10348131
An Infrastructure for Web Services Migration for Real-Time Applications
Service-oriented architecture (SOA) is a highly promising technology for software development. It can greatly reduce the development time and cost and achieve system agility. Web service is the current practice for realizing SOA. Due to the nature of widely distributed Web service providers, the service performance could be impacted when the network traffic is congested. There are methods, such as communication cost based service selection strategies, developed to cope with the problem. In this paper, we propose a complementing technology, migratable Web services, to overcome the performance problems for real-time applications. A service can be implemented by multiple light weighted Web services and some of them can be migrated to strategic sites to minimize the communication delays so the real-time requirements can be met. We have designed an infrastructure to support Web service migration and execution. Also, appropriate decision mechanism, which decides whether a Web service should be migrated and where to send it to, is the key for major performance gains. We use genetic algorithm to efficiently make the best migration decisions. Experimental studies show that the approach can achieve significant performance improvement
5453537
Testability of Software in Service-Oriented Architecture
Service-oriented architecture (SOA) is a system architecture in which a collection of loosely coupled services communicate with each other using standard interfaces and message-exchanging protocols. As an emerging technology in software development, the SOA presents a new paradigm, and it affects the entire software development cycle including analysis, specification, design, implementation, verification, validation, maintenance and evaluation. This paper proposes several testability evaluation criteria for SOA software, which serves as a reference for both service providers and application builders to evaluate the test support to SOA software. The proposed evaluation criteria are illustrated in a stock-trading case study
3132727
Query Processing over Incomplete Autonomous Databases
Incompleteness due to missing attribute values (aka "null values") is very common in autonomous web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to a user query. Ideally we would like the mediator to retrieve such possible answers and gauge their relevance by accessing their likelihood of being pertinent answers to the query. The autonomous nature of web databases poses several challenges in realizing this objective. Such challenges include the restricted access privileges imposed on the data, the limited support for query patterns, and the bounded pool of database and network resources in the web environment. We introduce a novel query rewriting and optimization framework QPIAD that tackles these challenges. Our technique involves reformulating the user query based on mined correlations among the database attributes. The reformulated queries are aimed at retrieving the relevant possible answers in addition to the certain answers. QPIAD is able to gauge the relevance of such queries allowing tradeoffs in reducing the costs of database query processing and answer transmission. To support this framework, we develop methods for mining attribute correlations (in terms of Approximate Functional Dependencies), value distributions (in the form of Naive Bayes Classifiers), and selectivity estimates. We present empirical studies to demonstrate that our approach is able to effectively retrieve relevant possible answers with high precision, high recall, and manageable cost.
2511748
QPIAD: Query Processing over Incomplete Autonomous Databases
Incompleteness due to missing attribute values (aka "null values") is very common in autonomous Web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to a user query. Ideally we would like the mediator to retrieve such relevant uncertain answers and gauge their relevance by accessing their likelihood of being relevant answers to the query. However, the autonomous nature of the databases poses several challenges, such as the restricted access privileges, limited query patterns, and sensitivity of database and network resource consumption in the Web environment. We introduce a novel query rewriting and optimization framework QPIAD that tackles these challenges to retrieve relevant uncertain answers. Our technique involves reformulating the user query based on approximate functional dependencies (AFDs) among the database attributes and ranking these queries using value distributions learned from naive Bayes classifiers. Empirical studies demonstrate the effectiveness of our approach in retrieving relevant uncertain answers with high precision, high recall and manageable cost.
37282807
Testing Web Services Using Progressive Group Testing
This paper proposes progressive group testing techniques to test large number of Web services (WS) available on Internet. At the unit testing level, the WS with the same functionality are tested in group using progressively increasing number of test cases. A small number of WS that scored best will be integrated into the real environment for operational testing. At the integration testing level, many composite services will be constructed and tested by group integration testing. The results of group testing at both unit and integration levels are verified by weighted majority voting mechanisms. The weights are based on the reliability history of the WS under test. A case study is designed and implemented, where the dependency among the test cases in WS is analyzed and used to generate progressive layers of test cases.
