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Foreword; Preface; Contents; About the Editors; Big Data Analytics: Views from Statistical and Computational Perspectives; 1 Some Unique Characteristics of Big Data; 2 Computational versus Statistical Complexity; 3 Techniques to Cope with Big Data; 4 Conclusion; References; Massive Data Analysis: Tasks, Tools, Applications, and Challenges; 1 Introduction; 1.1 Motivation; 1.2 Big Data Overview; 1.3 Big Data Adoption; 1.4 The Chapter Structure; 2 Big Data Analytics; 2.1 Descriptive Analytics; 2.2 Predictive Analytics; 2.3 Prescriptive Analytics; 3 Big Data Analytics Platforms; 3.1 MapReduce
3.2 Apache Hadoop3.3 Spark; 3.4 High Performance Computing Cluster; 4 Distributed Data Management Systems for Big Data Analytics; 4.1 Hadoop Distributed File System; 4.2 NoSQL Databases; 5 Examples of Massive Data Applications; 5.1 Recommendations in e-Commerce; 5.2 Link Prediction in Biomedical Literature; 6 Current Issues of Big Data Analytics; 6.1 Data Locality; 6.2 Fault-Tolerance of Big Data Applications; 6.3 Replication in Big Data; 6.4 Big Data Security; 6.5 Data Heterogeneity; 7 Summary and Discussion; References; Statistical Challenges with Big Data in Management Science
1 Introduction2 Big Data; 3 Statistical Challenges; 3.1 Volume; 3.2 Velocity; 3.3 Variety; 3.4 Veracity; 3.5 Privacy and Confidentiality; 4 Statistical Methods for Big Data; 4.1 Symbolic Data Analysis; 4.2 Approximate Stream Regression; 5 Case Studies; 5.1 Online Recommendations for Cross Selling; 5.2 Talent Acquisition; 6 Concluding Remarks; References; Application of Mixture Models to Large Datasets; 1 Introduction; 2 Finite Mixture Models; 3 Factor Models; 4 Dimension Reduction; 4.1 EMMIX-GENE; 4.2 Projection Methods; 4.3 Matrix Factorization; 5 Flow Cytometry Data
5.1 Non-elliptical Clusters5.2 FLAME Procedure; 5.3 JCM Procedure; 5.4 Clustering and Matching of Flow Cytometric Samples; 5.5 Classification of Flow Cytometric Samples; 6 Summary and Conclusions; References; An Efficient Partition-Repetition Approach in Clustering of Big Data; 1 Introduction; 2 Formulation of Big Data Clustering; 3 Principles of Big Data Clustering; 4 Literature Review; 5 Clustering Meta Algorithm/Partition-Repetition Approach; 5.1 Adaptive K-Means Clustering for Big Data; 5.2 Adaptation of Tight Clustering to Microarray Data; 6 Simulation Studies; 7 Concluding Remarks
6.3 Partition Results Using ADG Heuristic.
3.2 Apache Hadoop3.3 Spark; 3.4 High Performance Computing Cluster; 4 Distributed Data Management Systems for Big Data Analytics; 4.1 Hadoop Distributed File System; 4.2 NoSQL Databases; 5 Examples of Massive Data Applications; 5.1 Recommendations in e-Commerce; 5.2 Link Prediction in Biomedical Literature; 6 Current Issues of Big Data Analytics; 6.1 Data Locality; 6.2 Fault-Tolerance of Big Data Applications; 6.3 Replication in Big Data; 6.4 Big Data Security; 6.5 Data Heterogeneity; 7 Summary and Discussion; References; Statistical Challenges with Big Data in Management Science
1 Introduction2 Big Data; 3 Statistical Challenges; 3.1 Volume; 3.2 Velocity; 3.3 Variety; 3.4 Veracity; 3.5 Privacy and Confidentiality; 4 Statistical Methods for Big Data; 4.1 Symbolic Data Analysis; 4.2 Approximate Stream Regression; 5 Case Studies; 5.1 Online Recommendations for Cross Selling; 5.2 Talent Acquisition; 6 Concluding Remarks; References; Application of Mixture Models to Large Datasets; 1 Introduction; 2 Finite Mixture Models; 3 Factor Models; 4 Dimension Reduction; 4.1 EMMIX-GENE; 4.2 Projection Methods; 4.3 Matrix Factorization; 5 Flow Cytometry Data
5.1 Non-elliptical Clusters5.2 FLAME Procedure; 5.3 JCM Procedure; 5.4 Clustering and Matching of Flow Cytometric Samples; 5.5 Classification of Flow Cytometric Samples; 6 Summary and Conclusions; References; An Efficient Partition-Repetition Approach in Clustering of Big Data; 1 Introduction; 2 Formulation of Big Data Clustering; 3 Principles of Big Data Clustering; 4 Literature Review; 5 Clustering Meta Algorithm/Partition-Repetition Approach; 5.1 Adaptive K-Means Clustering for Big Data; 5.2 Adaptation of Tight Clustering to Microarray Data; 6 Simulation Studies; 7 Concluding Remarks
6.3 Partition Results Using ADG Heuristic.