Data Management and Analysis

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.

Contenu

Chapter1: Leveraging protection and efficiency of query answering in heterogenous RDF data by a view layer.- Chapter2: Big Data Analytics of Healthcare Tweets for Opinion Mining on Physician Assistants.- Chapter3: An Introductory Multidisciplinary Data Science Course Incorporating Experiential Learning.- Chapter4: Homogenous Vs. Heterogeneous Distributed Data Clustering: A Taxonomy.- Chapter5: Order acceptance policy for make-to-order supply chain.- Chapter6: Importance of Data Analytics for Improving Teaching and Learning Methods.- Chapter7: Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach.- Chapter8: Face Reconstruction from profile to frontal evaluation of face recognition.- Chapter9: A Data Management Scheme for Micro-Level Modular Computation-intensive Programs in Big Data Platforms.- Chapter10: A Vertical Breadth-First Multilevel Path Algorithm to Find All Paths in a Graph.- Chapter11: Workflow Provenance for Big Data: From Modelling to Reporting.- Chapter12: A Perspective on Working with Data Curriculum Development.- Chapter13: Approaches for Early Detection of Glaucoma using Retinal Images: A Performance Analysis.- Chapter14: Binary Thermal Exchange Optimization for Feature Selection