LEADER 14247nam 22008775 450 001 996215642503316 005 20201107193708.0 010 $a3-319-22324-0 024 7 $a10.1007/978-3-319-22324-7 035 $a(CKB)3710000000454224 035 $a(SSID)ssj0001558487 035 $a(PQKBManifestationID)16183012 035 $a(PQKBTitleCode)TC0001558487 035 $a(PQKBWorkID)14819273 035 $a(PQKB)11156470 035 $a(DE-He213)978-3-319-22324-7 035 $a(MiAaPQ)EBC6302884 035 $a(MiAaPQ)EBC5586925 035 $a(Au-PeEL)EBL5586925 035 $a(OCoLC)915756385 035 $a(PPN)187688974 035 $a(EXLCZ)993710000000454224 100 $a20150729d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aDatabase Systems for Advanced Applications$b[electronic resource] $eDASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Hanoi, Vietnam, April 20-23, 2015, Revised Selected Papers /$fedited by An Liu, Yoshiharu Ishikawa, Tieyun Qian, Sarana Nutanong, Muhammad Aamir Cheema 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (XIV, 328 p. 99 illus.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v9052 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-22323-2 327 $aIntro -- Message from the Workshop Chairs -- Message from the Poster Chairs -- DASFAA 2015 Workshop Organizers -- DASFAA 2015 Posters Organizers -- Contents -- The Second International Workshop on Semantic Computing and Personalization (SeCoP) -- A Novel Method for Clustering Web Search Results with Wikipedia Disambiguation Pages -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Method -- 4.1 Construction of Clustering Structure -- 4.2 Assignment of Search Results -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Measurements -- 5.3 Results -- 6 Conclusions -- References -- Integrating Opinion Leader and User Preference for Recommendation -- 1 Introduction -- 2 Related Work -- 3 The OLrs method -- 3.1 Preliminaries -- 3.2 Identifying Opinion Leaders -- 3.3 Predicting -- 4 Evaluation -- 4.1 Data Acquisition -- 4.2 Evaluation Metrics -- 4.3 Experimental Settings -- 4.4 Results and Analysis -- 5 Conclusions and Future Works -- References -- Learning Trend Analysis and Prediction Based on Knowledge Tracing and Regression Analysis -- Abstract -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing Model -- 2.2 Post-Test Score Prediction -- 3 Problem Analysis -- 4 Algorithm Design -- 4.1 Course Structure Definition -- 4.2 Knowledge Tracing -- 4.3 Regression Modeling -- 4.4 Trend Analysis and Learning Prediction -- 5 Experiment Design and Results Analysis -- 5.1 Experiment Data Selection -- 5.2 Experiment Design -- 5.3 Results and Analysis -- 6 Conclusion -- References -- Intensive Maximum Entropy Model for Sentiment Classification of Short Text -- 1 Introduction -- 2 Related Work -- 3 Maximum Entropy Model via Intensive Feature Functions -- 3.1 Problem Definition -- 3.2 Intensive Maximum Entropy Model -- 3.3 Parameter Estimation -- 4 Experiments -- 4.1 Data Set -- 4.2 Influence of the Number of Iterations. 327 $a4.3 Comparison with Baselines -- 5 Conclusion -- References -- Maintaining Ranking Lists in Dynamic Virtual Environments -- 1 Introduction -- 2 Continuously Ranking List Maintenance -- 2.1 Preference Query by Global Ranking Lists -- 2.2 Preference Query by Brute Force -- 3 A Solution of CRL -- 3.1 Maintaining the Perceiving Relationships -- 3.2 Continuous Query Processing -- 3.3 Upper Bound the Interest Scores -- 3.4 Effective Size of Materialized Item Lists -- 4 Performance Evaluation -- 4.1 Experimental Settings -- 4.2 Results -- 5 Related Work -- 6 Conclusion -- References -- Knowledge Communication Analysis Based on Clustering and Association Rules Mining -- 1 Introduction -- 2 Related Work -- 3 Knowledge Communication Analysis Framework -- 3.1 Clustering Analysis -- 3.2 Association Rule Mining -- 4 Experiments -- 4.1 Data Set -- 4.2 Clustering Analysis for Knowledge Sources -- 4.3 Clustering Analysis for Knowledge Diffusion -- 4.4 Association Rule Mining for Knowledge Sources -- 4.5 Association Rule Mining for Knowledge Diffusion -- 5 Conclusion -- References -- Sentiment Detection of Short Text via Probabilistic Topic Modeling -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Detection -- 2.2 Short Text Classification -- 2.3 Latent Semantic Analysis -- 3 Short Text Sentiment Detection -- 3.1 Probabilistic Topic Modeling -- 3.2 Sentiment Detection with Topic-Based Similarity -- 4 Experiments -- 4.1 Data Set -- 4.2 Parameters -- 4.3 Results and Analysis -- 5 Conclusion -- References -- Schema Matching Based on Source Codes -- 1 Introduction -- 2 A General Framework for Schema Matching Based on Source Codes -- 2.1 Extracting Exterior Schemas -- 2.2 Evaluating the Quality of Matching -- 2.3 Finding the Optimal Mapping -- 3 Related Work -- 4 Conclusions -- References. 