Efficient Storage Structure Research Articles

Simultaneous optimization and evaluation of multiple dimensional queries

Database researchers have made significant progress on several research issues related to multidimensional data analysis, including the development of fast cubing algorithms, efficient schemes for creating and maintaining precomputed group-bys, and the design of efficient storage structures for multidimensional data. However, to date there has been little or no work on multidimensional query optimization. Recently, Microsoft has proposed “OLE DB for OLAP” as a standard multidimensional interface for databases. OLE DB for OLAP defines Multi-Dimensional Expressions (MDX), which have the interesting and challenging feature of allowing clients to ask several related dimensional queries in a single MDX expression. In this paper, we present three algorithms to optimize multiple related dimensional queries. Two of the algorithms focus on how to generate a global plan from several related local plans. The third algorithm focuses on generating a good global plan without first generating local plans. We also present three new query evaluation primitives that allow related query plans to share portions of their evaluation. Our initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.

A new storage organization for temporal data bases

A temporal data base needs to maintain large amounts of data, which causes long delays in accessing information. In this article, we design and develop an efficient storage structure for maintaining both current and historical data. Our strategy consists of two storage organizations, one for current versions and the other for history versions. The organization for current versions follows an efficient storage organization of a conventional nontemporal data base. However, for maintaining the history versions of each item in the data base, we use an extended version of a Fibonacci heap, referred to as a history version heap. The novelty of our approach resides in its ability to insert a new version for an item into the data base in O(1) amortized time and retrieve the oldest version of an item in O(1) amortized time. These bounds are irrespective of the number of existing versions for that item. In addition, we can access or delete any version of a given item in logarithmic amortized time. Because our approach treats the current store and history store differently, it uses different access methods for each store possible.

Efficient handling of data structures in definitional languages

Implementations of operations on general data structures in definitional languages often lead to excessive copying and storage requirements. To partially overcome this problem, users are given facilities to select efficient storage structures or to guide storage allocation. This contradicts the spirit of definitional languages, requiring the user to get involved with implementation details.This paper presents a method for automatically recognizing excessive copying and optimizing the storage for data structures. Based on analysis of data dependencies, the storage may be reduced from an entire structure to individual elements of the structure. The benefits are especially significant in incremental structures, where only a constant number of elements of a large data structure is modified in each operation. For incremental structures, copying of unchanged parts of the structure is avoided, and unnecessary iterations are eliminated, without involving the user in this consideration. The user is thus relieved of considering the inefficiencies inherent in specifications in definitional languages. The method is applicable to a variety of language processors and computer architectures. The proposed optimization method produces better results than those obtained by explicit storage references.The paper describes the implementation of the method in the compiler of the MODEL definitional language. First, criteria are presented for recognizing structures that may be optimized. Then, a transformation that removes iterations implied by the operations on incremental structures is described. The method is exemplified by its application to the non-recursive iteration computation of the Ackermann's function.

Implementation of algorithms for K shortest loopless paths

AbstractImplementations of loopless k shortest path algorithms are examined. Efficient storage structures for a large number of paths are given. A fast algorithm for determining the shortest paths in Yen's method is developed. Timing experiments show that a hybrid of Clarke's and Yen's methods is generally the fastest, although not significantly. Using upper bounds for the lengths of paths essentially improves all methods.

Choosing an optimal sequence of discriminators

Multidimensional data structures have proved to be efficient storage structures for multidimensional dynamic data bases. A judicious choice of a sequence of discriminators can greatly reduce search costs for partial match queries. This paper proposes a scheme for optimal choice of sequence of discriminators for the K-D-B tree which takes the query statistics into account. The scheme is later extended to take into account various system parameters like seek time, block size, disk transfer rates and page placement policy. The problem of choosing the sequence is formulated as a 0–1 integer programming problem, the solution to which gives the required sequence of discriminators.

Choosing optimal branching factors for k-d-B trees

A k-d-B tree is an efficient multidimensional storage structure for a dynamic database. This study attempts to find optimal branching factors for a k-d-B tree subject to partial match queries which specify the same number of attributes. The suggested method is supported by experimental results.

Implicit enumeration based algorithms for postoptimizing zero‐one programs

AbstractIt has been shown by G. Roodman that useful postoptimization capabilities for the 0‐1 integer programming problem can be obtained from an implicit enumeration algorithm modified to classify and collect all fathomed partial solutions. This paper extends the the approach as follows: 1) Improved parameter ranging formulas are obtained by higher resolution classification criteria. 2) Parameters may be changed so as to tighten the original problem, in addition to relaxing it. 3) An efficient storage structure is presented to cope with difficult data collection task implicit in this approach. 4) Finally, computer implementation is facilitated by the elaboration of a unified set of algorithms.