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  • 1
    Online Resource
    Online Resource
    Social Informatics : 5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings (2013)
    Cham : Springer International Publishing
    Details
    ISBN: 9783319032603
    Language: English
    Pages: 1 Online-Ressource (508 pages)
    Series Statement: Lecture Notes in Computer Science Ser. v.8238
    Parallel Title: Print version Jatowt, Adam Social Informatics : 5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings
    DDC: 303.4833
    Keywords: Information technology-Social aspects-Congresses.. ; Information society-Social aspects-Congresses.. ; Online social networks-Congresses ; Electronic books
    Abstract: Preface -- Organization -- Table of Contents -- Modeling Analogies for Human-Centered Information Systems -- 1 Introduction -- 2 Towards Analogy-Enabled Information Systems -- 3 Adapting Semantics and Data Sources -- 3.1 Relational Databases -- 3.2 Linked Open Data and Ontologies -- 3.3 Unstructured Text and the Social Web -- 4 Conceptual Model for Analogies -- 4.1 System Design -- 4.2 Knowledge Base Primitives -- 4.3 Analogons, General Analogies and 4-Term Analogies -- 4.4 Semantics of Analogy Statements -- 4.5 Normalizing Analogies, Expansions, and Supportive Facts -- 4.6 Analogy Queries -- 5 Case Study: Mining Analogies from the Social Web -- 6 Summary and Outlook -- References -- Resilience of Social Networks under Different Attack Strategies -- 1 Introduction -- 2 Related Work -- 3 DataSets -- 4 Experimentation -- 5 Results and Discussion -- 6 Conclusion -- References -- Changing with Time: Modelling and Detecting User Lifecycle Periods in Online Community Platforms -- 1 Introduction -- 2 Related Work -- 3 Modelling User Lifecycles -- 3.1 Defining Lifecycle Periods -- 3.2 Modelling User Properties -- 3.3 Analysing User Lifecycles -- 4 Lifecycle Period Detection -- 4.1 Feature Engineering -- 4.2 Vector Space Detection Model -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Detection Results -- 6 Conclusions and Future Work -- References -- Metro: Exploring Participation in Public Events -- 1 Introduction -- 2 Exploring People and Events -- 2.1 Interface Structure -- 2.2 Recommendation Algorithm -- 2.3 Functionalities -- 3 Conclusions and Future Work -- References -- Follow My Friends This Friday! An Analysis of Human-Generated Friendship Recommendations -- 1 Introduction -- 2 Related Work -- 3 A Dataset of Broadcast Friend Recommendations -- 4 Analysis of Broadcast Recommendations -- 4.1 Effect of #FF Recommendation
    Abstract: 4.2 Repeated Recommendations -- 5 Recommender System -- 5.1 Features for Ranking Recommendations -- 5.2 EvaluationMethodology -- 5.3 Results -- 6 Conclusions -- References -- A Divide-and-Conquer Approach for Crowdsourced Data Enumeration -- 1 Introduction -- 2 Related Work -- 3 Microtask-Based Crowdsourcing -- 3.1 Microtask-Based Crowdsourcing Framework -- 3.2 Task Representation -- 4 Data Enumeration in the Microtask-Based Crowdsourcing Framework -- 4.1 Problems -- 5 ProposedMethod -- 5.1 Task Generation Plan -- 5.2 Task Generation Algorithm -- 6 Evaluation -- 6.1 Theoretical Analysis -- 6.2 Experimental Settings -- 6.3 Results of the Experiment -- 7 Summary -- References -- Social Listening for Customer Acquisition -- 1 Introduction -- 2 Architecture and Algorithm -- 2.1 Data Collection and Processing -- 2.2 Social Network Marketing -- 3 Demonstration Cases -- 4 Conclusions -- References -- Passive Participation in Communities of Practice: Scope and Motivations -- 1 Theoretical Background -- 1.1 Research Setting: Social Workers' Online Communities of Practice -- 2 Methodology -- 3 Passive Participation in Social Workers' Communities of Practice: Findings -- 3.1 Insecurity and Concerns of Criticism -- 3.2 Lack of Time and Motivation -- 3.3 Concern from Possible Lack of Feedback and Reciprocation -- 3.4 Reasons Related to Status and Expertise -- 3.5 Sense of Redundancy -- 3.6 Dislike of Technology -- 3.7 The Visual Aspect- "Not Facebook-ish Enough" -- 4 Discussion and Conclusions -- References -- An Ontology-Based Approach to Sentiment Classification of Mixed Opinions in Online Restaurant Reviews -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Datasets -- 3.2 Proposed Approach -- 4 Results -- 5 Discussion and Conclusion -- References -- A Novel Social Event Recommendation Method Based on Social and Collaborative Friendships
