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E-raamat: Urban Computing

(Microsoft Research Asia)
  • Formaat: EPUB+DRM
  • Sari: Information Systems
  • Ilmumisaeg: 12-Feb-2019
  • Kirjastus: MIT Press
  • Keel: eng
  • ISBN-13: 9780262350235
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Urban ComputingSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Information Systems
  • Ilmumisaeg: 12-Feb-2019
  • Kirjastus: MIT Press
  • Keel: eng
  • ISBN-13: 9780262350235
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An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications.Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using todays large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications.Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field. An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications.
Preface xiii
Acknowledgments xxi
I Concepts and Framework 1(112)
1 Overview
3(46)
1.1 Introduction
3(1)
1.2 Definition of Urban Computing
4(1)
1.3 General Framework
5(5)
1.3.1 Brief and Example
5(1)
1.3.2 Functions of Each Layer
5(5)
1.4 Key Urban-Computing Challenges
10(18)
1.4.1 Urban-Sensing Challenges
10(5)
1.4.2 Urban Data Management Challenges
15(2)
1.4.3 Urban Data Analytics Challenges
17(5)
1.4.4 Urban Service Challenges
22(6)
1.5 Urban Data
28(11)
1.5.1 Taxonomy of Urban Data
28(2)
1.5.2 Geographical Data
30(1)
1.5.3 Traffic Data on Road Networks
31(2)
1.5.4 Mobile Phone Data
33(1)
1.5.5 Commuting Data
34(1)
1.5.6 Environmental-Monitoring Data
35(2)
1.5.7 Social Network Data
37(1)
1.5.8 Energy
38(1)
1.5.9 Economy
39(1)
1.5.10 Health Care
39(1)
1.6 Public Datasets
39(2)
References
41(8)
2 Urban-Computing Applications
49(64)
2.1 Introduction
49(1)
2.2 Urban Computing for Urban Planning
49(9)
2.2.1 Gleaning Underlying Problems in Transportation Networks
49(3)
2.2.2 Discovering Functional Regions
52(2)
2.2.3 Detecting a City's Boundaries
54(1)
2.2.4 Facility and Resource Deployment
55(3)
2.3 Urban Computing for Transportation Systems
58(12)
2.3.1 Improving Driving Experiences
58(3)
2.3.2 Improving Taxi Services
61(4)
2.3.3 Improving Bus Services
65(1)
2.3.4 Subway Services
66(1)
2.3.5 Bike-Sharing Systems
67(3)
2.4 Urban Computing for the Environment
70(11)
2.4.1 Air Quality
71(4)
2.4.2 Noise Pollution
75(3)
2.4.3 Urban Water
78(3)
2.5 Urban Computing for Urban Energy Consumption
81(3)
2.5.1 Gas Consumption
81(2)
2.5.2 Electricity Consumption
83(1)
2.6 Urban Computing for Social Applications
84(4)
2.6.1 Concepts of Location-Based Social Networks
84(1)
2.6.2 Understanding Users in Location-Based Social Networks
85(1)
2.6.3 Location Recommendations
85(3)
2.7 Urban Computing for the Economy
88(3)
2.7.1 Location Selection for Businesses
88(1)
2.7.2 Optimizing Urban Logistics
89(2)
2.8 Urban Computing for Public Safety and Security
91(5)
2.8.1 Detecting Urban Anomalies
91(4)
2.8.2 Predicting the Flow of Crowds
95(1)
2.9 Summary
96(1)
References
97(16)
II Urban Sensing and Data Acquisition 113(42)
3 Urban Sensing
115(40)
3.1 Introduction
115(6)
3.1.1 Four Paradigms of Urban Sensing
115(4)
3.1.2 General Framework of Urban Sensing
119(2)
3.2 Sensor and Facility Deployment
121(12)
3.2.1 Finding Optimal Meeting Points
121(4)
3.2.2 Maximizing Coverage
125(4)
3.2.3 Learning-to-Rank Candidates
129(3)
3.2.4 Minimizing Uncertainty
132(1)
3.3 Human-Centric Urban Sensing
133(6)
3.3.1 Data Evaluation
134(2)
3.3.2 Participant Recruitment and Task Design
136(3)
3.4 Filling Missing Values
139(11)
3.4.1 Problem and Challenges
