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Big data Framework for Analyzing Patents to Support Strategic R&D Planning

Big Data Framework for Analyzing Patents to Support Strategi...

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Wonchul Seo Namhyoung Kim Sungchul Choi Division of Systems Management and Engineering Pukyong National University Busan Republic of Korea Department of Industrial and Management Engineering Gachon University Republic of Korea

Patents are important technical documents that are widely used to support strategic research and development (R&D) planning. With the rapid evolution of Information Technology (IT), many researchers have attempted to analyze patent data using IT-based methods such as data mining. More recently, advancements in big data technologies have increased their suitability for analyzing large-scale patent data. In this paper, we propose a big data framework to process and analyze large-scale patent data. the proposed framework consists of four layers: an aggregator layer, a storage layer, an analysis layer, and an application layer. these layers are designed to collect patent data, store the collected data, analyze the data, and present the results. the primary objectives of the proposed framework are to provide a patent analysis service platform based on big data technologies, and to support strategic R&D planning for organizations. To demonstrate the feasibility of the proposed approach, we implement a Hadoop cluster based on the proposed framework and conduct a case study to analyze patents in the Quantum Dot technology field.

关键词： Patents Big data Planning Distributed databases Text mining

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A Novel Verifiable and Dynamic Fuzzy Keyword Search Scheme over Encrypted data in Cloud Computing 15

A Novel Verifiable and Dynamic Fuzzy Keyword Search Scheme o...

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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big data Science and Engineering / 14th IEEE Int symposium on Parallel and Distributed Proc with Applicat (IEEE Trustcom/BigdataSE/ISPA)

作者： Zhu, Xiaoyu Liu, Qin Wang, Guojun Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Univ Sch Comp Sci & Elect Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Univ Sch Comp Sci & Educ Software Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781509032051

In recent years, cloud computing becomes more and more popular. Users outsource large amount of encrypted documents to the cloud in order to avoid information leakage. Searchable encryption technique is a desirable service to enable users search on encrypted data. In most existing searchable encryption schemes, they only provide exact keyword search. Fuzzy keyword search improves the system usability because it allows users to make spelling errors or format inconsistencies. Besides, verifiable encryption schemes usually consider a semi-trusted server and verify the authenticity of the search results. However, the server may be malicious, which may modify/delete some encrypted files or forge erroneous results in order to save its storage space or computation ability. In this paper, we investigate the searchable encryption problem in the presence of a malicious server, the verifiable searchability is needed to provide users the ability to detect the potential misbehavior. We propose a verifiable and dynamic fuzzy keyword search (VDFS) scheme to offer secure fuzzy keyword search, update the outsourced document collection and verify the authenticity of the search result. Our scheme is proved universally composable (UC) security by rigorous security analysis.

关键词： Cloud computing Searchable encryption Malicious server Verifiable search Dynamic fuzzy search

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Saliency Detection Based on Multi-scale Image Features

Saliency Detection Based on Multi-scale Image Features

引用

IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Chaoqun Zheng Xiaozhi Zheng Guizhong Wang Shuo Tian Qiang Guo Department of Computer Science and Technology Shandong University of Finance and Economics Shandong Provincial Key Laboratory of Digital Media Technology Shangdong Linglong Tire Co. Ltd China Machinery Engineering Corporation

In this paper, a simple method is proposed for detecting salient regions by utilizing muti-scale features at a superpixel-based level. To guarantee the accuracy of the detected region, we make use of superpixel to form a new measurement, instead of using the level of pixel. Majority of saliency models consider visual features such as orientation, color, intensity and the importance of visual features has not yet been fully explored. Here, we take texture feature into consideration in particular, using the gray level co-occurrence matrix(GLCM) as a texture descriptor. Specifically, a new local smoothing filter based on GLCM is proposed to smooth image and remove some simple texture before getting GLCM. Experimental results demonstrate that our approach significantly outperforms some existing popular methods with full resolution and well-defined boundary.

关键词： Feature extraction visualization Image color analysis Computational modeling Smoothing methods Image resolution Image segmentation

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Communities Evolution analysis Based on Events in Dynamic Complex Network

Communities Evolution Analysis Based on Events in Dynamic Co...

