206780897
An efficient hybrid join algorithm: a DB2 prototype
A new join method, called hybrid join, is proposed which uses the join-index filtering and the skip sequential prefetch mechanism for efficient data access. With this method, the outer table is sorted on the join column. Then, the outer is joined with the index on the join column of the inner. The inner tuple is represented by its surrogate, equivalent of its physical disk address, which is carried in the index. The partial join result is sorted on the surrogate and then the inner table is accessed sequentially to complete the join result. Local predicate filtering can also be applied before the access of the inner relation through the index AND/ORing. Efficient methods for skip sequential access and prefetching of logically discontiguous leaf pages of B/sup +/-tree indexes are also presented.<<ETX>>
6838561
High Level Indexing of User-Defined Types
35134245
Single Table Access Using Multiple Indexes: Optimization, Execution, and Concurrency Control Techniques
Many data base management systems' query optimizers choose at most one index for accessing the records of a table in a given query, even though many indexes may exist on the table. In spite of the fact that there are some systems which use multiple indexes, very little has been published about the concurrency control or query optimization implications (e.g., deciding how many indexes to use) of using multiple indexes. This paper addresses these issues and presents solutions to the associated problems. Techniques are presented for the efficient handling of record ID lists, elimination of some locking, and determination of how many and which indexes to use. The techniques are adaptive in the sense that the execution strategies may be modified at run-time (e.g., not use some indexes which were to have been used), if the assumptions made at optimization-time (e.g., about selectivities) turn out to be wrong. Opportunities for exploiting parallelism are also identified. A subset of our ideas have been implemented in IBM's DB2 V2R2 relational data base management system.
20214518
Extensible Indexing Support in DB2 Universal Database
Publisher Summary Emerging database (DB) applications require the scalable management of a large quantity of complex data, together with traditional business data and flexible querying capabilities for business intelligence. Object-relational databases or universal servers are component database management systems (DBMSs) developed to support applications. They leverage the mature relational database technology that provides scalability, reliability, and recovery. They enable users to introduce application-specific types and methods into a database. These extensibility features can be seen as infrastructure for providing plug-in components that extend the built-in repertoire of database types and functions with support for additional, nonstandard data types. Tables in a database may contain user-defined objects such as geographical shapes, images, and semi-structured or unstructured text documents. This chapter presents a high-level framework of indexing for user-defined types. The framework allows users to concentrate on the semantics of applications and user-defined predicates without being concerned with the low-level details of locking, recovery, buffer management, or balanced search-tree updates. It is tightly integrated with the database engine and enhances the value of underlying access methods, built-in or user-defined, in a database system by supporting indexing on new data types and indexed scans using new predicates.
58268816
Query Optimization in the IBM DB2 Family.
6225988
A global optimal algorithm for class-dependent discretization of continuous data
This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first use the normalized mutual information that measures the interdependence between the class labels and the variable to be discretized as the objective function, and then use fractional programming (iterative dynamic programming) to find its optimum. Unlike the majority of class-dependent discretization methods in the literature which only find the local optimum of the objective functions, the proposed method, OCDD, or Optimal Class-Dependent Discretization, finds the global optimum. The experimental results demonstrate that this algorithm is very effective in classification when coupled with popular learning systems such as C4.5 decision trees and Naive-Bayes classifier. It can be used to discretize continuous variables for many existing inductive learning systems.
11056640
Two Further Gradient BYY Learning Rules for Gaussian Mixture with Automated Model Selection
Under the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for Gaussian mixture model with an important feature that, via its maximization through a gradient learning rule, model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper proposes two further gradient learning rules, called conjugate and natural gradient learning rules, respectively, to efficiently implement the maximization of the harmony function on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient learning rules not only work well, but also converge more quickly than the general gradient ones.
33302534
A space-efficient algorithm for sequence alignment with inversions and reversals
A dynamic programming algorithm to find an optimal alignment for a pair of DNA sequences has been described by Schoniger and Waterman. The alignments use not only substitutions, insertions, and deletions of single nucleotides, but also inversions, which are the reversed complements, of substrings of the sequences. With the restriction that the inversions are pairwise non-intersecting, their proposed algorithm runs in O(n2m2) time and consumes O(n2m2) space, where n and m are the lengths of the input sequences, respectively. We develop a space-efficient algorithm to compute such an optimal alignment which consumes only O(nm) space within the same amount of time. Our algorithm enables the computation for a pair of DNA sequences of length up to 10,000 to be carried out on an ordinary desktop computer. Simulation study is conducted to verify some biological facts about gene shuffling across species.
122644283
Implementation of a modified Marder-Weitzner method for solving nonlinear eigenvalue problems
Our goal is to propose four versions of modified Marder-Weitzner methods and to present the implementation of the new-type methods with incremental unknowns for solving nonlinear eigenvalue problems. By combining with compact schemes and modified Marder-Weitzner methods, six schemes well suited for the calculation of unstable solutions are obtained. We illustrate the efficiency of the new algorithms by using numerical computations and by comparing them with existing methods for some two-dimensional problems.
18971548
Coverage-preserving Data Gathering in Wireless Sensor Networks
The power is the most significant resource in wireless sensor nodes, so energy conservation is the most important factor in designing the protocol for sensor networks. In this paper, we study the problem of energy-efficient collection of the physical information from the target area. The coverage-preserving data gathering (CPDG) problem is to build up a tree, which minimizes the total energy consumption, and preserves the original coverage to ensure the result correctness. This problem can be formalized as 0-1 integer linear programming, and solved by an efficient heuristic algorithm. Furthermore, an improved algorithm is also proposed to balance the energy consumption among all the sensor nodes. The experimental results show that CPDG algorithm can conserve the energy about 30% of PEDAP protocol, which is regarded as one of the most efficient data gathering protocols as we know.