06137nam 2200457 450 99650347040331620230417025442.03-031-23501-0(MiAaPQ)EBC7156603(Au-PeEL)EBL7156603(CKB)25657403700041(PPN)268651914(EXLCZ)992565740370004120230417d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierBig Data - BigData 2022 11th international conference, held as part of the Services Conference Federation, SCF 2022, Honolulu, HI, USA, December 10-14, 2022 : proceedings /edited by Bo Hu, [and three others]Cham, Switzerland :Springer,[2022]©20221 online resource (101 pages)Lecture Notes in Computer Science Ser. ;v.13730Print version: Hu, Bo Big Data - BigData 2022 Cham : Springer International Publishing AG,c2023 9783031235009 Includes bibliographical references and index.Intro -- Preface -- Organization -- Services Society -- Services Conference Federation (SCF) -- Contents -- A Massive Data Retrieval Method for Power Information Collection Systems -- 1 Introduction -- 2 Related Works -- 3 The Proposed Method for Massive Data Retrieval -- 3.1 The Proposed Method -- 3.2 Pseudocode -- 4 The Experiment -- 5 Conclusions -- References -- Research and Application of the Data Resource Directory System of the Aerospace Enterprise -- 1 Introduction -- 2 The Data Resource Directory System -- 2.1 Data Standard Design -- 2.2 Data Resource Inventory -- 2.3 Data Resource Directory Framework Construction -- 3 Application -- 3.1 Data Asset Management Platform -- 3.2 Data Resource Pool Construction -- 4 Conclusion -- Appendix A -- References -- Performance Analysis of Cross-Border Mergers and Acquisitions of Baiyuan Pants Industry in the Internet Era -- 1 Introduction -- 2 Overview of the Case of Baiyuan Pants Industry' Cross-Border M&amp -- A of Global Tesco -- 2.1 Motivation for Cross-Border M&amp -- A of the Baiyuan Pants Industry -- 2.2 The Process of Cross-Border M&amp -- A of the Baiyuan Pants Industry -- 3 Performance Analysis of Cross-Border M&amp -- A of the Baiyuan Pants Industry -- 4 Analysis of the Reasons for the Successful Cross-Border M&amp -- A of the Baiyuan Pants Industry -- 4.1 Building a New Development Pattern by Acquiring Complementary Assets -- 4.2 Existing Capabilities Have Been Expanded Through the Bundling and Reorganization of Resources -- 4.3 The Leverage Effect is Achieved Through the Deployment and Utilization of Resources -- 5 Research Conclusions and Management Implications -- References -- Topic Modeling in the ENRON Dataset -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Text Preprocessing -- 3.2 Topic Modeling -- 3.3 Term Frequency-Inverse Document Frequency (TF-IDF).4 Experiment Result -- 4.1 Analyzing ENRON Dataset with TF-IDF -- 4.2 Analyzing ENRON Dataset with LDA -- 5 Conclusion -- References -- Design of Multi-data Sources Based Forest Fire Monitoring and Early Warning System -- 1 Introduction -- 2 Related Works -- 3 Top-Level Design -- 3.1 Data Source -- 3.2 System Design Process -- 3.3 Functional Design -- 4 Algorithm Design -- 4.1 Forest Fire Point Satellite Monitoring -- 4.2 Forest Fire Point Video Monitoring -- 4.3 Inspectors Report Monitoring and Early Warning -- 5 Implementation and Discussion -- References -- Research on the Pricing Method of Power Data Assets -- 1 The Introduction -- 2 Research Status at Home and Abroad -- 2.1 Current Status of Foreign Research -- 2.2 Domestic Research Status -- 3 Data Asset Valuation Methods -- 3.1 Cost Method -- 3.2 Income Method -- 3.3 Market Law -- 4 Power Data Asset Pricing -- 4.1 Characteristics of Power Data Assets -- 4.2 Division of Power Data Assets -- 4.3 Pricing Principles -- 4.4 Pricing Method -- 5 Summary and Outlook -- References -- Research and Design on the Confirmation Method of Power Data Assets -- 1 The Introduction -- 2 Analysis of Power Data Assets -- 2.1 Power Data Asset Concept -- 2.2 Power Data Asset Characteristics -- 2.3 Classification of Power Data Assets -- 2.4 Power Data Asset Classification -- 2.5 Data Flow Process -- 3 Division of Property Rights of Power Data Assets -- 4 Power Data Assets Confirmation Scheme -- 4.1 Confirmation Principle -- 4.2 Power Data Assets Confirmation Scheme -- 5 Summary and Outlook -- References -- Big Data Technology in Real Estate Industry: Scenarios and Benefits -- 1 Introduction -- 2 Literature Review -- 2.1 Real Estate Big Data Types -- 2.2 Real Estate Big Data Used in Development and Management Decisions -- 2.3 Real Estate Big Data Used in Real Estate Policy Formulation -- 2.4 Comments.3 Opportunities and Dilemmas of Real Estate Big Data Application -- 3.1 Opportunities -- 3.2 Dilemmas -- 4 Innovative Scenarios for the Application of Big Data -- 4.1 "Internet+" Real Estate Marketing -- 4.2 Personalization of Real Estate -- 4.3 Digital Property Services -- 4.4 Upstream and Downstream Integration -- 5 Application Path of Big Data in Real Estate Industry -- 5.1 The Main Characteristics of Big Data Empowerment -- 5.2 Feasible Paths for Big Data to Empower the Real Estate Industry -- 6 Conclusion and Suggestion -- References -- Copyright and AI: Are Extant Laws Adequate? -- 1 Introduction -- 2 Protection of AI Creation by Existing Copyright System -- 3 Inadequate Protection of AI Under Copyright Law -- 4 Suggestions on Perfecting the Copyright Law Protection of AI -- 5 Conclusion -- References -- Author Index.Lecture Notes in Computer Science Ser.Big dataBig data.170Hu BoMiAaPQMiAaPQMiAaPQBOOK996503470403316Big Data - BigData 20223088627UNISA