139(2)
3.4.2 Spatial Models
141(4)
3.4.3 Temporal Models
145(3)
3.4.4 Spatiotemporal Models
148(2)
3.5 Summary
150(1)
References
150(5)
III Urban Data Management 155(150)
4 Spatiotemporal Data Management
157(62)
4.1 Introduction
157(4)
4.1.1 Data Structures
157(1)
4.1.2 Queries
158(1)
4.1.3 Indexes
158(3)
4.1.4 Retrieval Algorithms
161(1)
4.2 Data Structures
161(6)
4.2.1 Point-Based Spatial Static Data
161(2)
4.2.2 Point-Based Spatial Time Series Data
163(1)
4.2.3 Point-Based Spatiotemporal Data
163(1)
4.2.4 Network-Based Spatial Static Data
164(1)
4.2.5 Network-Based Spatial Time Series Data
165(1)
4.2.6 Network-Based Spatiotemporal Data
165(2)
4.3 Spatial Data Management
167(13)
4.3.1 Grid-Based Spatial Index
168(1)
4.3.2 Quadtree-Based Spatial Index
169(3)
4.3.3 K-D Tree-Based Spatial Index
172(3)
4.3.4 R-Tree-Based Spatial Index
175(5)
4.4 Spatiotemporal Data Management
180(18)
4.4.1 Managing Spatial Static and Temporal Dynamic Data
180(3)
4.4.2 Moving-Object Databases
183(6)
4.4.3 Trajectory Data Management
189(9)
4.5 Hybrid Indexes for Managing Multiple Datasets
198(15)
4.5.1 Queries and Motivations
198(2)
4.5.2 Spatial Key Words
200(10)
4.5.3 Indexes for Managing Multiple Datasets
210(3)
4.6 Summary
213(1)
References
214(5)
5 Introduction to Cloud Computing
219(44)
5.1 Introduction
219(2)
5.2 Storage
221(14)
5.2.1 SQL Databases
221(2)
5.2.2 Azure Storage
223(10)
5.2.3 Redis Cache
233(2)
5.3 Computing
235(20)
5.3.1 Virtual Machine
236(1)
5.3.2 Cloud Services
237(3)
5.3.3 HDInsight
240(15)
5.4 Applications
255(4)
5.4.1 Web Apps
256(1)
5.4.2 Mobile Apps
257(1)
5.4.3 API Apps
258(1)
5.5 Summary
259(1)
References
259(4)
6 Managing Spatiotemporal Data in the Cloud
263(42)
6.1 Introduction
263(4)
6.1.1 Challenges
263(2)
6.1.2 General Data Management Schemes on the Cloud
265(2)
6.2 Managing Point-Based Data
267(17)
6.2.1 Managing Point-Based Spatiotemporal Static Data
267(5)
6.2.2 Managing Point-Based Spatial Static and Temporal Dynamic Data
272(6)
6.2.3 Managing Point-Based Spatiotemporal Dynamic Data
278(6)
6.3 Managing Network-Based Data
284(17)
6.3.1 Managing Spatiotemporal Static Networks
284(6)
6.3.2 Managing Network-Based Spatial Static and Temporally Dynamic Data
290(4)
6.3.3 Managing Network-Based Spatiotemporal Dynamic Data
294(7)
6.4 Urban Big-Data Platform
301(2)
6.5 Summary
303(2)
IV Urban Data Analytics 305(294)
7 Fundamental Data-Mining Techniques for Urban Data
307(84)
7.1 Introduction
307(7)
7.1.1 General Framework of Data Mining
307(2)
7.1.2 Relationship between Data Mining and Related Technologies
309(5)
7.2 Data Preprocessing
314(12)
7.2.1 Data Cleaning
314(3)
7.2.2 Data Transformation
317(2)
7.2.3 Data Integration
319(7)
7.3 Frequent Pattern Mining and Association Rules
326(19)
7.3.1 Basic Concepts
327(2)
7.3.2 Frequent Itemset Mining Methods
329(5)
7.3.3 Sequential Pattern Mining
334(7)
7.3.4 Frequent Subgraph Pattern Mining
341(4)
7.4 Clustering
345(11)
7.4.1 Concepts
345(2)
7.4.2 Partitioning Clustering Methods
347(2)
7.4.3 Density-Based Clustering
349(5)
7.4.4 Hierarchical Clustering Methods
354(2)
7.5 Classification
356(14)
7.5.1 Concepts
356(3)
7.5.2 Naive Bayesian Classification
359(1)
7.5.3 Decision Trees
360(4)
7.5.4 Support Vector Machines
364(2)
7.5.5 Classification with Imbalanced Data
366(4)
7.6 Regression
370(6)
7.6.1 Linear Regression
370(3)
7.6.2 Autoregression
373(1)
7.6.3 Regression Tree
374(2)
7.7 Outlier and Anomaly Detection
376(8)
7.7.1 Proximity-Based Outlier Detection
377(3)
7.7.2 Statistic-Based Outlier Detection
