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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhao, Tong Zhao, H. Vicky King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Electrical and Computer Engineering Department University of Alberta EdmontonAB Canada

ISBN: (纸本)9781450337946

The popularity of Online Social Networks (OSNs) has attracted great research interests in different fields. In Economics, researchers use game theory to analyze the mechanism of network formation, which is called Network Formation Game. While in computer science, much effort has been done in building machine learning models to predict future or missing links. However, there are few works considering how to combine game theoretic analysis and machine learning models. Therefore, in this paper, we study the problem of Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks. Our goal is to improve link recommendation accuracy via leveraging the power of Network Formation Games into machine learning models. We present two different approaches to solve this problem. First, we propose a three-phase method that straightforwardly combines game theoretic analysis with machine learning models. Second, we develop a unified model, BPRLGT, that incorporates Network Formation Game into a Bayesian ranking framework for link recommendation. Specifically, BPRLGT takes advantage of network topology and we design a game theoretic sampling approach to improve its training process. The experiments are conducted on four real world datasets and the results on all datasets demonstrate that both our proposed three-phase method and the unified ranking model outperform the baseline methods. © 2015 ACM.

关键词： Game theory

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Virtual machines of high availability using hardware-assisted failure detection

Virtual machines of high availability using hardware-assiste...

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International Carnahan Conference on Security Technology

作者： Wei-Jen Wang Hung-Lin Huang Shan-Hao Chuang Shao-Jui Chen Chia Hung Kao Deron Liang Department of Computer Science and Information Engineering National Central University Taiwan Data Analytics Technology and Applications Institute Institute for Information Industry Taiwan

The virtualization technology has been widely used in today's doud computing datacenters. With the virtualization technology, each physical machine in a datacenter can be logically divided into several virtual machines, on which different types of software services can host. However, many reasons may decrease the availability of the whole system. For example, a failed physical machine automatically fails all virtual machines on the physical machine, and consequently fails every software service on the virtual machines. It is difficult to detect failures efficiently in a general-purpose computer architecture because the hardware cannot provide enough information for fast failure detection. On the contrary, the ATCA (Advanced Telecommunications Computing Architecture) physical machines provide high hardware availability, and support IPMI (Intelligent Platform Management Interface) that can quickly detect the hardware status. In this paper, we developed a novel failure model and designed a symmetric fault-tolerant mechanism using ATCA physical machines and KVM to provide a solution for high system availability. The proposed fault-tolerant mechanism divides ATCA physical machines into pairs, such that each machine of a pair supports fault tolerance for each other. Once a failure is detected in the physical machine layer or the virtualization layer, the failed virtual machines are then recovered on the other physical machine. We have compared the proposed fault-tolerance mechanism with another prior VM-based fault-tolerance tool. The results show that the proposed mechanism significantly reduces the service downtime. That is, it provides better system availability for software services running on the virtual machines.

关键词： Servers Blades Virtual machining Hardware Monitoring Software Temperature sensors

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pNFS-Based Software-Defined Storage for Information Lifecycle Management

pNFS-Based Software-Defined Storage for Information Lifecycl...

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International Conference on Cloud Computing and Big data (CCBD)

作者： Kuo Sheng Deng Chin Feng Lee Jerry Chou Yi Chen Shih Shang Hao Chuang Po Hsuan Wu Department of Computer Science National Tsing Hua University Hsinchu Taiwan Data Analytics Technology & Applications Research Institute Institute for Information Industry Taipei Taiwan

The data explosion brings several difficult challenges. One of critical and fundamental challenges is to have a storage infrastructure that can store and manage such voluminous information to meet business goals, such as cost, performance, reliability, and availability. To reduce the storage cost and improve data access performance, it is typically required a storage management solution based on Information Lifecycle Management (ILM). In this work, we propose a design of ILM system for managing a tiered storage using pNFS(Parallel NFS) file system. pNFS is an emerging NFSv4 extension that bypasses the server bottleneck by enabling direct access to storage by NFSv4 clients. Furthermore, because pNFS separates control and data flows, it enables a software-defined storage architecture. This paper describes our system design and shows the preliminary performance results in this work as a future roadmap to achieve our goal.

