46069080
A Graph-Theoretic Model for Optimizing Large Join Queries
9253270
Query Optimization in Multidatabase Systems Considering Schema Conflicts
In a multidatabase system, the participating databases are autonomous. The schemas of these databases may be different in various ways, while the same information is represented. A global query issued against the global database needs to be translated to a proper form before it can be executed in a local database. Since data requested by a query (or a part of a query) is sometimes available in multiple sites, the site (database) that processes the query with the least cost is the desired query processing site. The authors study the effect of differences in schemas on the cost of query processing in a multidatabase environment. They first classify schema conflicts to different types. For each type of conflict, they show how much more or less complex a translated query can become in comparison with the originally user-issued global query. Based on this observation, they propose an analytical method that considers the conflicts between local databases and finds the database(s) that renders the least execution cost in processing a global query. This research introduces a new level of query optimization (termed the schema-level optimization) in multidatabase environments. The results provide a new dimension of enhancement for the capability of a query optimizer in multidatabase systems.
20308502
Object signatures for supporting efficient navigation in object-oriented databases
Most methods that have been proposed to improve query processing in object-oriented databases are designed for the aggregation hierarchy. Although the structure of the generalization hierarchy is similar to the aggregation hierarchy, techniques developed for the aggregation hierarchy cannot fully use the characteristics of the generalization hierarchy to improve performance. We devise a new object signature for the generalization hierarchy. The object signature consists of two parts: a reference signature and a structure signature. The former can be used to eliminate objects that do not match the nested predicates specified in the query, and the latter can be used to eliminate objects that do not belong to the target classes. We propose some processing methods using the object signatures for queries that involve generalization hierarchies. In addition, we define cost models of the object signature and then compare the performance of the traditional query processing method with the object signature method. The results show that using the object signatures can effectively reduce the number of disk accesses in a query.
2862791
A data delivery strategy in ubiquitous computing systems
Browsing Web pages through wireless devices will become more and more popular in the near future. However, accessing a large chunk of Web data incurs a great amount of access latency while a mobile user migrates across cells. This paper investigates this problem and proposes an effective way of delivering such data to a mobile user in a ubiquitous computing system. Our data delivery (/spl Dscr//sup 2/) strategy is based on the dynamic programming skill needed to pre-assign the user-requested data on the possible cells to be visited in order to reduce the access latency. We first design an encoding method for the cells and then derive the properties of the encoding scheme. These properties are then used in our algorithm to deliver the data. Finally, experiments are conducted to demonstrate the feasibility and efficiency of the proposed method.
31930663
Minimization of resource consumption for multidatabase query optimization
Due to the autonomy of individual databases, a multidatabase system (MDBS) has no control over the task execution within each database system. This complicates the issue of query optimization in a MDBS. Past researchers tackled this problem mainly by regenerating the cost model of each participating database systems. However a completely autonomous participating database system does nor own the information (data) as well as the mechanism (software) for converting data of one database to another to resolve the data type conflict problems that can occur in any MDBS environment. In addition, unpredictable factors that can drastically deviate the accuracy of a regenerated cost model (in the past research) come into play at unknown time. This confines the processing of an intersite operation (such as a join over two relations of different databases) to the MDBS only. The participating database systems will not be able to share the workload of the MDBS under this circumstance. Hence, minimizing the consumption of system resources of the MDBS is an urgent problem. The authors propose three scheduling algorithms that are used in the MDBS to reduce the processing cost of a multidatabase query. A major difference between the strategies and the past methods is that ours do not require the knowledge of the cost models of the participating databases.
18796888
Design and Performance Evaluation of Broadcast Algorithms for Time-Constrained Data Retrieval
We refer "time-constrained services" to those requests that have to be replied to within a certain client-expected time duration. If the answer cannot reach the client within this expected time, the value of the information may seriously degrade or even become useless. On-demand channels may not be able to handle all time-constrained services without degrading the performance. How to handle these services in broadcast channels becomes crucial to balance the load of wireless systems. In this paper, we study this problem and find the minimum number of broadcast channels required for such a task. Also, we propose solutions for this problem when the available channels are insufficient. Our performance result reveals that only a moderate number of channels is required to promote these time-constrained services
13302890
An Efficient and Scalable Management of Ontology
OWL is a recommended language for publishing and sharing ontologies on the Semantic Web. To manage the ontologies, several OWL data management systems have been proposed. However, the existing systems have limitations of the scalability and the reasoning. In this paper, we propose an OWL data management system, ONTOMS, which stores OWL data into class based relations, performs complete inverseOf, symmetric, and transitive reasoning for instances, and efficiently evaluates OWL-QL queries against ontologies in a relational database.
