Data management and analysis : case studies in education, healthcare and beyond /: case studies in education, healthcare and beyond. (2020)

• Record Type: Book
• Further Information: Note: Reda Alhajj, Mohammad Moshirpour, Behrouz Far, editors.
• Other Names: Alhajj, Reda; Moshirpour, Mohammad; Far, Behrouz H, 1959-
• Contents: Intro -- Preface -- Contents -- Leveraging Protection and Efficiency of Query Answering in Heterogenous RDF Data Using Blockchain -- 1 Introduction -- 2 Challenges in Semantic Data Integration -- 2.1 Addressing Data Security Issues -- 2.2 Addressing Accuracy and Quality of Data Issues -- 2.3 Addressing Data Operation and Data Access Issues -- 3 Study Design and Experiment Setup -- 3.1 Ontology in Plant Science -- 3.2 Tools and Implementation of Ontology -- 3.3 Ontology Evaluation -- 4 View Layer on Semantic Data Integration Using Distributed Ledger Technology 4.1 The Architecture of the View Layer -- 5 Conclusion -- References -- Big Data Analytics of Twitter Data and Its Application for Physician Assistants: Who Is Talking About Your Profession in Twitter? -- 1 Introduction -- 2 Background and Related Works -- 2.1 Data Analytics on Facebook for Discovery of Most Interactive Friends of Users -- 2.2 Background on Physician Assistants -- 2.3 Data Analytics on Twitter -- 3 Our Data Science Solution -- 3.1 Data Extraction -- 3.2 Data Filtering -- 3.3 Data Analysis -- 4 Evaluation on Real-Life Twitter Data About Physician Assistants -- 5 Conclusions Deliverables -- Group Project -- Contributions -- Part 1: Topic Approval -- Part 2: Written Report -- Part 3: Presentation -- References -- Homogeneous Vs. Heterogeneous Distributed Data Clustering: A Taxonomy -- 1 Introduction -- 2 Clustering Analysis -- 2.1 Properties of Clustering Methods -- 2.1.1 Partitional Vs. Hierarchical -- 2.1.2 Hard Vs. Fuzzy -- 2.1.3 Distance Vs. Density -- 2.1.4 Deterministic Vs. Stochastic -- 2.2 Similarity Measures -- 2.3 The Clustering Performance Measures -- 3 Distributed Data Clustering -- 3.1 Distributed Networks -- 3.2 Local and Global Clustering Models 3.3 Distributed Clustering Architectures -- 4 Taxonomy of Distributed Clustering -- 4.1 Homogeneous Distributed Clustering -- 4.1.1 All-Nodes-Global Model: Distributed-Program -- 4.1.2 All-Nodes-Global Model: Distributed-Task -- 4.1.3 Facilitator-Global Model: Single-Local Model -- 4.1.4 Facilitator-Global Model: Multiple-Local Models -- 4.2 Heterogeneous Distributed Clustering -- 4.2.1 Intermediate Cooperation -- 4.2.2 End-Result Cooperation -- 5 Distributed Clustering Performance Measures -- 5.1 Execution Time -- 5.2 Speedup -- 5.3 The Efficiency -- 5.4 Isoefficiency Function
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource (261 pages)
• Subjects: 005.7; Big data; Data mining; Big data; Data mining; Electronic books
• Languages: English
• ISBNs: 9783030325879; 3030325873
• Related ISBNs: 9783030325862; 3030325865
• Notes: Note: References-An Introductory Multidisciplinary Data Science Course Incorporating Experiential Learning-1 Introduction-2 The Course-3 Student Feedback-4 Conclusion-Appendix 1-Assignment 1: Non-digital Data Visualizations-Contributions-Deliverables-Assignment 2: Data Collection-Contributions-Deliverables-Assignment 3: Data Cleaning-Contributions-Deliverables-Assignment 4: Qualitative Data Analysis-Contributions-Deliverables-Assignment 5: Digital Data Visualizations-Contributions-Deliverables-Appendix 2-Individual Project; Note: Print version record.
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