Li Zhang, Yan Wang, Baoyan Song, Xuecheng Li, Xiuping Hao
{"title":"Geometry-Based Spatial Skyline Query in Wireless Sensor Network","authors":"Li Zhang, Yan Wang, Baoyan Song, Xuecheng Li, Xiuping Hao","doi":"10.1109/WISA.2014.13","DOIUrl":"https://doi.org/10.1109/WISA.2014.13","url":null,"abstract":"The skyline query method in the wireless sensor network has been widely used in the field of environmental monitoring, the field is mostly related to the spatial distance. For the multidimension of space location, it brings enormous computational cost for the general skyline query method in terms of the property calculation. In order to improve the efficient use of sensor energy, the paper proposes the method of geometry-based spatial skyline query in wireless sensor network (GSSky). Firstly, we can quickly find spatial skyline with respect to the special query region by the cut of skyline region based on convex hull vertices method. It can reduce the comparison times between nodes. Secondly, we traverse all the neighbor sensor monitoring regions by the method of the distributed recursion query based on the data node tree, it can reduce the data transmission consumption in the network. Simulation results show that the method can quickly return the regions which are near to query location and own the larger pollution energy, and reduce energy consumption.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123777965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Design of the SIMPLE Programming Language","authors":"Di Wu, Lin Chen, Bin Yu, Baowen Xu","doi":"10.1109/WISA.2014.21","DOIUrl":"https://doi.org/10.1109/WISA.2014.21","url":null,"abstract":"Thousands of programming languages have been designed to meet different demands of application. Among the languages, many have been proposed merely for research exploration, while others have been developed for industrial software productions. Whatever the languages are targeted for, they are vehicles for implementing solutions in software engineering. Therefore, it is vital criteria to evaluate a language by principles of software engineering. But to our best knowledge, modern languages, especially the newly designed ones, are mostly designed to address domain-specific requirements, instead of focusing on the basic principles of software engineering. Thus, we develop a general-purpose language, SIMPLE, which supports the concise concepts adhering to the fundamental software engineering principles. To this end, simplicity, readability, reliability, security, scalability, and efficiency are established as goals of the language and various modular language features are provided. In this paper, we introduce the design of SIMPLE and discuss how its language features comply with the software engineering principles.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125308917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Efficient Intermediate Data Fault-Tolerance Approach in the Cloud","authors":"Baoyan Song, Cai Ren, Xuecheng Li, Linlin Ding","doi":"10.1109/WISA.2014.44","DOIUrl":"https://doi.org/10.1109/WISA.2014.44","url":null,"abstract":"Recently, cloud computing frameworks have gained popularity for processing large scale parallel data applications. They usually generate enormous amounts of intermediate data which are short-lived, yet are important for the completion of job. Once there are server failures, it leads to the failures of the intermediate data, and then affects the computation of the whole job. However, the existing fault-tolerant processing approaches only adopt simple replication strategies which can incur significant network overhead, and have no considering of the characteristics of the intermediate data. Therefore, in this paper, we propose an efficient supporting intermediate data fault-tolerant cloud computing framework, named IDF_Support framework. By dividing the computing tasks into different classifications, IDF_Support framework can effectively process the intermediate data failures. Then, two levels based intermediate data fault-tolerant algorithms are proposed, respectively the inner task intermediate data fault-tolerant algorithm (Inner task IDF) which resolves the fault-tolerance within a task, and the outer task intermediate data fault-tolerant algorithm (Outer task IDF) which resolves the fault-tolerance among tasks. The experimental results show that our algorithms keep the reliability of the system when there are server failures.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134056284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xinyi Li, Hui He, Pengfei Chen, Xiaohui Zhang, L. Su, Yong Qi
{"title":"Multiplexing of Backup VMs Based on Greedy Policy","authors":"Xinyi Li, Hui He, Pengfei Chen, Xiaohui Zhang, L. Su, Yong Qi","doi":"10.1109/WISA.2014.43","DOIUrl":"https://doi.org/10.1109/WISA.2014.43","url":null,"abstract":"With the development of the newly proposed cloud computing, an increasing number of software systems are deployed in the cloud environment. Both researchers and engineers in academia and industry focus on seeking efficient virtual machine (VM) deployment and VM consolidation strategies. Increasing the utilization of physical or virtual resources which are directly providing services is mainly concerned, while overlooking the under-utilization of backup virtual resources. Being the valid solution of ensuring the high availability of cloud system, offering redundant backup resources is commonly used. However, excessive consumption of redundant backup instances brings about wasting of resources as well as raising capitalized cost of operation and maintenance of cloud infrastructures. This paper proposes the multiplexing methodology of backup virtual resources based on the restless multi-armed bandit model, aiming to increase the resource utilization and satisfy the guaranteed level of service availability in cloud system. With the known failure models and recovery models of VMs providing services, optimality and performance of the greedy multiplexing policy are also presented. Through the validation in simulation experiment, the proposed policy achieves the goal of extending traditional 1:1 backup provision to 1:M (M ≫ 1) between the backup VM and the VMs providing services. Thus, the utilization of backup resources can be significantly enhanced correspondingly. On the premise that the guaranteed level of service availability is no lower than 95%, the utilization enhancement of backup VMs can be raised to 90%.