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2nd international Neural Network Society conference on Big data, INNS 2016

作者： Gu, Xiaowei Angelov, Plamen P. Gutierrez, German Iglesias, Jose Antonio Sanchis, Araceli Data Science Group School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom CAOS Research Group Computer Science Department Carlos III University of Madrid Leganes28911 Spain

ISBN: (纸本)9783319478975

In this paper, a novel online clustering approach called Parallel TEDA is introduced for processing high frequency streaming data. This newly proposed approach is developed within the recently introduced TEDA theory and inherits all advantages from it. In the proposed approach, a number of data stream processors are involved, which collaborate with each other efficiently to achieve parallel computation as well as a much higher processing speed. A fusion center is involved to gather the key information from the processors which work on chunks of the whole data stream and generate the overall output. The quality of the generated clusters is being monitored within the data processors all the time and stale clusters are being removed to ensure the correctness and timeliness of the overall clustering results. This, in turn, gives the proposed approach a stronger ability of handling shifts/drifts that may take place in live data streams. The numerical experiments performed with the proposed new approach Parallel TEDA on benchmark datasets present higher performance and faster processing speed when compared with the alternative well-known approaches. The processing speed has been demonstrated to fall exponentially with more data processors involved. This new online clustering approach is very suitable and promising for real-time high frequency streaming processing and data analytics. © Springer international Publishing AG 2017.

关键词： Clustering algorithms

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Multi-authority Distributed Attribute-Based Encryption with Application to Searchable Encryption on Lattices 2nd

Multi-authority Distributed Attribute-Based Encryption with ...

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2nd international conference on Cryptology and Malicious Security (Mycrypt)

作者： Kuchta, Veronika Markowitch, Olivier Univ Libre Bruxelles Dept Comp Sci Brussels Belgium

ISBN: (纸本)9783319612737;9783319612720

Many Internet users deploy several cloud services for storing sensitive data. Cloud services provide the opportunity to perform cheap and efficient storage techniques. In order to guarantee secrecy of uploaded data, users need first to encrypt it before uploading it to the cloud servers. There are also certain services which allow user to perform search operations according to certain attributes without revealing any information about the encrypted content. In the cryptographic community this service is known as the public key encryption with keyword search. In order to enable user control during performed search operations there exists an attribute-based encryption scheme that provides the required functionality. We introduce the first Key-Policy Multi-Authority AttributeBased Encryption (KP-MABE) on lattices assuming existence of multiple servers, where each of these servers contributes to the decryption process by computing decryption shares using its own secret share. Furthermore we construct a Key-Policy Distributed Attribute-Based Searchable Encryption (DABSE) which is based on lattices and use the introduced KP-MABE as a building block for the transformation to DABSE. We prove our scheme secure against chosen ciphertext attacks under the assumption that the underlying KP-MABE is secure under the hardness of learning with errors (LWE) problem.

关键词： Sensitive data

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Energy-Aware Topology Construction and Maintenance for Energy Harvesting Sensor Networks 2nd

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2nd international conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Sung, Tien-Wen Lee, Chao-Yang Lu, You-Te Shiu, Liang-Cheng Lin, Fu-Tian Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Peoples R China Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Peoples R China Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Southern Taiwan Univ Sci & Technol Dept Informat & Commun Tainan Taiwan Natl Pingtung Univ Dept Comp Sci & Informat Engn Pingtung Taiwan Tung Fang Design Univ Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9783319483085;9783319483078

This work designs a topology control approach which can stratify the perpetual energy supply to extend the system lifetime in energy harvesting sensor networks. Topology control is a well-known and energy-efficiency method that aims to reduce the energy consumption and prolong network lifetime in many research fields. The proposed perpetual and distributed topology control (PDTC) algorithm aims to make the harvesting ambient energy usefully and ensure network sustainability, and performs in each sensor which includes two phases, topology construction phase and topology maintenance phase. First, in topology construction phase, each sensor decides a most suitable parent node with maximal working time and adjusts the traffic generating rate to achieve the system sustainability. In the topology maintenance phase, this work adopts a topology maintenance algorithm to trigger the topology construction algorithm and then re-build a new network topology needed. The experimental results demonstrate the superiority of the PDTC algorithm in terms of energy efficient, network lifetime, and system sustainability.

关键词： Topology construction Topology maintenance Network lifetime Energy harvesting Sensor network

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A modified evidence theory model for data fusion

A modified evidence theory model for data fusion

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IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Jie Liu Dayou Liu Xinhua Wang Ni Ni Yungang Zhu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University Changchun China State Key Laboratory of Applied Optics Chinese Academy of Sciences Changchun China Bejing Entry-Exit Inspection and Quarantine Bureau Beijing China

ISBN: (纸本)9781509045006

To overcome the drawbacks of traditional convex evidence, in this paper we proposed a modified convex evidence theory model, we presented the modified combination function and use it to combine mass function of ordered propositions, we present the calculation of the parameters of the proposed combination function, and proposed a more accurate method to find the proposition which is most likely true. The theoretical analysis and experimental results demonstrate that the proposed method has higher accuracy than traditional convex evidence.