    Abstract: 1 Introduction -- 2 Related Work -- 3 The Proposed Event Recommendation Method -- 3.1 Acquaintance Identification -- 3.2 Recommendation Generation -- 4 Experiments -- 4.1 Dataset and Performance Metrics -- 4.2 System Component Evaluation -- 4.3 Comparison with Other Recommendation Methods -- 5 Concluding Remarks -- References -- Factors That Influence Social Networking Service Private Information Disclosure at Diverse Openness and Scopes -- 1 Introduction -- 2 Related Work -- 3 Openness Score and Explanatory Variables -- 4 Method -- 4.1 Data Collection Method Used for Public Data -- 4.2 Data Collection Method Used for Survey Data -- 5 Results -- 6 Discussions -- 7 Conclusion -- References -- An Approach to Building High-Quality Tag Hierarchies from Crowdsourced Taxonomic Tag Pairs -- 1 Introduction -- 2 Related Work -- 3 Tag Hierarchies Leaning from Taxonomic Tag Pairs -- 4 Empirical Study -- 4.1 Evaluation Methodology -- 5 Results and Analysis -- 5.1 Results of Semantic Evaluation -- 5.2 Results of Structural Evaluation -- 5.3 Results of Usability Evaluation -- 6 Discussion and Conclusion -- References -- Automating Credibility Assessment of Arabic News -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Crawler and Parser -- 3.2 Feature Extraction -- 3.3 Preprocessing -- 3.4 Classification -- 4 Results and Evaluation -- 4.1 Data Description -- 4.2 Results for "When?" -- 4.3 Results for "Source Info" -- 4.4 Credibility Score Per News Site -- 5 Conclusion -- References -- Polarity Detection of Foursquare Tips -- 1 Introduction -- 2 Related Work -- 3 Overview of Datasets -- 4 Polarity Detection -- 4.1 Supervised Methods -- 4.2 Unsupervised Method -- 5 Experimental Results -- 6 Conclusions and Future Work -- References -- The Study of Social Mechanisms of Organization, Boundary Capabilities, and Information System -- 1 Introduction
    Abstract: 2 Theory and Hypotheses -- 2.1 Boundary Capabilities -- 2.2 Organizational Mechanisms -- 2.3 Information System -- 3 Methodology -- 3.1 Data Collection Procedure -- 3.2 Measurements -- 4 Data Analysis and Results -- 4.1 Non-response Bias -- 4.2 Analysis of Measurement Model -- 4.3 Assessment of Structural Model -- 5 Discussion -- 5.1 Implications -- 5.2 Conclusion -- References -- Predicting User's Political Party Using Ideological Stances -- 1 Introduction -- 2 Related Work -- 3 Problem Setting -- 4 Solution -- 4.1 Ideological Stance Prediction -- 4.2 Party Prediction -- 5 Experiments -- 5.1 Dataset -- 5.2 Evaluation Criteria -- 5.3 Ideological Stance Prediction Experiments -- 5.4 Party Prediction Experiments -- 5.5 Discussion -- 6 Conclusion -- References -- A Fast Method for Detecting Communities from Tripartite Networks -- 1 Introduction -- 2 Related Works -- 2.1 Newman-Girvan Modularity -- 2.2 Tripartite Modularity -- 2.3 Modularity Optimization -- 3 OurMethod -- 3.1 Fast Unfolding for Edges -- 3.2 Merging Task 2 and Task 3 -- 4 Experiments on Synthetic Tripartite Networks -- 5 Experiment on a Real Tripartite Network -- 6 Conclusions -- References -- Predicting Social Density in Mass Events to Prevent Crowd Disasters -- 1 Human Crowd Density and Safety -- 2 Approximating Human Density - A Data Collection Approach -- 3 Towards the Prediction of Crowd Densities -- 3.1 A Human Crowd Density Flow Network -- 3.2 Prediction Approaches -- 3.3 Results -- 4 Future Work and Conclusion -- References -- Modeling Social Capital in Bureaucratic Hierarchy for Analyzing Promotion Decisions -- 1 Introduction -- 2 A Hybrid Mutliplex Network Model of Social Capital in Bureaucratic Career System -- 2.1 A Network Model of Superior-Subordinate Relationship -- 2.2 A Network Model of Bureaucratic Seniority -- 2.3 A Network Model of Career Distinction