380(4)
7.8 Summary
384(1)
References
385(6)
8 Advanced Machine-Learning Techniques for Spatiotemporal Data
391(100)
8.1 Introduction
391(1)
8.2 Unique Properties of Spatiotemporal Data
392(4)
8.2.1 Spatial Properties of Spatiotemporal Data
392(2)
8.2.2 Temporal Properties
394(2)
8.3 Collaborative Filtering
396(8)
8.3.1 Basic Models: User Based and Item Based
396(3)
8.3.2 Collaborative Filtering for Spatiotemporal Data
399(5)
8.4 Matrix Factorization
404(7)
8.4.1 Basic Matrix Factorization Methods
404(2)
8.4.2 Matrix Factorization for Spatiotemporal Data
406(5)
8.5 Tensor Decomposition
411(10)
8.5.1 Basic Concepts of Tensors
411(2)
8.5.2 Methods of Tensor Decomposition
413(3)
8.5.3 Tensor Decomposition for Spatiotemporal Data
416(5)
8.6 Probabilistic Graphical Models
421(33)
8.6.1 General Concepts
421(2)
8.6.2 Bayesian Networks
423(10)
8.6.3 Markov Random Field
433(1)
8.6.4 Bayesian Networks for Spatiotemporal Data
433(15)
8.6.5 Markov Networks for Spatiotemporal Data
448(6)
8.7 Deep Learning
454(17)
8.7.1 Artificial Neural Networks
455(4)
8.7.2 Convolutional Neural Networks
459(5)
8.7.3 Recurrent Neural Networks
464(3)
8.7.4 Deep Learning for Spatiotemporal Data
467(4)
8.8 Reinforcement Learning
471(13)
8.8.1 Concepts of Reinforcement Learning
471(3)
8.8.2 Tabular Action-Value Methods
474(7)
8.8.3 Approximate Methods
481(3)
8.9 Summary
484(1)
References
485(6)
9 Cross-Domain Knowledge Fusion
491(44)
9.1 Introduction
491(4)
9.1.1 Relationship to Traditional Data Integration
493(1)
9.1.2 Relationship to Heterogeneous Information Networks
494(1)
9.2 Stage-Based Knowledge Fusion
495(3)
9.3 Feature-Based Knowledge Fusion
498(6)
9.3.1 Feature Concatenation with Regularization
498(4)
9.3.2 Deep Learning-Based Knowledge Fusion
502(2)
9.4 Semantic Meaning-Based Knowledge Fusion
504(22)
9.4.1 Multi-View-Based Knowledge Fusion
505(6)
9.4.2 Similarity-Based Knowledge Fusion
511(6)
9.4.3 Probabilistic Dependency-Based Knowledge Fusion
517(1)
9.4.4 Transfer Learning-Based Knowledge Fusion
518(8)
9.5 Comparison between Different Fusion Methods
526(3)
9.5.1 Volume, Properties, and Insight of Datasets
527(1)
9.5.2 The Goal of a Machine-Learning Task
528(1)
9.5.3 Learning Approach of Machine-Learning Algorithms
528(1)
9.5.4 Efficiency and Scalability
528(1)
9.6 Summary
529(1)
References
530(5)
10 Advanced Topics in Urban Data Analytics
535(64)
10.1 How to Select Useful Datasets
535(7)
10.1.1 Understanding Target Problems
536(1)
10.1.2 Insights behind Data
537(1)
10.1.3 Validating Assumptions
538(4)
10.2 Trajectory Data Mining
542(34)
10.2.1 Trajectory Data
545(1)
10.2.2 Trajectory Preprocessing
546(10)
10.2.3 Trajectory Data Management
556(1)
10.2.4 Uncertainty in a Trajectory
556(4)
10.2.5 Trajectory Pattern Mining
560(6)
10.2.6 Trajectory Classification
566(2)
10.2.7 Anomalies Detection from Trajectories
568(2)
10.2.8 Transferring Trajectories to Other Representations
570(6)
10.3 Combining Machine Learning with Data Management
576(11)
10.3.1 Motivation
576(4)
10.3.2 Boosting Machine Learning with Indexing Structures
580(4)
10.3.3 Scale Down Candidates for Machine Learning
584(1)
10.3.4 Derive Bounds to Prune Computing Spaces for Machine Learning
585(2)
10.4 Interactive Visual Data Analytics
587(1)
10.4.1 Incorporating Multiple Complex Factors
587(1)
10.4.2 Adjusting Parameters without Prior Knowledge
587(1)
10.4.3 Drilling Down into Results
588(1)
10.5 Summary
588(1)
References
589(10)
About the Author 599(2)
Index 601
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