关键词： inference mechanisms sensor fusion uncertainty handling soft computing Inference mechanisms Sensor fusion data fusion Evidence Theory Convexity mass function Proposition data integration

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An Approach to Identify Travel Hotspots at Night Based on Mobile Phone data 2

An Approach to Identify Travel Hotspots at Night Based on Mo...

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2nd international conference on Information Technology and Intelligent Transportation Systems (ITITS)

作者： Dai, Guanchen Li, Linchao Zhang, Jian Zhang, Xiaoli Lei, Yi Yang, Zhongyue Peng, Qiuying Southeast Univ Jiangsu Key Lab Urban ITS Si Pai Lou 2 Nanjing 210096 Jiangsu Peoples R China Jiangsu Prov Collaborat Innovat Ctr Technol & App Si Pai Lou 2 Nanjing 210096 Jiangsu Peoples R China Southeast Univ Sch Transportat Si Pai Lou 2 Nanjing 210096 Jiangsu Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban Nanjing Jiangsu Peoples R China Hohai Univ Coll Civil & Transportat Engn Nanjing Jiangsu Peoples R China Shanghai Meihui Software Co Ltd Shanghai Peoples R China Jiangsu Expressway Network Operat & Management Ct Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781614997856;9781614997849

To improve the urban transport planning and safety management, an approach is proposed to identify travel hotspots based on mobile phone data. The workflow including data processing, movement state recognition, computing of travel density, identification of travel hotpots and visualization is described. A case study is presented to test the proposed approach. Moreover, the spatial distribution change of travel hotspots by time internal is analyzed. The location differences of travel hotspots on weekdays and weekends are also identified accurately with thematic map. The result shows that the hotspot identification can locate spatial clustering of trip activities effectively. It indicates that this approach is a valuable supplementary way to make up for the deficiency of traditional field surveys. It can provide some significant information for unban transport management and planning.

关键词： Origin-destination travel density spatial distribution significance analysis

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Composing High Event Impact Resistible Model by Interactive Artificial Bee Colony for the Foreign Exchange Rate Forecasting 2nd

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2nd international conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Tsai, Pei-Wei Yang, Li-Hui Zhang, Jing Zhang, Yong-Hui Chang, Jui-Fang Istanda, Vaci Coll Informat Sci & Engn Qingdao Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Peoples R China Natl Kaohsiung Univ Appl Sci Dept Int Business Kaohsiung Taiwan Council Indigenous Peoples Taipei Taiwan

ISBN: (纸本)9783319483085;9783319483078

Taiwan is an isolated island located in the south East Asia. Since Taiwan is lack of nature resources, thus, a huge part of the economy is export-oriented. To stimulate the economy to grow and activate the international trading, the Free Trading Agreement (FTA) is an activator to allow larger quantity of trading over the world. The foreign exchange rate plays the major role affecting the trade surplus in the export-oriented economic system. Hence, a stable and accurate foreign exchange rate forecasting model is important for the economic activity participants. In this paper, the event study method is used to examine 3 international trading related events including the Economic Cooperation Framework Agreement (ECFA), the Taiwan-Japan Bilateral Investment Arrangement (BIA), and the Agreement between Singapore and the Separate Customs Territory of Taiwan, Penghu, Kinmen and Matsu on Economic Partnership (ASTEP) signed between Taiwan and other participants. The foreign exchange rate forecasting models are built by the time-series methods and the computational intelligence method, namely, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH), the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), and the Interactive Artificial Bee Colony (IABC), respectively. In the event study, the observation period is chosen to include 70 days for both pre/post-event. The Mean Absolutely Percentage Error (MAPE) value is used to examine the forecasting accuracy of the models. The experimental results indicate that the IABC constructed foreign exchange rate forecasting model is the most capable one to resist the impact caused by the specific events. In other words, the impact results in more significant forecasting error in the GARCH and the EGARCH models, but not in the IABC model.