327 $aA Quota-Based Energy Consumption Management Method for Organizations Using Nash Bargaining Solution -- 1 Introduction -- 2 Energy Consumption Satisfaction Degree -- 3 The Proposed NBS-based Energy Allocation Scheme -- 4 Numerical Simulation -- 5 Conclusion -- References -- Entity Relation Mining in Large-Scale Data -- 1 Introduction -- 2 Related Works -- 3 The Proposed Framework -- 3.1 Acquiring the Entity-Relationship Patterns -- 3.2 Extracting the Entity-Relationship Pairs -- 3.3 Evaluation of the Candidate Relationship Pairs -- 4 Implement of Distributed System -- 5 Experiments -- 5.1 Dataset -- 5.2 Results of Comparison -- 6 Conclusion -- References -- A Collaborative Filtering Model for Personalized Retweeting Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering and Recommender Systems -- 2.2 Personalized Retweeting Prediction -- 3 Proposed Method -- 3.1 Latent Factor Model -- 3.2 Ranking -- 3.3 Decomposing Tweets -- 3.4 Attributes of Tweets in Latent Factor Model -- 3.5 Linear Combination of Latent Factors -- 4 Experiments -- 4.1 Data Set -- 4.2 Data Preprocess -- 4.3 Metrics -- 4.4 Comparison -- 4.5 Influence of Parameters -- 5 Conclusions -- References -- Finding Paraphrase Facts Based on Coordinate Relationships -- 1 Introduction -- 2 Related Work -- 2.1 Semantic Relation Extraction -- 2.2 Paraphrase Acquisition -- 3 Preliminaries -- 4 Basic Idea -- 5 Our Method -- 5.1 Template Extraction -- 5.2 Entity Tuple Extraction -- 5.3 The Mutual Reinforcement Algorithm -- 6 Evaluation -- 6.1 Experimental Setting -- 6.2 Results -- 7 Conclusion -- References -- The Second International Workshop on Big Data Management and Service (BDMS) -- Emergency Situation Awareness During Natural Disasters Using Density-Based Adaptive Spatiotemporal Clustering -- 1 Introduction -- 2 Related Work. 327 $a3 (, )-Density-based Adaptive Spatiotemporal Clusters -- 3.1 Data Model -- 3.2 Density-Based Spatiotemporal Adaptive Criteria -- 3.3 Definitions -- 3.4 (, )-Density-based Adaptive Spatiotemporal Cluster -- 4 Proposed Method -- 4.1 Concept and System Overview -- 4.2 Naive Bayes Classifier -- 4.3 Incremental Algorithm -- 5 Experimental Result -- 6 Conclusion -- References -- Distributed Data Managing in Health Care Social Network Based on Mobile P2P -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Workflow of Health Workers -- 2.2 Multi-Platform P2P Ad Hoc Network -- 2.3 Social Network and Distributed Data Storage -- 3 Results -- 4 Conclusions -- References -- Survey of MOOC Related Research -- 1 Introduction -- 2 Basic Research of Recent MOOC -- 3 MOOC Research Organizations -- 3.1 MOOC Research Organizations in Educational Area -- 3.2 MOOC Research Organizations in Computer Science Area -- 3.3 MOOC Research Organizations Combining both Education and Computer Science -- 4 Summary -- References -- Modeling Large Time Series for Efficient Approximate Query Processing -- 1 Introduction -- 2 Model-Based Database System Concept -- 3 Model Querying for Time Series Data -- 3.1 Model Construction -- 3.2 Query Computation over Models -- 4 Evaluation -- 4.1 AVG and SUM Queries -- 4.2 Histogram Query -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Personalized User Value Model and Its Application -- 1 Introduction -- 2 Related Works -- 3 Five-Dimension Personalized User Value Model -- 3.1 User Input-Output Ratio Model -- 3.2 User Behavior Value Model -- 3.3 User Net Present Value Model -- 3.4 Five-Dimension Personalized User Value Contribution -- 4 Experiments -- References -- Posters -- Flexible Aggregation on Heterogeneous Information Networks -- 1 Introduction -- 2 Preliminaries -- 3 Aggregation Algorithm -- 4 Experiments Evaluation. 