558748
Speech through myoelectric signal recognition SMyLES
People who can not communicate throu­ gh audible speech are separated from society. SMyLES, MES hardware, and Speech generation hardware will allow these people the ability to join society. This paper is about the software called SMyLES that is the heart of the speecli recognition through myoelectric
14565413
Performance Modeling of HPC Applications
Publisher Summary This chapter discusses the performance modeling of high performance computing (HPC) applications. Performance models of applications enable HPC system designers and centers to gain insight into the most optimal hardware for their applications, giving them valuable information into the components of hardware that for a certain investment of time/money will give the most benefit for the applications slated to run on the new system. The task of developing accurate performance models for scientific application on such complex systems can be difficult. The chapter briefly review a framework developed that provides an automated means for carrying out performance modeling investigations. The ongoing work to lower the overhead required for obtaining application signatures is discussed, and how one can increased the level-of-detail of convolutions with resulting improvements in modeling accuracy is explained. The chapter discusses how these technology advances enabled performance studies to explain why performance of applications, such as POP (Parallel Ocean Program), NLOM (Navy Layered Ocean Model), and Cobalt60, vary on different machines and quantifies the performance effect of various components of the machines. The chapter concludes by generalizing these results to show how this application's performance would likely improve if the underlying target machines were improved in various dimensions.
14489935
Dynamic resource allocation for CDMA-TDD indoor wireless systems
Future wireless communication systems are expected to provide a broad range of multimedia services that have a significant traffic asymmetry between uplink and downlink. The code division multiple access-time division duplex (CDMA-TDD) system is a promising solution to cope with the problem of traffic asymmetry. However, the TDD system is subject to inter-cell interference compared to frequency division duplex (FDD) system. Since both uplink and downlink share the same frequency in TDD, uplink and downlink may interfere each other especially when neighboring cells require different rates of traffic load. Thus, the resource allocation among cells is an important issue in TDD. In this paper, the resource allocation in the CDMA-TDD is formulated as a mixed integer programming (MIP) problem. A dynamic resource allocation algorithm (DRAA) is provided that effectively solves the traffic asymmetry problem. The MIP problem is also solved by a well-known branch and bound procedure. Both the crossed slot and non-crossed slot allocation are examined and compared to the DRAA. Computational result shows that proposed DRAA gives a good performance as the traffic asymmetry increases between the uplink and downlink. Copyright Â© 2003 John Wiley & Sons, Ltd.
30064492
Genetic reinforcement learning approach to the machine scheduling problem
This paper focuses on the development of a learning-based heuristic for the machine scheduling problem, which automatically captures the search control knowledge or the common features of good schedules while generating a number of schedules. Defining states and actions of the machine shop, the machine scheduling problem is transformed into a problem of reinforcement learning (RL) in which a learner or a scheduler will learn to select the right action at each state of the machine shop, using the reward from a schedule evaluator for executing the action. Implementing the proposed reinforcement learning with a genetic algorithm results in the genetic reinforcement learning (GRL) approach to the machine scheduling problem. Although the learning-based heuristic has the overhead of acquiring knowledge on the problem, it can be easily adapted for a wide variety of machine scheduling problems due to the weak dependence on the problem structures and objectives. A GRL-based scheduler, called EVIS (Evolutionary Intracell Scheduler), has been developed and applied to various classes of machine scheduling problems, such as the job-shop scheduling, the flow-shop scheduling and the open-shop scheduling problems, and even the processor scheduling problem, the performance evaluation of EVIS with a number of different problem instances has shown that the learning-based heuristic is robust and its performance is comparable with that of other problem-specific heuristics or search-oriented heuristics in the quality of solutions.