    Abstract: 2.4 Social Capital Evaluation for Bureaucratic Promotion Decisions -- 3 Experimental Evaluation -- 4 Conclusions -- References -- Information vs Interaction: An Alternative User Ranking Model for Social Networks -- 1 Introduction -- 2 ProposedSolution -- 2.1 Preliminaries -- 2.2 Model -- 2.3 Inference -- 3 Empirical Evaluation -- 3.1 Data Set -- 3.2 Comparison with Related Algorithms -- 3.3 Retweet Behaviour Prediction -- 4 Related Work -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Feature Extraction and Summarization of Recipes Using Flow Graph -- 1 Introduction -- 2 Feature Types and Their Importances of Recipe -- 3 Pre-processing for Recipes -- 3.1 Recipe Tree: The Work-Flow Format of a Recipe -- 3.2 Tree Mapping Algorithms -- 4 Generation of General Recipe Tree -- 4.1 Framework -- 4.2 Transformation to Ordered Tree -- 4.3 Node-to-Node Mapping between Two Trees -- 4.4 Recipe Tree Integration -- 4.5 Characteristic Feature Extraction -- 5 Experiments and Results -- 5.1 Recipe Data Set -- 5.2 Examples of Transformation to Ordered Trees -- 5.3 Node-to-node Mapping -- 5.4 Recipe Tree Integration -- 5.5 General Recipe Tree of Ten Recipes -- 5.6 Characteristic Features of Each Recipe -- 6 Discussions -- 7 Conclusions -- References -- Unsupervised Opinion Targets Expansion and Modification Relation Identification for Microblog Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 3.1 Topic-Specific Target Expansion -- 3.2 Modification Relation Identification -- 3.3 Opinion Score Estimation -- 4 Experiments and Discussions -- 4.1 The Effects of Named Entity Recognition -- 4.2 The Effects of Co-occurrence Analysis -- 4.3 The Effects of Synonym Finding -- 4.4 Comparing with Content Classification -- 5 Conclusion -- References -- Pilot Study toward Realizing Social Effect in O2O Commerce Services
    Abstract: 1 Introduction
    URL: Volltext  (lizenzpflichtig)
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  • 2
    Online Resource
    Online Resource
    VIVO : a semantic approach to scholarly networking and discovery (2012)
    [San Rafael] : Morgan & Claypool
    Details
    ISBN: 9781608459940
    Language: English
    Pages: 1 Online-Ressource (xvii, 160 Seiten) , Illustrationen
    Edition: Also available in print
    Series Statement: Synthesis lectures on the semantic web, theory and technology #2
    Series Statement: Synthesis lectures on the semantic web: theory and technology
    Parallel Title: Erscheint auch als VIVO
    DDC: 302.30285
    Keywords: VIVO (Computer file) ; Scholars Databases ; Web applications ; Research Databases ; Online social networks ; Universities and colleges Research ; Databases ; Semantic Web
    Abstract: The world of scholarship is changing rapidly. Increasing demands on scholars, the growing size and complexity of questions and problems to be addressed, and advances in sophistication of data collection, analysis, and presentation require new approaches to scholarship. A ubiquitous, open information infrastructure for scholarship, consisting of linked open data, open-source software tools, and a community committed to sustainability are emerging to meet the needs of scholars today. This book provides an introduction to VIVO, http://vivoweb.org/, a tool for representing information about research and researchers--their scholarly works, research interests, and organizational relationships. VIVO provides an expressive ontology, tools for managing the ontology, and a platform for using the ontology to create and manage linked open data for scholarship and discovery. Begun as a project at Cornell and further developed by an NIH funded consortium, VIVO is now being established as an open-source project with community participation from around the world. By the end of 2012, over 20 countries and 50 organizations will provide information in VIVO format on more than one million researchers and research staff, including publications, research resources, events, funding, courses taught, and other scholarly activity. The rapid growth of VIVO and of VIVO-compatible data sources speaks to the fundamental need to transform scholarship for the 21st century