关键词： GARCH EGARCH IABC Foreign exchange rate Event study

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Smart data integration by goal driven ontology learning 2nd

Smart data integration by goal driven ontology learning

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2nd international Neural Network Society conference on Big data, INNS 2016

作者： Chen, Jingliang Dosyn, Dmytro Lytvyn, Vasyl Sachenko, Anatoliy State Key Lab of Software Engineering Computer School Wuhan University Wuhan China Computer School Hubei University of Technology Wuhan China Lviv National Polytechnic University 12 Bandery Street Lviv79013 Ukraine Silesian University of Technology ul. Roosevelta 26-28 Zabrze41-800 Poland Ternopil National Economic University 3 Peremogy Square Ternopil46020 Ukraine

ISBN: (纸本)9783319478975

The smart data integration approach is proposed to compose data and knowledge of the different nature, origin, formats and standards. This approach is based on the selective goal driven ontology learning. The automated planning paradigm in a combination with a value of the perfect information approach is proposed to be used for evaluating the knowledge correspondence with the learning goal for the data integration domain. The information model of a document is represented as a supplement to the Partially Observable Markov Decision Process (POMDP) strategy of a domain. It helps to estimate the document a pertinence as the increment of the strategy expected utility. A statistical method for identifying the semantic relations in the natural language texts for their linguistic characteristics is developed. It helps to extract the Ontology Web Language (OWL) predicates from the natural language text using data about sub semantic links. A set of methods and means based on ontology learning was developed to support the smart data integration process. A technology uses the Natural Language Processing software Link Grammar Parser, WordNet Application Programming Interface (API) as well as the OWL API. © Springer international Publishing AG 2017.

关键词： Application programs

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Diagnostic Analysis of Breast Cancer in Non-Cancer History Patients Using Fuzzy Mapreduce Framework

Diagnostic Analysis of Breast Cancer in Non-Cancer History P...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： K.R Vaishnavi K Bhargavi Vadla Saisupriya K. Vidya S Soumya Department of Computer Science and Engineering Siddaganga Institute of Technology Tumkur Karnataka India

ISBN: (纸本)9781538620458

Breast cancer is most commonly occurring cancer among women of all ages and surprisingly in recent days it is found that women with no cancer history are being diagnosed with breast cancer. A lot of breast cancer related data is available on the web but it involves uncertainty. So, there is a need to use noise proof computing techniques to draw proper inferences about the disease. In this paper, fuzzy soft computing approach combined with the MapReduce framework is used to handle noisy data about cancer on web in order to precisely recognise the occurrence of breast cancer in non-cancer history patients at early stages.

关键词： Breast cancer Uncertainty History Detectors Fuzzy logic

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An energy-aware bi-level optimization model for multi-job scheduling problems under cloud computing

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soft computing 2016年第1期20卷 303-317页

作者： Wang, Xiaoli Wang, Yuping Cui, Yue Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

Recently, how to reduce huge energy consumption of data centers has caught wide attention in cloud computing. One effective way is to improve the energy efficiency of servers. To achieve this goal, we propose a new energy-aware multi-job scheduling model based on MapReduce in this paper. In the proposed model, first, the variation of energy consumption with the performance of servers is taken into account;second, since network bandwidth is a relatively limited resource in cloud computing, 100 % data locality is guaranteed;last but not least, considering that task-scheduling strategies depend directly on data placement policies, we formulate the problem as an integer bi-level programming model. It is worth noticing that there are usually tens of thousands of tasks to be scheduled in the cloud, so this is a large-scale optimization problem. In order to solve it efficiently, a local search operator is specifically designed, based on which, a bi-level genetic algorithm is proposed in this paper. Finally, numerical experiments indicate the effectiveness of the proposed model and algorithm.

关键词： Energy efficiency data locality Bi-level programming Multi-job scheduling Cloud computing

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Accelerating Redis with RDMA Over InfiniBand 2nd

Accelerating Redis with RDMA Over InfiniBand

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2nd international conference on data Mining and Big data (DMBD)

作者： Tang, Wenhui Lu, Yutong Xiao, Nong Liu, Fang Chen, Zhiguang Natl Univ Def Technol State Key Lab High Performance Comp Changsha Hunan Peoples R China

ISBN: (纸本)9783319618456;9783319618449

Redis is an open source high-performance in-memory key-value database supporting data persistence. Redis maintains all of the data sets and intermediate results in the main memory, using periodical persistence operations to write data onto the hard disk and guarantee the persistence of data. InfiniBand is usually used in high-performance computing domains because of its very high throughput and very low latency. Using RDMA technology over InfiniBand can efficiently improve network-communication's performance, increasing throughput and reducing network latency while reducing CPU utilization. In this paper, we propose a novel RDMA based design of Redis, using RDMA technology to accelerate Redis, helping Redis show a superior performance. The optimized Redis not only supports the socket based conventional network communication but also supports RDMA based high-performance network communication. In the high-performance network communication module of optimized Redis, Redis clients write their requests to the Redis server by using RDMA writes over an Unreliable Connection and the Redis server uses RDMA SEnd over an Unreliable datagram to send responses to Redis clients. The performance evaluation of our novel design reveals that when the size of key is fixed at 16 bytes and the size of value is 3 KB, the average latency of SET operations of RDMA based Redis is between 53 vs and 56 vs. This is about two times faster than IPoIB based Redis. And we also present a dynamic Registered Memory Region allocation method to avoid memory waste.

关键词： Hybrid communication InfiniBand RDMA Redis Big data

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