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Yao Hong Cai Fu Qingfeng Huang Zhicun Fang JieHua Zeng Lansheng Han School of Computer Huazhong University of Science and Technology Wuhan China Network and Computation Center Huazhong University of Science and Technology Wuhan China

In the dynamic complex networks, the community structure is constantly changing, such as generation, maintaining, merger, die, fusion, etc. these changes will lead to the dynamic evolution of the structure and morphology in an entire community. Current static community division methods can not be well used to analyze the dynamic network evolution. In this paper, based on events in the community evolution, firstly three evolutionary relationships are defined, as well as the definition of community aggregation and community divergence. then based on the idea of time sheet and the overlapping community division algorithm, dynamic complex networks are divided. the whole evolution of the dynamic community is portrayed, through the comprehensive analysis of the evolution relationships of the community on the adjacent time sheet. And according to values of community aggregation and community divergence, a great polymerization or large dispersion will be found in the community evolution process, so major events hidden in the data can be revealed.

关键词： Heuristic algorithms Complex networks Correlation Partitioning algorithms Algorithm design and analysis Polymers Measurement

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the Construction and analysis of Baidu Baike Entry Network

The Construction and Analysis of Baidu Baike Entry Network

引用

IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Lei Wang Lingyu Xu Jie Yu Yunlan Xue Gaowei Zhang Xiang Yu Bofeng Zhang School of Computer Engineering and Science Shanghai University Shanghai

Network has become the key method to analysis the interacting targets. the Baidu Baike entry network was constructed by means of Baidu Baike entry related terms and term category attributes. And then according to the feature of the closely connection in communities and the sparsely connection between communities in organization structure, it can reveal the relationship between the different entries in Baidu Baike. Many experiments show that communities of the Baidu Baike entry network have many overlap relations which explain that many Baidu Baike entries have rich meanings. And it is also found size and edge of community have a relationship of law of power function, which provides a reference about studying whether communities still have a large number of overlapping relationships when the network scale expand.

关键词： Clustering methods Image edge detection Blogs Buildings Java Internet Measurement units

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Performance Optimization of the SSVD Collaborative Filtering Algorithm on MapReduce Architectures

Performance Optimization of the SSVD Collaborative Filtering...

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： ShiouCheng Yu Quey-Liang Kao Che-Rung Lee Department of Computer Science National Tsing Hua University HsinChu Taiwan

MapReduce that attends data locality and fault tolerance is one of the most successful programming paradigms for data intensive and high-throughput applications. While many researches focus on the system performance of MapReduce, the application level performance optimization techniques are seldom discussed. In this paper, we present strategies to optimize the performance of Stochastic Singular Value Decomposition Collaborative Filtering (SSVD-CF) algorithm on MapReduce architectures. We applied the performance optimization techniques from scientific computing, including BLAS 3 matrix operations, Tall and Skinny QR (TSQR) decomposition, tiled algorithm, and distributed cache, to the SSVD-CF computation. Experiments showed although the MapReduce architecture is different from traditional high performance computing environments, those techniques can be still very effective. Near eight times speedup can be achieved for the most time consuming job, and more than five times speedup can be obtained for the entire program for large datasets.

关键词： Matrix decomposition Collaboration Optimization Algorithm design and analysis Sparse matrices Computer architecture Programming

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analysis of Human Autonomic Nervous Activity Based on Big data Measurement via SmartPhone

Analysis of Human Autonomic Nervous Activity Based on Big Da...

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Makoto Komazawa Kenichi Itao Hiroyuki Kobayashi Zhiwei Luo Graduate School of System Informatics Kobe University Kobe Japan

