Data management and analysis : case studies in education, healthcare and beyond /

Data management and analysis : case studies in education, healthcare and beyond /

Saved in:
Bibliographic Details
Imprint:Cham : Springer, 2020.
Description:1 online resource (261 pages)
Language:English
Series:Studies in Big Data ; v. 65
Studies in big data ; v. 65.
Subject:
Big data.
Data mining.
Big data.
Data mining.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12602912
Hidden Bibliographic Details
Other authors / contributors:Alhajj, Reda.
Moshirpour, Mohammad.
Far, Behrouz H., 1959-
ISBN:9783030325879
3030325873
9783030325862
3030325865
Notes:5.5 Distributed Messaging Cost
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
Print version record.
Summary:Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
Other form:Print version: Alhajj, Reda. Data Management and Analysis : Case Studies in Education, Healthcare and Beyond. Cham : Springer, ©2020 9783030325862
Standard no.:10.1007/978-3-030-32
Table of 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

Similar Items