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Proceedings of the Biannual World Automation Congress

作者： Nan Su Aiwu Shi Changnian Chen E Chen Yuxuan Wang Cloud Computing & Big Data Research Center Wuhan Textile University Wuhan China School of Computer Science and Technology Wuhan University of Technology Wuhan China

How to efficiently place virtual machines is an important issue in the cloud data center. Virtual machine placement method based on traditional heuristic algorithm costs long time to reach an optimal allocation so that it is difficult to meet the real production environment. To solve this problem, this paper proposes a virtual machine placement method based on improved simulated annealing algorithm (ISA). Firstly, resource utilization model of servers and dynamic placement model for virtual machines are introduced. Secondly, two threshold values and an optimized annealing process are presented in the ISA. Finally, the ISA-based allocation weight vector is computed. Experimental results show that the method determines virtual machine placement in a short time span, achieves load balance and improves resource utilization rate in the cloud data center, as well as reduces the number of active servers.

关键词： Virtual machining Servers Resource management Simulated annealing cloud computing Heuristic algorithms Algorithm design and analysis

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Secure searchable encryption:a survey

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Journal of Communications and Information Networks 2016年第4期1卷 52-65页

作者： WANG Yunling WANG Jianfeng CHEN Xiaofeng State Key Laboratory of Integrated Service Networks(ISN) Xidian UniversityXian 710071China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China

cloud computing facilitates convenient and on-demand network access to a centralized pool of ***,many users prefer to outsource data to the cloud in order to mitigate the burden of local ***,storing sensitive data on remote servers poses privacy challenges and is currently a source of ***(Searchable Encryption)is a positive way to protect users sensitive data,while preserving search ability on the server *** allows the server to search encrypted data without leaking information in plaintext *** two main branches of SE are SSE(Searchable Symmetric Encryption)and PEKS(Public key Encryption with Keyword Search).SSE allows only private key holders to produce ciphertexts and to create trapdoors for search,whereas PEKS enables a number of users who know the public key to produce ciphertexts but allows only the private key holder to create *** article surveys the two main techniques of SE:SSE and *** SE schemes are categorized and compared in terms of functionality,efficiency,and ***,we point out some valuable directions for future work on SE schemes.

关键词： cloud storage encrypted data searchable encryption searchable symmetric encryption public key encryption with keyword search.

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Energy efficient data center management under availability constraints 9

Energy efficient data center management under availability c...

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9th Annual IEEE International Systems Conference, SysCon 2015

作者： Sitaram, Dinkar Phalachandra, H.L. Gautham, S. Swathi, H.V. Tp, Sagar Center for Cloud Computing and Big Data Dept. CSE PES University Bangalore India

ISBN: (纸本)9781479959273

Due to increasing power consumption and cost, data center energy conservation has become increasingly important. As a result, a number of algorithms for optimizing compute, storage, and networking have been devised. However, these algorithms focus on job distribution based upon optimizing energy efficiency, and do not take availability into consideration. For example, if there are 3 servers, allocating workloads based only upon energy efficiency may result in consolidation of the workload into only 1 server, resulting in a single point of failure. This paper outlines the importance of availability and designs a hill climbing algorithm to prevent failure zone failure and hence also reduce the energy consumption by the datacenter. © 2015 IEEE.

关键词： Availability

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Yu-Hui Chang Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781509003914

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets.

关键词： Cybernetics Acceleration Time complexity Rough sets Heuristic algorithms data mining Algorithm design and analysis

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Flickr group recommendation via heterogeneous information networks 15

Flickr group recommendation via heterogeneous information ne...

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7th International Conference on Internet Multimedia computing and Service, ICIMCS 2015

作者： Wang, Yueyang Xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting College of Computer Science and Technology Zhejiang University Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS China

ISBN: (纸本)9781450335287

As an important characteristic of social media (i.e. Flickr or Facebook), user communities or groups are beginning to attract increasing attention. Most of the previous studies on group recommendation only consider a homogeneous relationship, such as the rating of users to groups in social network. In real world, each group has its textual descriptions (e.g. the "Sky & cloud" group in Flickr), and the describing words about a given group may mention particular entities as well as their belonging categories (e.g. "Nature"). In fact, the group recommendation can be conducted in a heterogeneous information network, where informative cues are in general multi-typed. Motivated by the assumption that different types of relationships in heterogeneous information network can be potentially used to boost the performance of the group recommendation. We propose to combine heterogeneous relationship information for users with implicit user feedback. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user-user similarity via heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation techniques. © 2015 ACM.