this study used a large amount of autonomic nervous system data to investigate the relationship between autonomic nervous system and activity, day of the week, and region. data was measured via a heart rate variability analysis system that utilizes the camera of smartphones [8]. this system was developed by the authors during previous research. As for as the relationship between autonomic nervous system and activity, total power and sympathetic nervous activity were found to rise after waking, while during leisure time, it was found that total power rises and sympathetic nervous activity is inhibited. Concerning the relationship between autonomic nervous system and day of the week, it was found that total power decreases from the middle through the latter part of the week (namely, Wednesday, thursday, and Friday), while it rises on Saturday. What's more, sympathetic nervous activity is suppressed on Saturday. Regarding the relationship between autonomic nervous system and region, it was found that total power is lower in the Kanto region than in others. Regarding weather conditions, the results showed a trend in which a significant decrease was seen in sympathetic nervous activity the higher the temperature. In addition, a significant increase was seen in the sympathetic nervous system the higher the atmospheric pressure. Lastly, a significant decrease was seen in the sympathetic nervous system the more precipitation there was. these results accord with prior research and with human biological phenomena, and we were able to use big data to statistically demonstrate our hypothesis.

关键词： Autonomic nervous system Market research Atmospheric measurements Temperature measurement Big data Cameras Power measurement

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CDR Based Temporal-Spatial analysis of Anomalous Mobile Users

CDR Based Temporal-Spatial Analysis of Anomalous Mobile User...

引用

IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Zhen Wang Sihai Zhang China Telecom Corporation Limited Anhui Branch Information Center Dept. of Electronic Engineering and Information Science University of Science and Technology of China Hefei Anhui China

As the competition in mobile markets becomes fiercer, telecom operators have devoted themselves to serving subscribers better. Among large number of subscribers, there are some anomalous users who should be paid more attention to, such as those issuing a lot of unsolicited calls to normal subscribers or making calls to one single phone number continuously. Our study is based on the data of Call Detail Records(CDR) of 10,000 subscribers provided by a Chinese telecom operator, who are randomly sampled from a high-tech industry district for one month. In this paper, we distinguish the abnormal users from the normal ones using agglomerative hierarchical clustering, and divide the anomalous users into telecom marketers or frauds, mobile hot lines and testers. We find that all the anomalous users are distributed in urban areas. In addition, testers and telecom marketers or frauds have relatively weak mobility, and are located in the centralized areas. On the contrary, users being recognized as mobile hot lines show the characteristics of high mobility and relatively decentralized distribution in geographical aspects.

关键词： Telecommunications Mobile communication Indexes Mobile handsets Clustering algorithms Big data Robots

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visualization System for Human Mobility analysis 14

Visualization System for Human Mobility Analysis

引用

14th RoEduNet International Conference on Networking in Education and Research (RoEduNet NER)

作者： Draghici, Adriana Agiali, Taygun Chilipirca, Cristian Univ Politehn Bucuresti Comp Sci & Engn Dept Bucharest Romania

ISBN: (纸本)9781467381796

City-scale events, such as festivals, sport events, concerts gather thousands of people in different areas of a city. By installing large area Wi-Fi routers which record the presence of people using devices such as smartphones and tables, the density and the movements of the crowds can he estimated and classified. analysis of crowd data collected during such events leads to valuable insights and feedback both for the parties involved and for city planners and advertisers. In this paper we present a analysis and visualization system for improved understanding of density and movement information. We discuss real datasets constraints, data clean-up techniques and methods for estimating and classifying the crowd movements. these methods are implemented inside a framework that allows easy integration and analysis of mobility datasets. As proof of concept we present the analysis of a real dataset collected during a city scale experiment in Arnhem, the Netherlands.

关键词： pedestrian mobility crowd density estimation Wi-Fi hotspots

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data analysis of Distributed Application Platform Based on the R which Apply to Digital Library 14

Data Analysis of Distributed Application Platform Based on t...

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International symposium Distributed Computing and Applications Business Engineering and Science

作者： Wu, Ningbo Yang, Fan Guizhou Univ Finance & Econ Coll Informat Guiyang Peoples R China Guizhou Univ Finance & Econ Guiyang Peoples R China

ISBN: (纸本)9781467365932

Digital Library large data resource lack of analysis and use, in order to mining the value of big data resources, proposed platformization analysis and processing mode. By integrate R and Hadoop to construct distributed data analysis platform, many big data analytical can be decomposed into "large" and "small" data processing section, overcome before scheme puzzle on analytical of large dataset, improve the performance of data analysis, platform able to handle data analysis tasks.

关键词： component big data distributed data analysis

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