5650965
L-Tree: A Dynamic Labeling Structure for Ordered XML Data
With the ever growing use of XML as a data representation format, we see an increasing need for robust, high performance XML database systems While most of the recent work focuses on efficient XML query processing, XML databases also need to support efficient updates To speed up query processing, various labeling schemes have been proposed However, the vast majority of these schemes have poor update performance In this paper, we introduce a dynamic labeling structure for XML data: L-Tree and its order-preserving labeling scheme with O(log n) amortized update cost and O(log n) bits per label L-Tree has good performance on updates without compromising the performance of query processing We present the update algorithm for L-Tree and analyze its complexity.
14759214
Real-coded ECGA for economic dispatch
In this paper, we propose a new approach that consists of the extended compact genetic algorithm (ECGA) and split-on-demand (SoD), an adaptive discretization technique, to economic dispatch (ED) problems with nonsmooth cost functions. ECGA is designed for handling problems with decision variables of the discrete type, while the decision variables of ED problems are oftentimes real numbers. Thus, in order to employ ECGA to tackle ED problems, SoD is utilized for discretizing the continuous decision variables and works as the interface between ECGA and the ED problem. Furthermore, ED problems in practice are usually hard for traditional mathematical programming methodologies because of the equality and inequality constraints. Hence, in addition to integrating ECGA and SoD, in this study, we devise a repair operator specifically for making the infeasible solutions to satisfy the equality constraint. To examine the performance and effectiveness, we apply the proposed framework to two different-sized ED problems with nonsmooth cost function considering the valve-point effects. The experimental results are compared to those obtained by various evolutionary algorithms and demonstrate that handling ED problems with the proposed framework is a promising research direction.
16055310
Step-by-Step Regression: A More Efficient Alternative for Polynomial Multiple Linear Regression in Stream Cube
Facing tremendous and potentially infinite stream data, it is impossible to record them entirely. Thus synopses are required to be generated timely to capture the underlying model for stream management systems. Traditionally, curve fitting through Multiple Linear Regression (MLR) is a powerful and efficient modeling tool. In order to further accelerate its processing efficiency, we propose Step-by-step Regression (SR) as a more efficient alternative. As revealed in experiments, it speeds up for more than 40 times. In addition, inspired by previous work, we integrated SR into cube environment through similar compression technique to perform online analytical processing and mining over data stream. Finally, experiments show that SR not only significantly alleviates the computation pressure on the front ends of data stream management systems, but also results in a much smaller stream cube for on line analysis and real-time surveillance.
13487570
Texture classification using statistical geometrical features
This paper presents a new texture feature set based on the statistics of geometrical attributes of connected regions in a stack of binary images obtained from a texture image. Systematic evaluation using all the Brodatz textures shows that the new set outperforms the well-known statistical gray level dependence matrix, the recently proposed statistical feature matrix, and Liu's features.<<ETX>>
3447304
Measuring the Dependability of Web Services for Use in e-Science Experiments
This paper introduces a dependability assessment tool (WSsDAT) for Web Services monitoring and testing. It allows users to evaluate dependability of Web Services from the point of view of their clients by collecting metadata representing a number of dependability metrics. This Java-based tool can be deployed in diverse geographical locations to monitor and test the behavior of a selected set of Web Services within preset time intervals. The graphical user interface of the tool provides real-time statistical data describing dependability of the Web Services under monitoring. The tool makes it possible for the users to identify typical patterns of dependability-specific behavior depending on the time of the day, days of the week or the client locations. In addition, WSsDAT can collect and analyze service dependability measurements during long periods of time, as well as obtaining dependability-related information about current service behavior. The paper reports on a successful series of experiments using this tool for investigating the dependability of two BLAST Web Services which are widely used in the bioinformatics domain. The paper shows how the tool can be employed by e-scientists in making informed choices of the distributed services being used in an in silico experiment and thereby improving their overall dependability.
6261034
An Economic Model of User Rating in an Online Recommender System
Economic modeling provides a formal mechanism to understand user incentives and behavior in online systems. In this paper we describe the process of building a parameterized economic model of user-contributed ratings in an online movie recommender system. We constructed a theoretical model to formalize our initial understanding of the system, and collected survey and behavioral data to calibrate an empirical model. This model explains 34% of the variation in user rating behavior. We found that while economic modeling in this domain requires an initial understanding of user behavior and access to an uncommonly broad set of user survey and behavioral data, it returns significant formal understanding of the activity being modeled.