    Abstract: 1. Scholarly networking needs and desires / Michael Conlon -- 1.1 The world of the scholar today -- 1.2 Research discovery and expert identification -- 1.3 The semantic web -- 1.4 VIVO: a semantic web information infrastructure for scholarship -- 1.5 How VIVO addresses the needs of the scholar today and tomorrow -- References --
    Abstract: 2. The VIVO ontology / Jon Corson-Rikert ... [et al.] -- 2.1 Introduction -- 2.2 Semantic technologies -- 2.2.1 Rationale for using semantic standards -- 2.2.2 RDF and OWL -- 2.2.3 Linked data -- 2.2.4 Features of semantic modeling -- 2.3 Design of the ontology -- 2.3.1 Goals -- 2.3.2 Independence -- 2.3.3 The class hierarchy -- 2.3.4 Modeling principles -- 2.4 Relationship to the application -- 2.4.1 Ontology as data model -- 2.4.2 Reasoning -- 2.4.3 Common identifiers for shared individuals -- 2.4.4 External controlled vocabulary references -- 2.4.5 Migrating instance data -- 2.4.6 Integrated ontology editor -- 2.5 Extending the ontology -- 2.5.1 Modeling guidelines -- 2.5.2 Case studies -- 2.6 VIVO ontology community effort -- 2.7 Looking ahead -- 2.7.1 International partnerships -- 2.7.2 Future directions -- References --
    Abstract: 3. Implementing VIVO and filling it with life / Valerie Davis ... [et al.] -- 3.1 Preparation for implementation -- 3.1.1 Create your project plan -- 3.1.2 Create your one-pager -- 3.2 The importance of stakeholders -- 3.2.1 Identifying stakeholders -- 3.2.2 What motivates stakeholders? -- 3.2.3 Engaging stakeholders -- 3.3 Identifying sources and negotiating data access -- 3.4 Filling VIVO with life -- 3.4.1 Manual editing -- 3.4.2 Automated ingest -- 3.4.3 Tools for ingest -- 3.4.4 Updating data -- 3.5 Making VIVO a local success -- 3.5.1 Outreach and marketing to community -- 3.5.2 Value-added services sharing data -- 3.5.3 Theme elements and customization of interface -- 3.5.4 Conclusion and general tips for success -- References --
    Abstract: 4. Case study : University of Colorado Boulder / Liz Tomich and Alex Viggio -- 4.1 How CU-Boulder chose VIVO -- 4.2 Faculty demographics and reporting tools at CU-Boulder -- 4.2.1 FIS applications -- 4.2.2 FIS team -- 4.3 Implementation strategy -- 4.4 Source data from enterprise systems rather than building new profiles -- 4.5 Our technical environment and getting work done -- 4.5.1 Values and principles -- 4.5.2 Process -- 4.5.3 Practices -- 4.5.4 Tools -- 4.5.5 It partners and VIVO hosting -- 4.5.6 Continuous improvement -- 4.6 Challenges -- 4.7 The current and future value of VIVO to the CU-Boulder campus -- 4.8 Contributing to the VIVO community -- 4.8.1 National implementation support -- 4.8.2 University of Colorado semantic web incubator -- 4.8.3 Implementation workshop --
    Abstract: 5. Case study : Weill Cornell Medical College / Paul J. Albert ... [et al.] -- 5.1 Multi-institutional environment -- 5.2 Legacy researcher profiling system -- 5.2.1 Advantages of the legacy system as compared with VIVO -- 5.2.2 Shortcomings of the legacy system as compared with VIVO -- 5.3 Preparation for ingest -- 5.3.1 Setting up the environment -- 5.3.2 Negotiating with system owners and operators -- 5.3.3 Finding and cleaning authoritative data -- 5.3.4 Manual entry vs. automated ingest -- 5.4 Policies and procedures -- 5.4.1 Inaccurate data -- 5.4.2 Sensitive data -- 5.4.3 Preference data -- 5.4.4 What constitutes a minimally populated profile -- 5.4.5 Inclusion criteria -- 5.4.6 Representing data across institutions -- 5.4.7 Federated authentication -- 5.5 Data ingest -- 5.5.1 Data mapping and modeling -- 5.5.2 Harvester and google refine + VIVO -- 5.5.3 Publications metadata -- 5.5.4 Testing -- 5.6 SPARQL query builder -- 5.7 Custom extensions to the ontology -- 5.8 Successes -- 5.9 Select remaining issues -- 5.9.1 Clinical specialty and expertise -- 5.9.2 Self-editing -- 5.9.3 Transition to operations --
