Computer Information Systems and Industrial Management

The Open Library:

This book constitutes the proceedings of the 13th IFIP TC 8 International Conference on Computer Information Systems and Industrial Management, CISIM 2014, held in Ho Chi Minh City, Vietnam, in November 2014. The 60 paper presented in this volume were carefully reviewed and selected from 98 submissions. They are organized in topical sections named: algorithms; biometrics and biometrics applications; data analysis and information retrieval; industrial management and other applications; modelling and optimization; networking; pattern recognition and image processing; and various aspects of computer security.

Open Data:

Intro -- Preface -- Message from the Host University -- Organization -- Table of Contents -- Keynote Papers -- Processing Collective Knowledge - Conflict Resolution and Integration Aspects -- 1 Collective Knowledge -- 2 Inconsistency Aspect of Collective Knowledge -- 3 Integration Computing -- References -- How to Store and Process Big Data: Are Today's Databases Sufficient? -- 1 Introduction -- 2 Big Data and Big Analytics -- 3 Big Data Storage and Management -- 3.1 Relational DBMSs -- 3.2 Distributed File Systems and Hadoop Technologies -- 3.3 NoSQL Databases -- 3.4 Big Data Management Systems -- 3.5 NewSQL Databases -- 3.6 NoSQL with ACID Transactions -- 4 Challenges and Conclusions -- References -- Algorithms -- A New Contention Management Technique for Obstruction Free Transactional Memory -- 1 Introduction -- 2 Background -- 2.1 DSTM -- 2.2 ASTM -- 3 Proposed Scheme of Arbitration over a Resource between Two or More Competing Transactions -- 3.1 TM-Object Structure in Proposed OFTM -- 3.2 Nomenclature -- 3.3 Proposed Algorithm -- 3.4 Case Study -- 3.5 Comparison of the Proposed OFTM with DSTM and ASTM -- 4 Performance Evaluation -- 4.1 Metric of Evaluation -- 4.2 Performance Summary -- 5 Conclusion and Future Scope -- References -- Evolutionary Algorithm for Decision Tree Induction -- 1 Introduction -- 2 Decision Tree -- 3 Evolutionary Algorithm -- 4 The EVO-Tree Algorithm -- 4.1 Representation -- 4.2 Initial Population -- 4.3 Fitness Evaluation -- 4.4 Selection -- 4.5 Crossover -- 4.6 Mutation -- 4.7 Stopping Criteria -- 5 Experiments -- 5.1 Default Parameters -- 5.2 Datasets -- 5.3 Experimental Analysis and Results -- 6 Conclusion and Future Work -- References -- A New Method for Mining High Average Utility Itemsets -- 1 Introduction -- 2 Related Work -- 2.1 High Utility Itemset Mining -- 2.2 High Average-Utility Itemset Mining

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