关键词： Non-negative matrix factorization

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Sentiment analysis of mixed language employing Hindi-English code switching

Sentiment analysis of mixed language employing Hindi-English...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Dinkar Sitaram Savitha Murthy Debraj Ray Devansh Sharma Kashyap Dhar Center for Cloud computing and Big Data (CCBD) PES University Bangalore

Sentiment analysis has emerged as one of the prominent research branches because of its endless usages and applications. Monitoring social media, forums, blogs and other online resources for customer reviews, product competition and survey responses to understand customer insight is of significant importance in business analytics. With the proliferation of informal user generated data online, the use of mixed language has become a common phenomenon. Mixed language arises through the use of linguistic code switching (LCS) or the practice of using more than one language in a single sentence. Such mixed language has rarely been a subject of sentiment analysis before. The lack of a clear grammatical structure renders the previous approaches to sentiment analysis ineffective for such text. In this paper, we propose a strategy to determine the sentiment of sentences written in a mixed language comprising of Hindi and English lexicons. Our technique can be used to analyze the sentiment of data belonging to any one of the source languages as well as the mixed language data. Grammatical transitions which are very common in mixed language have been taken into account during the sentiment analysis. We demonstrate the effectiveness of the proposed approach via case studies on social media data sets.

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Predicting the Next Move: Determining Mobile User Location using Semantic Information

Predicting the Next Move: Determining Mobile User Location u...

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International Conference on Advances in computing, Communications and Informatics

作者： Nitin Bhyri Gautham V. Kidiyoor Varun, S. K. Subramaniam Kalambur Dinkar Sitaram Chid Kollengode Center of Cloud Computing and Big Data PES Institute of technology DataXu

ISBN: (纸本)9781479987931

The location of a mobile user is used to deliver context sensitive information like advertisements and deals. Predicting the future possible locations of a mobile user can help target specific services. Nokia provided researchers with data collected from around 200 mobile users over a period of about 2 years for the purpose of research. Previous efforts have attempted either to predict the location of the user or the semantics associated with a location. In our work, we demonstrate that using a multi-level approach by predicting the semantics of a location and then the specific location within helps improve the accuracy of the prediction. We have shown that for certain semantic locations, the prediction accuracy can be as high as 90%.

关键词： Mobile User Semantic Location

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Energy efficient data center management under availability constraints

Energy efficient data center management under availability c...

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Annual IEEE Systems Conference

作者： Dinkar Sitaram H.L. Phalachandra Gautham S Swathi H V Sagar TP Dept. CSE PES University Center for Cloud Computing and Big Data Bangalore India Center for Cloud Computing and Big Data Dept. CSE PES University Bangalore India

关键词： Servers Algorithm design and analysis Resource management Redundancy Gravity Hardware Energy efficiency

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Parallel knowledge acquisition algorithm for big data using MapReduce

Parallel knowledge acquisition algorithm for big data using ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Min Xia Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781467372220

With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.

关键词： big data Hierarchical Rough Set Knowledge Acquisition MapReduce big data Information Extraction Decision tables

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Using hybrid Neural Network to address Chinese Named Entity Recognition

Using hybrid Neural Network to address Chinese Named Entity ...

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IEEE International Conference on cloud computing and Intelligence Systems (CCIS)

作者： Guoyu Wang Yongquan Cai Fujiang Ge Beijing University of Technology Beijing China Lenovo Cloud/Big Data and Intelligent Computing Lab China

Many machine learning methods have been applied on Named Entity Recognition (NER). Such methods generally build on a large manually-annotated training set. However, the training set is usually limited as human labeling is costly and time consuming. Compare to the training set, the unlabeled corpus is usually much bigger and contains rich information about language. In this paper, a hybrid Deep Neural Network (DNN) is proposed to take advantage of the implicit information embedded in the un-labeled corpus. The experiments show that F1-score is improved from 85% to 90% (person name), from 75% to 81% (location name), and from 74% to 78% (organization name), compared with Conditional Random Fields (CRFs).

关键词： Training Neurons Neural networks Semantics Computational modeling Hidden Markov models Dictionaries

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