    Abstract: 6. Extending VIVO / Chris Barnes ... [et al.] -- 6.1 VIVO application overview: functions, components, and tools -- 6.1.1 Key application functions -- 6.1.2 VIVO open-source components -- 6.1.3 Tools developed for VIVO -- 6.2 VIVO application architecture -- 6.2.1 Vitro -- 6.2.2 VIVO: the first extension -- 6.3 Typical VIVO modifications -- 6.3.1 Theme changes -- 6.3.2 Ontology extensions -- 6.3.3 Custom list views -- 6.3.4 Custom templates -- 6.3.5 Menus and pages -- 6.3.6 Logging activity in VIVO -- 6.3.7 Alternative authentication protocols -- 6.3.8 Extensions achieved through VIVO mini-grants -- 6.3.9 Modularity -- 6.4 Tools for data -- 6.4.1 VIVO harvester -- 6.4.2 Data sharing and reuse -- 6.4.3 VIVO multi-institutional search -- 6.5 The VIVO open-source community -- References--
    Abstract: 7. Analyzing and visualizing VIVO data / Chintan Tank ... [et al.] -- 7.1 Visualization design philosophy and development goals -- 7.1.1 User friendly and informative -- 7.1.2 Gracefully degrading -- 7.1.3 Modular and robust software -- 7.1.4 Extendible and well-documented software -- 7.2 Social network visualizations -- 7.2.1 Sparkline -- 7.2.2 Temporal graph -- 7.2.3 Map of science -- 7.2.4 Network visualization -- 7.3 VIVO visualization system architecture -- 7.3.1 Front-end visualization libraries -- 7.3.2 Client-server architecture -- 7.3.3 Client-server architecture in action -- 7.4 Accessing, mining, and visualizing VIVO data -- 7.4.1 Data provided by VIVO visualizations -- 7.4.2 Data access and visualization using visualization templates -- 7.4.3 Data retrieval via SPARQL queries or dumps -- 7.5 Insightful visualizations of IRN data -- 7.5.1 Collaboration patterns for medical institutions -- 7.5.2 Top MeSH disease concepts appearing in PubMed publications -- 7.5.3 Identification of collaboration networks in support of funding proposals -- 7.5.4 Inter-institutional collaboration explorer -- 7.6 Discussion and outlook -- 7.6.1 Open social containers and gadgets -- 7.6.2 Federated search and visualizations -- References --
    Abstract: 8. The future of VIVO : growing the community / Dean B. Krafft ... [et al.] -- 8.1 Introduction -- 8.2 Upcoming research and development -- 8.2.1 Developing the VIVO application -- 8.2.2 Supporting VIVO collaboration and discovery networks -- 8.3 Integrating VIVO into the researcher ecosystem -- 8.4 Encouraging adoption -- 8.5 Creating an open-source community -- 8.6 A standard for exchanging information about researchers -- 8.7 Summary: VIVO's challenges and opportunities --
    Abstract: Appendix A: VIVO ontology classes, object properties, and data type properties -- Authors' biographies
    Description / Table of Contents: Preface; Structure of the Book; Acknowledgments; Scholarly Networking Needs and Desires; The World of the Scholar Today; Research Discovery and Expert Identification; The Semantic Web; VIVO: A Semantic Web Information Infrastructure for Scholarship; How VIVO Addresses the Needs of the Scholar Today and Tomorrow; References; The VIVO Ontology; Introduction; Semantic Technologies; Rationale for Using Semantic Standards; RDF and OWL; Linked Data; Features of Semantic Modeling; Design of the Ontology; Goals; Independence; The Class Hierarchy; Modeling Principles; Relationship to the Application
    Description / Table of Contents: Ontology as Data ModelReasoning; Common Identifiers for Shared Individuals; External Controlled Vocabulary References; Migrating Instance Data; Integrated Ontology Editor; Extending the Ontology; Modeling Guidelines; Case Studies; VIVO Ontology Community-Effort; Looking Ahead; International Partnerships; Future Directions; References; Implementing VIVO and Filling It with Life; Preparation for Implementation; Create Your Project Plan; Create Your One-Pager; The Importance of Stakeholders; Identifying Stakeholders; What Motivates Stakeholders?; Engaging Stakeholders