327 $a5 Conclusions -- References -- Discovering Organized POI Groups in a City -- 1 Introduction -- 2 Problem and Approach -- 2.1 Definition of Hybrid Similarity -- 2.2 Algorithm for Discovering OPGs -- 2.3 Algorithm for Classification -- 3 Visualization -- 4 Conclusions -- References -- Multi-roles Affiliation Model for General User Profiling -- 1 Introduction -- 2 Model -- 3 Inference -- 3.1 Update of Link Factors -- 3.2 Update of Attributes Affiliation Graph A -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Needle in a Haystack: Max/Min Online Aggregation in the Cloud -- 1 Introduction -- 2 Related Work -- 3 Overview -- 4 Randomization of Blocks -- 5 Query Processing -- 5.1 Estimation of Max/Min Value -- 5.2 Error Correction -- 6 Max/Min Online Aggregation in the Cloud -- 7 Performance Evaluation -- 7.1 Experiment Overview -- 7.2 Performance over Real Data -- 7.3 Query Error -- 8 Conclusion -- References -- FFD-Index: An Efficient Indexing Scheme for Star Subgraph Matching on Large RDF Graphs -- 1 Introduction -- 2 FFD-Index and Query Processing -- 3 Verification -- 4 Experiments -- 5 Conclusion -- References -- Leveraging Interactive Knowledge and Unlabeled Data in Gender Classification with Co-training -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Gender Classification with Co-training -- 4 Experimentation -- 5 Conclusion -- References -- Interactive Gender Inference in Social Media -- Abstract -- 1 Introduction -- 2 Related Work -- 3 A Two-Stage Approach -- 3.1 Stage 1: Four-Category Classification -- 3.2 Stage 2: Global Label Optimization -- 4 Experimentation -- 5 Conclusion -- References -- Joint Sentiment and Emotion Classification with Integer Linear Programming -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Joint Sentiment and Emotion Classification -- 4 Experimentation. 327 $a5 Conclusion. 330 $aDASFAA is an annual international database conference, located in the Asia-Pacific region,which show cases state-of-the-art R & D activities in databases-terms and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry. DASFAA 2015 the 20th in the series, was held during April 20-23, 2015 in Hanoi, Vietnam. In this year, we carefully selected two workshops, each focusing on specific research issues that contribute to the main themes of the DASFAA conference. This volume contains the final versions of papers accepted for the two workshops: Second International Workshop on Semantic Computing and Personalization (SeCoP 2015); Second International Workshop on Big Data Management and Service (BDMS 2015); and a Poster Session. [All the workshops were selected via a public call-for-proposals process. The workshop organizers put a tremendous amount of effort into soliciting and - lecting papers with a balance of high quality, new ideas and new applications. We asked all workshops to follow a rigid paper selection process, including the procedure to ensure that any Program Committee members are excluded from the paper review process of any paper they are involved with. A requirement about the overall paper acceptance rate of no more than 50% was also imposed on all the workshops.]. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v9052 606 $aDatabase management 606 $aData mining 606 $aInformation storage and retrieval 606 $aApplication software 606 $aAlgorithms 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aDatabase management. 615 0$aData mining. 615 0$aInformation storage and retrieval. 615 0$aApplication software. 615 0$aAlgorithms. 615 14$aDatabase Management. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a005.74 702 $aLiu$b An$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aIshikawa$b Yoshiharu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aQian$b Tieyun$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNutanong$b Sarana$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCheema$b Muhammad Aamir$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996215642503316 996 $aDatabase Systems for Advanced Applications$9772450 997 $aUNISA