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is robust under ugly data and queries. In part I I we compare spatial access methods for rectangles. After presenting a survey and classification of existing spatial access methods we carefully selected the following four methods for implementation and performance comparison under six different data files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree. This comparison is a first step towards a standardized testbed or benchmark. We offer our data and query files to each designer of a new point or spatial access method such that he can run his implementation in our testbed

Isomorphic Strategy for Processor Allocation in k-Ary n-Cube Systems

Due to its topological generality and flexibility, the k-ary n-cube architecture has been actively researched for various applications. However, the processor allocation problem has not been adequately addressed for the k-ary n-cube architecture, even though it has been studied extensively for hypercubes and meshes. The earlier k-ary n-cube allocation schemes based on conventional slice partitioning suffer from internal fragmentation of processors. In contrast, algorithms based on job-based partitioning alleviate the fragmentation problem but require higher time complexity. This paper proposes a new allocation scheme based on isomorphic partitioning, where the processor space is partitioned into higher dimensional isomorphic subcubes. The proposed scheme minimizes the fragmentation problem and is general in the sense that any size request can be supported and the host architecture need not be isomorphic. Extensive simulation study reveals that the proposed scheme significantly outperforms earlier schemes in terms of mean response time for practical size k-ary and n-cube architectures. The simulation results also show that reduction of external fragmentation is more substantial than internal fragmentation with the proposed scheme

Isomorphic Strategy for Processor Allocation in k-Ary n-Cube Systems

Due to its topological generality and flexibility, the k-ary n-cube architecture has been actively researched for various applications. However, the processor allocation problem has not been adequately addressed for the k-ary n-cube architecture, even though it has been studied extensively for hypercubes and meshes. The earlier k-ary n-cube allocation schemes based on conventional slice partitioning suffer from internal fragmentation of processors. In contrast, algorithms based on job-based partitioning alleviate the fragmentation problem but require higher time complexity. This paper proposes a new allocation scheme based on isomorphic partitioning, where the processor space is partitioned into higher dimensional isomorphic subcubes. The proposed scheme minimizes the fragmentation problem and is general in the sense that any size request can be supported and the host architecture need not be isomorphic. Extensive simulation study reveals that the proposed scheme significantly outperforms earlier schemes in terms of mean response time for practical size k-ary and n-cube architectures. The simulation results also show that reduction of external fragmentation is more substantial than internal fragmentation with the proposed scheme

Storage Coalescing

Typically, when a program executes, it creates objects dynamically and requests storage for its objects from the underlying storage allocator. The patterns of such requests can potentially lead to internal fragmentation as well as external fragmentation. Internal fragmentation occurs when the storage allocator allocates a contiguous block of storage to a program, but the program uses only a fraction of that block to satisfy a request. The unused portion of that block is wasted since the allocator cannot use it to satisfy a subsequent allocation request. External fragmentation, on the other hand, concerns chunks of memory that reside between allocated blocks. External fragmentation becomes problematic when these chunks are not large enough to satisfy an allocation request individually. Consequently, these chunks exist as useless holes in the memory system. In this thesis, we present necessary and sufficient storage conditions for satisfying allocation and deallocation sequences for programs that run on systems that use a binary-buddy allocator. We show that these sequences can be serviced without the need for defragmentation. We also explore the effects of buddy-coalescing on defragmentation and on overall program performance when using a defragmentation algorithm that implements buddy system policies. Our approach involves experimenting with Sun’s Java Virtual Machine and a buddy system simulator that embodies our defragmentation algorithm. We examine our algorithm in the presence of two approximate collection strategies, namely Reference Counting and Contaminated Garbage Collection, and one complete collection strategy - Mark and Sweep Garbage Collection. We analyze the effectiveness of these approaches with regards to how well they manage storage when we alter the coalescing strategy of our simulator. Our analysis indicates that prompt coalescing minimizes defragmentation and delayed coalescing minimizes number of coalescing in the three collection approaches

Online Data Structures in External Memory

The original publication is available at www.springerlink.comThe data sets for many of today's computer applications are too large to t within the computer's internal memory and must instead be stored on external storage devices such as disks. A major performance bottleneck can be the input/output communication (or I/O) between the external and internal memories. In this paper we discuss a variety of online data structures for external memory, some very old and some very new, such as hashing (for dictionaries), B-trees (for dictionaries and 1-D range search), bu er trees (for batched dynamic problems), interval trees with weight-balanced B-trees (for stabbing queries), priority search trees (for 3-sided 2-D range search), and R-trees and other spatial structures. We also discuss several open problems along the way

A Framework for Index Bulk Loading and Dynamization

In this paper we investigate automated methods for externalizing internal memory data structures. We consider a class of balanced trees that we call weight-balanced partitioning trees (or wp-trees) for indexing a set of points in Rd. Well-known examples of wp-trees include fed-trees, BBD-trees, pseudo quad trees, and BAR trees. These trees are defined with fixed degree and are thus suited for internal memory implementations. Given an efficient wp-tree construction algorithm, we present a general framework for automatically obtaining a new dynamic external data structure. Using this framework together with a new general construction (bulk loading) technique of independent interest, we obtain data structures with guaranteed good update performance in terms of I /O transfers. Our approach gives considerably improved construction and update I/O bounds of e.g. fed-trees and BBD-trees

Compression Techniques for the Grid File

Computing and Information Scienc