    Description / Table of Contents: Identifying Sources and Negotiating Data AccessFilling VIVO with Life; Manual Editing; Automated Ingest; Tools for Ingest; Updating Data; Making VIVO a Local Success; Outreach and Marketing to Community; Value-Added Services Sharing Data; Theme Elements and Customization of Interface; Conclusion and General Tips for Success; References; Case Study: University of Colorado Boulder; How CU-Boulder Chose VIVO; Faculty Demographics and Reporting Tools at CU-Boulder; FIS Applications; FIS Team; Implementation Strategy; Source Data from Enterprise Systems Rather than Building New Profiles
    Description / Table of Contents: Our Technical Environment and Getting Work DoneValues and Principles; Process; Practices; Tools; IT Partners and VIVO Hosting; Continuous Improvement; Challenges; The Current and Future Value of VIVO to the CU-Boulder Campus; Contributing to the VIVO Community; National Implementation Support; University of Colorado Semantic Web Incubator; Implementation Workshop; Case Study: Weill Cornell Medical College; Multi-institutional Environment; Legacy Researcher Profiling System; Advantages of the Legacy System as Compared with VIVO; Shortcomings of the Legacy System as Compared with VIVO
    Description / Table of Contents: Preparation for IngestSetting Up the Environment; Negotiating with System Owners and Operators; Finding and Cleaning Authoritative Data; Manual Entry vs. Automated Ingest; Policies and Procedures; Inaccurate Data; Sensitive Data; Preference Data; What Constitutes a Minimally Populated Profile; Inclusion Criteria; Representing Data Across Institutions; Federated Authentication; Data Ingest; Data Mapping and Modeling; Harvester and Google Refine+VIVO; Publications Metadata; Testing; SPARQL Query Builder; Custom Extensions to the Ontology; Successes; Select Remaining Issues
    Description / Table of Contents: Clinical Specialty and Expertise
    Note: Part of: Synthesis digital library of engineering and computer science , Includes bibliographical references , Abstract freely available; full-text restricted to subscribers or individual document purchasers , 1. Scholarly networking needs and desires , 6. Extending VIVO , 1.1 The world of the scholar today ; 1.2 Research discovery and expert identification ; 1.3 The semantic web ; 1.4 VIVO: a semantic web information infrastructure for scholarship ; 1.5 How VIVO addresses the needs of the scholar today and tomorrow ; References ; 2. The VIVO ontology , 2.1 Introduction ; 2.2 Semantic technologies ; 2.2.1 Rationale for using semantic standards ; 2.2.2 RDF and OWL ; 2.2.3 Linked data ; 2.2.4 Features of semantic modeling ; 2.3 Design of the ontology ; 2.3.1 Goals ; 2.3.2 Independence ; 2.3.3 The class hierarchy ; 2.3.4 Modeling principles ; 2.4 Relationship to the application ; 2.4.1 Ontology as data model ; 2.4.2 Reasoning ; 2.4.3 Common identifiers for shared individuals ; 2.4.4 External controlled vocabulary references ; 2.4.5 Migrating instance data ; 2.4.6 Integrated ontology editor ; 2.5 Extending the ontology ; 2.5.1 Modeling guidelines ; 2.5.2 Case studies ; 2.6 VIVO ontology community effort ; 2.7 Looking ahead ; 2.7.1 International partnerships ; 2.7.2 Future directions ; References ; 3. Implementing VIVO and filling it with life , 3.1 Preparation for implementation ; 3.1.1 Create your project plan ; 3.1.2 Create your one-pager ; 3.2 The importance of stakeholders ; 3.2.1 Identifying stakeholders ; 3.2.2 What motivates stakeholders? ; 3.2.3 Engaging stakeholders ; 3.3 Identifying sources and negotiating data access ; 3.4 Filling VIVO with life ; 3.4.1 Manual editing ; 3.4.2 Automated ingest ; 3.4.3 Tools for ingest ; 3.4.4 Updating data ; 3.5 Making VIVO a local success ; 3.5.1 Outreach and marketing to community ; 3.5.2 Value-added services sharing data ; 3.5.3 Theme elements and customization of interface ; 3.5.4 Conclusion and general tips for success ; References ; 4. Case study : University of Colorado Boulder , 4.1 How CU-Boulder chose VIVO ; 4.2 Faculty demographics and reporting tools at CU-Boulder ; 4.2.1 FIS applications ; 4.2.2 FIS team ; 4.3 Implementation strategy ; 4.4 Source data from enterprise systems rather than building new profiles ; 4.5 Our