关键词： Layout Servers Protocols Performance evaluation Metadata Cost accounting Interoperability

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Information Management and Platform Refinement for Big data

Information Management and Platform Refinement for Big Data

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International Conference on Cloud Computing and Big data (CCBD)

作者： Joey J. Syu Kevin Z. Ji Hui-I Hsiao Po-Hung Lin Data Analytics Technology & Applications Research Institute Institute for Information Industry Taipei Taiwan Department of Computer Science Trinity University San Antonio TX USA

Information management and related applications are being vigorously researched and developed. According to IDC research reports, the global market for information management is forecasted to grow by 30 percent annually (from a value of 8 billion US dollars in 2012, to a forecasted 24 billion US dollar value in 2016). Both reports explicitly identify the significant growth in the importance of information management and related services in the future. This paper will discuss and analyze our development in topic-specific applications, namely the collection, access, and enrichment of massive data. Through the development of adaptive file access technology and the combination of distributed storage (i.e. Hive, HBase, etc.) with In-Memory algorithmic technologies, we will be able to have the system adjust data storage policies and increase efficiency through the identification of data types and some algorithmic properties. Based on the virtual machine migration technology, we utilize the mass data processing workload to dynamically migrate the virtual computing node. This can automatically adjust virtual resources to achieve outstanding calculation and localization of data through parallel execution of multiple mass data management processes, and maximize massive data processing efficiency. In the future we plan to progressively seek potential clients and partners, from which we will tailor a variety of field tests according to our clients' needs. And through this process, is where we will gradually achieve technical and application verifications.

关键词： data mining Crawlers Thesauri HTML Terminology Distributed databases Big data

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Preserving privacy in collaborative Business Process Composition

Preserving privacy in collaborative Business Process Composi...

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International Joint Conference on e-Business and Telecommunications (ICETE)

作者： Hassaan Irshad Basit Shafiq Jaideep Vaidya Muhammad Ahmed Bashir Shafay Shamail Nabil Adam Department of Computer Science Lahore University of Management Sciences Lahore Pakistan Management Science and Information Systems Department Rutgers University Newark New Jersey U.S.A. Rutgers Institute for Data Science Learning and Applications Rutgers University Newark New Jersey U.S.A.

ISBN: (纸本)9781467385329

Collaborative business process composition exploits the knowledge of existing business processes of related organizations to compose an executable business process for a given organization based on its requirements and design specifications. Typically, this requires organizations to share and upload their existing business process execution sequences to a central repository. However, even after masking of confidential data, the execution sequences may still include sensitive business information which organizations may not want to share with their competitors. To address this issue, we develop a privacy-preserving Business Process Recommendation and Composition System (BPRCS), that generates a differentially private dataset of execution sequences which can be published and shared with other organizations for composition and implementation of their business processes. We also employ process mining and classification techniques on this differentially private dataset to regenerate the executable business process workflow. We experimentally validate the effectiveness of our approach.

关键词： Web services Organizations Collaboration Privacy data privacy

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Leveraging social connections to improve personalized ranking for collaborative filtering 14

Leveraging social connections to improve personalized rankin...

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Zhao, Tong McAuley, Julian King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science and Engineering UC San Diego San DiegoCA United States

ISBN: (纸本)9781450325981

Recommending products to users means estimating their preferences for certain items over others. This can be cast either as a problem of estimating the rating that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a ranking. Although collaborative-filtering approaches can be used to identify users who rate and rank products similarly, another source of data that informs us about users' preferences is their set of social connections. Both rating- and ranking-based paradigms are important in real-world recommendation settings, though rankings are especially important in settings where explicit feedback in the form of a numerical rating may not be available. Although many existing works have studied how social connections can be used to build better models for rating prediction, few have used social connections as a means to derive more accurate ranking-based models. Using social connections to better estimate users' rankings of products is the task we consider in this paper. We develop a model, SBPR (Social Bayesian Personalized Ranking), based on the simple observation that users tend to assign higher ranks to items that their friends prefer. We perform experiments on four real-world recommendation data sets, and show that SBPR outperforms alternatives in ranking prediction both in warm- and cold-start settings. Copyright 2014 ACM.

