Auditing social media : a governance and risk guide

Multimedia data require specialised management techniques because the representations of colour, time, semantic concepts, and other underlying information can be drastically different from one another. This textbook on multimedia data management techniques gives a unified perspective on retrieval efficiency and effectiveness. It provides a comprehensive treatment, from basic to advanced concepts, that will be useful to readers of different levels, from advanced undergraduate and graduate students to researchers and to professionals. After introducing models for multimedia data (images, video, audio, text, and web) and for their features, such as colour, texture, shape, and time, the book presents data structures and algorithms that help store, index, cluster, classify, and access common data representations. The authors also introduce techniques, such as relevance feedback and collaborative filtering, for bridging the 'semantic gap' and present the applications of these to emerging topics, including web and social networking.

Author Notes

K. Seluk Candan is a Professor of Computer Science and Engineering at Arizona State University.
Maria Luisa Sapino is a Professor in the Department of Computer Science at the University of Torino.

Preface	p. ix
1 Introduction: Multimedia Applications and Data Management Requirements	p. 1
1.1 Heterogeneity	p. 1
1.2 Imprecision and Subjectivity	p. 8
1.3 Components of a Multimedia Database Management System	p. 12
1.4 Summary	p. 19
2 Models for Multimedia Data	p. 20
2.1 Overview of Traditional Data Models	p. 21
2.2 Multimedia Data Modeling	p. 32
2.3 Models of Media Features	p. 34
2.4 Multimedia Query Languages	p. 92
2.5 Summary	p. 98
3 Common Representations of Multimedia Features	p. 99
3.1 Vector Space Models	p. 99
3.2 Strings and Sequences	p. 109
3.3 Graphs and Trees	p. 111
3.4 Fuzzy Models	p. 115
3.5 Probabilistic Models	p. 123
3.6 Summary	p. 142
4 Feature Quality and Independence: Why and How?	p. 143
4.1 Dimensionality Curse	p. 144
4.2 Feature Selection	p. 145
4.3 Mapping from Distances to a Multidimensional Space	p. 167
4.4 Embedding Data from One Space into Another	p. 172
4.5 Summary	p. 180
5 Indexing, Search, and Retrieval of Sequences	p. 181
5.1 Inverted Files	p. 181
5.2 Signature Files	p. 184
5.3 Signature-and Inverted-File Hybrids	p. 190
5.4 Sequence Matching	p. 191
5.5 Approximate Sequence Matching	p. 195
5.6 Wildcard Symbols and Regular Expressions	p. 202
5.7 Multiple Sequence Matching and Filtering	p. 204
5.8 Summary	p. 206
6 Indexing, Search, and Retrieval of Graphs and Trees	p. 208
6.1 Graph Matching	p. 208
6.2 Tree Matching	p. 212
6.3 Link/Structure Analysis	p. 222
6.4 Summary	p. 233
7 Indexing, Search, and Retrieval of Vectors	p. 235
7.1 Space-Filling Curves	p. 238
7.2 Multidimensional Index Structures	p. 244
7.3 Summary	p. 270
8 Clustering Techniques	p. 271
8.1 Quality of a Clustering Scheme	p. 272
8.2 Graph-Based Clustering	p. 275
8.3 Iterative Methods	p. 280
8.4 Multiconstraint Partitioning	p. 286
8.5 Mixture Model Based Clustering	p. 287
8.6 Online Clustering with Dynamic Evidence	p. 288
8.7 Self-Organizing Maps	p. 290
8.8 Co-clustering	p. 292
8.9 Summary	p. 296
9 Classification	p. 297
9.1 Decision Tree Classification	p. 297
9.2 k-Nearest Neighbor Classifiers	p. 301
9.3 Support Vector Machines	p. 301
9.4 Rule-Based Classification	p. 308
9.5 Fuzzy Rule-Based Classification	p. 311
9.6 Bayesian Classifiers	p. 314
9.7 Hidden Markov Models	p. 316
9.8 Model Selection: Overfitting Revisited	p. 322
9.9 Boosting	p. 324
9.10 Summary	p. 326
10 Ranked Retrieval	p. 327
10.1 k-Nearest Objects Search	p. 328
10.2 Top-k Queries	p. 337
10.3 Skylines	p. 360
10.4 Optimization of Ranking Queries	p. 373
10.5 Summary	p. 379
11 Evaluation of Retrieval	p. 380
11.1 Precision and Recall	p. 381
11.2 Single-Valued Summaries of Precision and Recall	p. 381
11.3 Systems with Ranked Results	p. 383
11.4 Single-Valued Summaries of Precision-Recall Curve	p. 384
11.5 Evaluating Systems Using Ranked and Graded Ground Truths	p. 386
11.6 Novelty and Coverage	p. 390
11.7 Statistical Significance of Assessments	p. 390
11.8 Summary	p. 397
12 User Relevance Feedback and Collaborative Filtering	p. 398
12.1 Challenges in Interpreting the User Feedback	p. 400
12.2 Alternative Ways of Using the Collected Feedback in Query Processing	p. 401
12.3 Query Rewriting in Vector Space Models	p. 404
12.4 Relevance Feedback in Probabilistic Models	p. 404
12.5 Relevance Feedback in Probabilistic Language Modeling	p. 408
12.6 Pseudorelevance Feedback	p. 411
12.7 Feedback Decay	p. 411
12.8 Collaborative Filtering	p. 413
12.9 Summary	p. 425
Bibliography	p. 427
Index	p. 473

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