technical environment and getting work done ; 4.5.1 Values and principles ; 4.5.2 Process ; 4.5.3 Practices ; 4.5.4 Tools ; 4.5.5 It partners and VIVO hosting ; 4.5.6 Continuous improvement ; 4.6 Challenges ; 4.7 The current and future value of VIVO to the CU-Boulder campus ; 4.8 Contributing to the VIVO community ; 4.8.1 National implementation support ; 4.8.2 University of Colorado semantic web incubator ; 4.8.3 Implementation workshop ; 5. Case study : Weill Cornell Medical College , 5.1 Multi-institutional environment ; 5.2 Legacy researcher profiling system ; 5.2.1 Advantages of the legacy system as compared with VIVO ; 5.2.2 Shortcomings of the legacy system as compared with VIVO ; 5.3 Preparation for ingest ; 5.3.1 Setting up the environment ; 5.3.2 Negotiating with system owners and operators ; 5.3.3 Finding and cleaning authoritative data ; 5.3.4 Manual entry vs. automated ingest ; 5.4 Policies and procedures ; 5.4.1 Inaccurate data ; 5.4.2 Sensitive data ; 5.4.3 Preference data ; 5.4.4 What constitutes a minimally populated profile ; 5.4.5 Inclusion criteria ; 5.4.6 Representing data across institutions ; 5.4.7 Federated authentication ; 5.5 Data ingest ; 5.5.1 Data mapping and modeling ; 5.5.2 Harvester and google refine + VIVO ; 5.5.3 Publications metadata ; 5.5.4 Testing ; 5.6 SPARQL query builder ; 5.7 Custom extensions to the ontology ; 5.8 Successes ; 5.9 Select remaining issues ; 5.9.1 Clinical specialty and expertise ; 5.9.2 Self-editing ; 5.9.3 Transition to operations , 6.1 VIVO application overview: functions, components, and tools ; 6.1.1 Key application functions ; 6.1.2 VIVO open-source components ; 6.1.3 Tools developed for VIVO ; 6.2 VIVO application architecture ; 6.2.1 Vitro ; 6.2.2 VIVO: the first extension ; 6.3 Typical VIVO modifications ; 6.3.1 Theme changes ; 6.3.2 Ontology extensions ; 6.3.3 Custom list views ; 6.3.4 Custom templates ; 6.3.5 Menus and pages ; 6.3.6 Logging activity in VIVO ; 6.3.7 Alternative authentication protocols ; 6.3.8 Extensions achieved through VIVO mini-grants ; 6.3.9 Modularity ; 6.4 Tools for data ; 6.4.1 VIVO harvester ; 6.4.2 Data sharing and reuse ; 6.4.3 VIVO multi-institutional search ; 6.5 The VIVO open-source community ; References ; 7. Analyzing and visualizing VIVO data , 7.1 Visualization design philosophy and development goals ; 7.1.1 User friendly and informative ; 7.1.2 Gracefully degrading ; 7.1.3 Modular and robust software ; 7.1.4 Extendible and well-documented software ; 7.2 Social network visualizations ; 7.2.1 Sparkline ; 7.2.2 Temporal graph ; 7.2.3 Map of science ; 7.2.4 Network visualization ; 7.3 VIVO visualization system architecture ; 7.3.1 Front-end visualization libraries ; 7.3.2 Client-server architecture ; 7.3.3 Client-server architecture in action ; 7.4 Accessing, mining, and visualizing VIVO data ; 7.4.1 Data provided by VIVO visualizations ; 7.4.2 Data access and visualization using visualization templates ; 7.4.3 Data retrieval via SPARQL queries or dumps ; 7.5 Insightful visualizations of IRN data ; 7.5.1 Collaboration patterns for medical institutions ; 7.5.2 Top MeSH disease concepts appearing in PubMed publications ; 7.5.3 Identification of collaboration networks in support of funding proposals ; 7.5.4 Inter-institutional collaboration explorer ; 7.6 Discussion and outlook ; 7.6.1 Open social containers and gadgets ; 7.6.2 Federated search and visualizations ; References ; 8. The future of VIVO : growing the community , 8.1 Introduction ; 8.2 Upcoming research and development ; 8.2.1 Developing the VIVO application ; 8.2.2 Supporting VIVO collaboration and discovery networks ; 8.3 Integrating VIVO into the researcher ecosystem ; 8.4 Encouraging adoption ; 8.5 Creating an open-source community ; 8.6 A standard for exchanging information about researchers ; 8.7 Summary: VIVO's challenges and opportunities ; Appendix A: VIVO ontology classes, object properties, and data type properties ; Authors' biographies. , Also available in print. , System requirements: Adobe Acrobat Reader. , Mode of access: World Wide Web.
