Yue Kou

Resolving heterogeneity of Web-service composition in network manufacturing based on ontology

Web-service composition provides more powerful service functions through integrating existing Web services on the Internet, and collaborative working and resource-sharing among enterprises on the Internet have become a popular trend by means of Web-service composition technologies. Network manufacturing plays an important role in the manufacturing domain and can be realized based on Web-service composition. However, heterogeneity exists widely in complicated Web-service composition, except for manufacturing resources, and this must be resolved. In this paper, we focus on the heterogeneity existing in the execution of composite Web services in network manufacturing, in which the heterogeneity is classified into the heterogeneity between semantic equivalent Web services and the heterogeneity between subsequent Web services in a composite service. We summarize five types of conflicts, namely Semantic Conflict, Parameter Data Type Conflict, Parameter Structure Conflict, Parameter Number Conflict, and Parameter Data Unit Conflict. According to these five types of conflicts, an ontology-based approach for resolving them is proposed, including the definition of domain business process ontology knowledge, domain and industry ontology knowledge, and meta-ontology knowledge about transformation rules. Finally, a case is given, and the implementation technologies are introduced. The ontology based on heterogeneity resolution policy should be established as middleware and applied to any domain to make Web-service composition more available.

Query Intent Disambiguation of Keyword-Based Semantic Entity Search in Dataspaces

Keyword query has attracted much research attention due to its simplicity and wide applications. The inherent ambiguity of keyword query is prone to unsatisfied query results. Moreover some existing techniques on Web query keyword query in relational databases and XML databases cannot be completely applied to keyword query in dataspaces. So we propose KeymanticES a novel keyword-based semantic entity search mechanism in dataspaces which combines both keyword query and semantic query features. And we focus on query intent disambiguation problem and propose a novel three-step approach to resolve it. Extensive experimental results show the effectiveness and correctness of our proposed approach.

An Entity Relation Extraction Model Based on Semantic Pattern Matching

This paper proposes a relation extraction model based on semantic pattern matching in Web environment. It consists of frequent pattern extraction, pattern clustering based on density, and pattern matching based on semantic similarity. First, based on the entities with known relations in a limited training set, we extract relation patterns containing these named entities from the web page. Then the relations between entities from the web page in specific areas can be extracted based on these relation patterns extracted. Experiments show the affectivity and the self-adaptive of our method on extracting relations between entities from dynamic web environment.

A Throughput Driven Task Scheduler for Improving MapReduce Performance in Job-Intensive Environments

MapReduce has been proven to be a highly desirable platform for scalable parallel data analysis. The task scheduling in MapReduce is very crucial for the job execution and has a marked impact on the system performance. To the best of our knowledge, the previous scheduling algorithms rarely consider the job-intensive environments and are not able to provide high system throughput. Hence this paper proposes a novel technique for job-intensive scheduling to improve the system throughput. Firstly, by making an in-depth analysis of job-intensive environments, we sum up 4 major factors which affect the system throughput. Secondly, based on the factors, an efficient technique, called throughput driven task scheduler is proposed, in which, we adopt a series of effective measures to improve the throughput of a MapReduce cluster system. Finally, plenty of simulation experiments are made and the experimental results show that the scheduler can provide higher throughput than the previous systems and is able to meet the requirements of practical job-intensive applications.

Crawling Result Pages for Data Extraction Based on URL Classification

In Web database integration, crawling data pages is important for data extraction. The fact that data are contained by multiple result pages increases the difficulty of accessing data for integration. Thus, it is necessary to accurately and automatically crawl query result pages from Web database. To address this problem, we propose a novel approach based on URL classification to effectively identify result pages. In our approach, we compute the similarity between URLs of hyperlinks in result pages and classify them into four categories. Each category maps to a set of similar web pages, which separate result pages from others. Then, we use the page probing method to verify the correctness of classification and improve the accuracy of crawled result pages. The experimental result demonstrates that our approach is effective for identifying the collection of result pages in Web database, and can improve the quality and efficiency of data extraction.

SAMES: deadline-constraint scheduling in MapReduce

MapReduce is a popular parallel data-processing system, and task scheduling is one of the kernel techniques in MapReduce. In many applications, users have requirements that their MapReduce jobs should be completed before specific deadlines. Hence, in this paper, a novel scheduling algorithm based on the most effective sequence (SAMES) is proposed for deadline-constraint jobs in MapReduce. First, according to the characteristics of MapReduce, we propose a novel sequence-based execution strategy for MapReduce jobs and a new concept, the effective sequence (ES). Then, we design some efficient approaches for finding ESes and choose the most effective sequence (MES) for job execution. We also propose methods for MES-updates and exception handling. Finally, we verify the effectiveness of SAMES through experiments. The experimental results show that SAMES is an efficient scheduling algorithm for deadline-constraint jobs in MapReduce.

Computing the Split Points for Learning Decision Tree in MapReduce

The explosive growth of Data is bringing more and more challenges and opportunities to data mining. In data mining, learning decision tree is a common method, in which determining split points is the key problem. Existing methods of calculating split points in the distributed setting on large data either (1) cause high communication overhead or (2) are not universal for different levels of skewness of data distribution. In this paper, we study the properties of Gini impurity, which is a measure for determining split points, and design new algorithms for calculating split points in MapReduce. Empirical evaluation demonstrates that our method outperforms existing state-of-the-art techniques on communication cost and universality.

Subject-Oriented Classification Based on Scale Probing in the Deep Web

To access the large-scale data sources efficiently and automatically, it is necessary to classify these data sources into different domains and categories. In this paper, we propose a novel classification approach to classify data sources into detail domain subjects by query probing. In our approach, we train sample instances for each subject category and use them to probe the data scale of each source and category. And then we build a matrix to classify a data source into one or more subject categories and develop a decision algorithm based on probing iteration to rectify the classification result. Our experiments over real deep web sources show that our approach can achieve higher accuracy across a variety of data sources.

An Approach for Composing Web Services on Demand

The Web services composition plays a more and more important role in SOA environment nowadays. Composing Web services on users demand proves to be essential for both B2B and B2C applications. However, existing composition approaches and services composition description languages, such as BPEL4WS, are insufficient for satisfying user requirements. In this paper, we present an approach for Web services composition on demand. Our approach defines a description language for describing users demand and supporting dynamic services binding for flexible service composition, and applies SLA to service discovery that can automatically adapt the change of QoS constraints. The description language can be applied to any process-oriented composition language that supports executable business processes. Finally, our approach is applied to a service grid system where it provides a fully automatic, stable service composition on users demand

Discovering Relationships among Data Resources in DataSpace

Discovering the relationships among data resources in dataspace is an important issue, which is the basis for creating index, browsing, searching, querying, lineage and other services.However current researches mostly focus on the assumption that the relationships among data resources have been obtained, so they have more or less limitation. In order to solve this problem we propose an approach to discover the relationships among data resources accurately for managing the data resources in data space effectively