58268816
Query Optimization in the IBM DB2 Family.
31748919
DB2 Query Parallelism: Staging and Implementation
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.
6838561
High Level Indexing of User-Defined Types
38283433
Resource Integration Framework of Logistics Resource Grid
In this paper, the Logistics Resource Grid (LRG) for complex logistics applications is constructed. Furthermore, a Resource Integration Framework (RIF) of LRG is developed with Globus Toolkit and OGSA-DAI. LRG and RIF provides a logistics resource integration and optimization environment for Third party logistics companies.
32478144
Experience from a real life query optimizer
DB2 is a major relational database system on IBM main frames, it has over six thousand licenses over the world to support a very large number of major applications for customer business, Optimizer in DB2 is a critical component for the query processing, it inherited system R technologies and has been improved in very many aspects from numerous valuable real life experiences on customer applications. The major DB2 optimizer enhancements will be discussed with the backgrounds and examples. A wish list for future enhancements as well as some trend and direction of DB2 optimizer will also be discussed.
37274241
Preparation of one-dimensional photonic crystal with variable period by using ultra-high vacuum electron beam evaporation
One-dimensional photonic crystal with variable period has been prepared by using the method of ultra-high vacuum electron beam evaporation. Advantage of this method is gaining variable period easily. The transmittance of the photonic crystal has been also studied.
14649490
Generation of finite tight frames by Householder transformations
Finite tight frames are widely used for many applications. An important problem is to construct finite frames with prescribed norm for each vector in the tight frame. In this paper we provide a fast and simple algorithm for such a purpose. Our algorithm employs the Householder transformations. For a finite tight frame consisting of m vectors in ℝn or ℂn only O(nm) operations are needed. In addition, we also study the following question: Given a set of vectors in ℝn or ℂn, how many additional vectors, possibly with constraints, does one need to add in order to obtain a tight frame?
15440093
Pattern discovery: a data driven approach to decision support
Decision support nowadays is more and more targeted to large scale complicated systems and domains. The success of a decision support system relies mainly on its capability of processing large amounts of data and efficiently extracting useful knowledge from the data, especially knowledge which is previously unknown to the decision makers. With a large scale system, traditional knowledge acquisition models become inefficient and/or more biased, due to the subjectivity of the experts or the pre-assumptions of certain ideas or algorithmic procedures. Today, with the rapid development of computer technologies, the capability of collecting data has been greatly advanced. Data becomes the most valuable resource for an organization. We present a fundamental framework toward intelligent decision support by analyzing a large amount of mixed-mode data (data with a mixture of continuous and categorical values) in order to bridge the subjectivity and objectivity of a decision support process. By considering significant associations of artifacts (events) inherent in the data as patterns, we define patterns as statistically significant associations among feature values represented by joint events or hypercells in the feature space. We then present an algorithm which automatically discovers statistically significant hypercells (patterns) based on: 1) a residual analysis, which tests the significance of the deviation when the occurrence of a hypercell differs from its expectation, and 2) an optimization formulation to enable recursive discovery. By discovering patterns from data sets based on such an objective measure, the nature of the problem domain will be revealed. The patterns can then be applied to solve specific problems as being interpreted or inferred with.