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132088012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Workload-Based Dynamic Adaptive Data Replica Placement Method","authors":"Wei Guo, Xinjun Wang, Yongquan Dong","doi":"10.1109/WISA.2014.41","DOIUrl":"https://doi.org/10.1109/WISA.2014.41","url":null,"abstract":"The data placement issues in cloud computing platform have been extensively researched, mainly in the choice of the number of data replica, initial data placement strategy, the run-time and dynamically adjustment and routing algorithm and other aspects of the transaction request. In this paper, we design a whole framework to the method of placing data replica for analysis and description from a higher level. We then briefly describe a method based on workload, and propose a cloud computing data replica dynamic scheduling mechanism, while in our main contribution propose a set of novel dynamic adaptive data replica placement techniques to achieve higher scalability, fault tolerance and increased variation and ability to cope with the workload. Experimental results show that our approach in data replica placement can significantly reduce the frequency of distributed transactions.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132279784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pengfei Lei, Yukun Li, Pengcheng Tian, Li Liu, Dexin Zhao
{"title":"A Method of Same Name Disambiguation towards Literature Search","authors":"Pengfei Lei, Yukun Li, Pengcheng Tian, Li Liu, Dexin Zhao","doi":"10.1109/WISA.2014.16","DOIUrl":"https://doi.org/10.1109/WISA.2014.16","url":null,"abstract":"The issue that many authors share the same name results in many problems for paper search and the study of the Scientists Collaboration Network. In this paper, we study the problem of name ambiguities towards author-based search, propose a framework on same name disambiguation towards literatures searching, and propose a method to classify author-based searching results based on researchers of the real world. The experimental results show the effectiveness of the methods we proposed.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132889982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chong Kuang, Zhiyuan Liu, Maosong Sun, Feng Yu, Pengfei Ma
{"title":"Quantifying Chinese Happiness via Large-Scale Microblogging Data","authors":"Chong Kuang, Zhiyuan Liu, Maosong Sun, Feng Yu, Pengfei Ma","doi":"10.1109/WISA.2014.48","DOIUrl":"https://doi.org/10.1109/WISA.2014.48","url":null,"abstract":"Happiness is an important indicator to measure our life satisfaction. Microblogging data can reflect users' living standards and psychological state. We build a large lexicon based on the PERMA lexicon and large-scale microblogging data on Sina Weibo by combining the PMI and distrabutional similarty method. Using this lexicon, we propose a method to calculate the happiness quantitatively based on PERMA theory. Experiments shows that our method achieve significant improvement compared with the baseline in terms of AP and Bpref respectively. After the verification of our method on manual annotation dataset, we perform an in-depth analysis on large-scale microblogging data using our method.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125584758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Structure Learning of Large Scale Bayesian Network","authors":"Xiang Xu, Qing Liu, Yaping Li, Lin Xiao","doi":"10.1109/WISA.2014.35","DOIUrl":"https://doi.org/10.1109/WISA.2014.35","url":null,"abstract":"We improve the structure learning approach from several aspects to learn huge Bayesian network and propose network merging methods to get better result. This approach is applied to build mRNA-miRNA-cancer network by using dataset whose samples have both mRNAs and miRNAs expression data. We evaluate the learning approach and compare merging methods through experiments and evaluate the network we have learned. Experiments show that the gene interact relationship and even causal relationship can be revealed to get better understanding of the way they interact.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adding Lexical Chain to Keyphrase Extraction","authors":"Zefeng Li, Binlai He, Yangnan","doi":"10.1109/WISA.2014.53","DOIUrl":"https://doi.org/10.1109/WISA.2014.53","url":null,"abstract":"Key phrase extraction is widely used in information retrieval, automatic summarizing, text clustering, etc. KEA is a traditional and classical algorithm. But it mainly uses the statistical information and ignores the semantic information. In our paper, we propose a method which combine semantic information with KEA by constructing lexical chain that based on Reget's thesaurus. In this method, we use the semantic similarity between terms to construct lexical chain, and then the length of the chain will be used as a feature to build the extraction model. The experiment results attest that the performance of our system has an obvious improvement compare with the KEA and Nguyen and Kan's method.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128105872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hybrid Information Flow Analysis for Python Bytecode","authors":"Zhifei Chen, Lin Chen, Baowen Xu","doi":"10.1109/WISA.2014.26","DOIUrl":"https://doi.org/10.1109/WISA.2014.26","url":null,"abstract":"Python is widely used to create and manage complex, database-driven websites. However, due to dynamic features such as dynamic typing of variables, Python programs pose a serious security risk to web applications. Most security vulnerabilities result from the fact that unsafe data input reaches security-sensitive operations. To address this problem, information flow analysis for Python programs is proposed to enforce this property. Information flow can capture the fact that a particular value affects another value in the program. In this paper, we present a novel approach for analyzing information flow in Python byte code which is a low-level language and is more widely broadcast. Our approach performs a hybrid of static and dynamic control/data flow analysis. Static analysis is used to study implicit flow, while dynamic analysis efficiently tracks execution information and determines definition-use pair. To the best of our knowledge, it is the first one for Python byte code.","PeriodicalId":366169,"journal":{"name":"2014 11th Web Information System and Application Conference","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123697194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}