    DOI: 10.2200/S00428ED1V01Y201207WEB002
    URL: Volltext
    URL: Volltext
    URL: Volltext
    URL: Volltext
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    WebSphere engineering : a practical guide for WebSphere support managers and senior consultants (2008)
    Upper Saddle River, N.J. : IBM Press/Pearson plc | Boston, Mass. :Safari Books Online,
    Details
    ISBN: 9780137012459 , 0137012454
    Language: English
    Pages: xxi, 336 p , ill. , 25 cm
    Parallel Title: Erscheint auch als
    Keywords: WebSphere ; Handbooks, manuals, etc ; Electronic books ; local
    Abstract: The Practical, End-to-End Guide to WebSphere® Infrastructure Engineering and Technical Management Companies depend on the IBM® WebSphere platform to deliver mission-critical Web applications and services and to provide the foundation for Service Oriented Architecture (SOA). To gain maximum value from WebSphere technologies, organizations must implement comprehensive, integrated best practices for managing their WebSphere infrastructures. In this book, one of the most experienced enterprise WebSphere support managers introduces those best practices and explains exactly how to make the most of them. Drawing on his tremendous real-world expertise, Ying Ding shows how to maximize the WebSphere platform's reliability, stability, scalability, and performance for large enterprise systems. You'll find insightful discussions of each option and strategy for managing WebSphere, including practical guidance on making the right tradeoffs for your environment. Whether you're a WebSphere administrator, developer, consultant, support manager, engineer, or architect, this book brings together the information you need to run your WebSphere infrastructure with maximum effectiveness and efficiency. Coverage includes Planning, hiring, training, funding, and building a world-class WebSphere engineering support organization Implementing tight standards and consistent, comprehensive processes for managing the entire WebSphere engineering life cycle Creating optimal testing environments, administering parallel testing pipelines, and managing testing workloads Empowering production support teams with knowledge, system privileges, and the right tools Managing production emergencies and critical situations: evaluating problem severity, mitigating customer experience, restoring service, performing post-problem resolution, and much more Maximizing the stability of large-scale interconnected WebSphere systems for composite applications Supporting WebSphere platforms that provide end-to-end SOA infrastructure Introduction 1 Chapter 1: Organization Models and Choices 7 Chapter 2: Building a World-Class WebSphere Team Through Hiring and Training 41 Chapter 3: WebSphere Operations Framework 75 Chapter 4: Engagement Challenges 93 Chapter 5: Server Build 121 Chapter 6: Functional and Integration Testing Environment Support 139 Chapter 7: Stress-Testing Environment Support 153 Chapter 8: Production Environment Support 171 Chapter 9: Managing a ...
    Note: Includes bibliographical references and index
    URL: https://learning.oreilly.com/library/view/-/9780137012459/?ar
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    Online Resource
    WebSphere eXtreme Scale Best Practices for Operation and Management (2011)
    [Erscheinungsort nicht ermittelbar] : IBM Redbooks | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (220 pages)
    Keywords: Electronic books ; local
    Abstract: This IBM® Redbooks® publication contains a summary of the leading practices for implementing and managing a WebSphere® eXtreme Scale installation. The information in this book is a result of years of experience that IBM has had in with production WebSphere eXtreme Scale implementations. The input was received from specialists, architects, and other practitioners who have participated in engagements around the world. The book provides a brief introduction to WebSphere eXtreme Scale and an overview of the architecture. It then provides advice about topology design, capacity planning and tuning, grid configuration, ObjectGrid and backing map plug-ins, application performance tips, and operations and monitoring. This book is written for a WebSphere eXtreme Scale-knowledgeable audience. Please note that the additional material referenced in Appendix B is not available from IBM.
    Note: Online resource; Title from title page (viewed August 10, 2011) , Mode of access: World Wide Web.
    URL: https://learning.oreilly.com/library/view/-/0738435929/?ar
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