关键词： Collaborative filtering

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Computational neuroscience breakthroughs through innovative data management 1

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17th East-European Conference on Advances in databases and Information Systems, ADBIS 2013

作者： Tauheed, Farhan Nobari, Sadegh Biveinis, Laurynas Heinis, Thomas Ailamaki, Anastasia Data-Intensive Applications and Systems Lab. EPFL Switzerland Brain Mind Institute EPFL Switzerland National University of Singapore Singapore Singapore Department of Computer Science Aalborg University Denmark

ISBN: (数字)9783642406836

ISBN: (纸本)9783642406829

Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine-grained models (including subcellular details, e.g., neurotransmitter) of the neocortex to understand the mechanisms causing brain diseases and to test new treatments in-silico. The sheer size and, more importantly, the level of detail of their models challenges today's spatial data management techniques. In collaboration with the Blue Brain project (BBP) we develop new approaches that efficiently enable analysis, navigation and discovery in spatial models of the brain. More precisely, we develop an index for the scalable and efficient execution of spatial range queries supporting model building and analysis. Furthermore, we enable navigational access to the brain models, i.e., the execution of of series of range queries where he location of each query depends on the previous ones. To efficiently support navigational access, we develop a method that uses previous query results to prefetch spatial data with high accuracy and therefore speeds up navigation. Finally, to enable discovery based on the range queries, we conceive a novel in-memory spatial join. The methods we develop considerably outperform the state of the art, but more importantly, they enable the neuroscientists to scale to building, simulating and analyzing massively bigger and more detailed brain models. © 2013 Springer-Verlag.

关键词： Navigation

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Special issue on the best papers of the Conference on Intelligent data Understanding (CIDU 2010)

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Statistical Analysis and data Mining 2011年第4期4卷 355-357页

作者： Srivastava, Ashok N. Chawla, Nitesh V. Intelligent Systems Division Intelligent Data Understanding Group NASA Ames Research Center Moffett Field CA United States Department of Computer Science and Engineering Interdisciplinary Center for Network Science and Applications University of Notre Dame South Bend IN United States

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A soft computing approach for data mining based query processing using rough sets and genetic algorithms

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International Journal of Hybrid Intelligent Systems 2008年第1期5卷 1-17页

作者： Srinivasa, K.G. Venugopal, K.R. Patnaik, L.M. Department of Computer Science and Engineering Data Mining Laboratory M S Ramaiah Institute of Technology Bangalore – 560054 India University Visvesvaraya College of Engineering Bangalore – 560001 India Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

The optimization of queries is critical in database management systems and the complexity involved in finding optimal solutions has led to the development of heuristic approaches. Answering data mining query involves a random search over large databases. Due to the enormity of the data set involved, model simplification is necessary for quick answering of data mining queries. In this paper, we propose a hybrid model using rough sets and genetic algorithms for fast and efficient query answering. Rough sets are used to classify and summarize the datasets, whereas genetic algorithms are used for answering association related queries and feedback for adaptive classification. Here, we consider three types of queries, i.e., select, aggregate and classification based data mining queries. Summary tables that are built using rough sets and analytical model of attributes are used to speed up select queries. Mining associations, building concept hierarchies and reinforcement of reducts are achieved through genetic algorithms. The experiments are conducted on three real-life data sets, which include KDD 99 Cup data, Forest Cover-type data and Iris data. The performance of the proposed algorithm is analyzed for both execution time and classification accuracy and the results obtained are good.

关键词： Rough sets genetic algorithms query answering optimization

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Fingerprint Matching Algorithm Based on Tree Comparison using Ratios of Relational Distances

Fingerprint Matching Algorithm Based on Tree Comparison usin...

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International Conference on Availability, Reliability and Security, ARES

作者： Abinandhan Chandrasekaran Bhavani Thuraisingham Data and Applications Security Laboratory Department of Computer Science University of Texas Dallas USA

ISBN: (纸本)9780769527758

We present a fingerprint matching algorithm that initially identifies the candidate common unique (minutiae) points in both the base and the input images using ratios of relative distances as the comparing function. A tree like structure is then drawn connecting the common minutiae points from bottom up in both the base and the input images. Matching score is obtained by comparing the similarity of the two tree structures based on a threshold value. We define a new term called the 'M(i)-tuple' for each minutiae point which uniquely encodes details about the local surrounding region, where i = 1 to N, and N is the number of minutiae. The proposed algorithm requires no explicit alignment of the two to-be compared fingerprint images and also tolerates distortions caused by spurious minutiae points. The algorithm is also capable of comparing and producing matching scores between two images obtained from two different kinds of sensors, hence is sensor interoperable and also reduces the FNMR in cases where there is very little overlap region between the base and the input image. We conducted evaluations on the FVC-2000 datasets and have summarized the results in the concluding section

关键词： Fingerprint recognition Biometrics Image sensors Biosensors Pattern matching Image matching Biomedical optical imaging Optical distortion Optical sensors Solid state circuits

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