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Advances in Spatial and Temporal Databases [[electronic resource] ] : 15th International Symposium, SSTD 2017, Arlington, VA, USA, August 21 – 23, 2017, Proceedings / / edited by Michael Gertz, Matthias Renz, Xiaofang Zhou, Erik Hoel, Wei-Shinn Ku, Agnes Voisard, Chengyang Zhang, Haiquan Chen, Liang Tang, Yan Huang, Chang-Tien Lu, Siva Ravada
| Advances in Spatial and Temporal Databases [[electronic resource] ] : 15th International Symposium, SSTD 2017, Arlington, VA, USA, August 21 – 23, 2017, Proceedings / / edited by Michael Gertz, Matthias Renz, Xiaofang Zhou, Erik Hoel, Wei-Shinn Ku, Agnes Voisard, Chengyang Zhang, Haiquan Chen, Liang Tang, Yan Huang, Chang-Tien Lu, Siva Ravada |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIV, 454 p. 206 illus.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Artificial intelligence Computer science—Mathematics Data mining Database Management Artificial Intelligence Discrete Mathematics in Computer Science Data Mining and Knowledge Discovery |
| ISBN | 3-319-64367-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Routing and Trajectories -- Multi-user Itinerary Planning for Optimal Group Preference -- 1 Introduction -- 2 Problem Definition and Preliminaries -- 3 Proposed Solutions -- 3.1 Meeting Graph and Node Profit -- 3.2 Greedy Itinerary Construction -- 3.3 Optimal Itinerary Construction -- 3.4 Acceleration via Graph Compression -- 4 Related Work -- 5 Experiments -- 5.1 Experiment Design -- 5.2 Experimental Results -- 6 Discussions and Conclusion -- References -- Hybrid Best-First Greedy Search for Orienteering with Category Constraints -- 1 Introduction -- 2 Related Work -- 3 Problem Formalization -- 4 Best-First Search Strategy -- 4.1 Potential Score -- 4.2 Our Algorithm -- 4.3 Further Optimizations -- 5 Approximation Algorithms -- 5.1 Bounding the Score -- 5.2 Bounding the Run Time -- 6 Properties and Bounds -- 6.1 Correctness of Pruning -- 6.2 Lower Bounding the Score -- 6.3 Upper Bounding the Run Time -- 7 Experimental Evaluation -- 7.1 Data Sets -- 7.2 Effects of Parameters -- 7.3 Comparison with Competitors -- 8 Conclusion and Future Work -- References -- On Privacy in Spatio-Temporal Data: User Identification Using Microblog Data -- 1 Introduction -- 2 Related Work -- 2.1 User Identification -- 2.2 User Linkage -- 2.3 Spatial Privacy -- 3 Problem Definition -- 4 Trajectory Based User Identification -- 4.1 Trace Profile Modeling -- 4.2 Set Descriptors -- 4.3 Transition Descriptors -- 4.4 Classification -- 4.5 User Linkage -- 5 Experimental Evaluation -- 5.1 Accuracy Using Set Descriptors -- 5.2 Accuracy Using Frequent Transitions -- 5.3 Accuracy for Different Observation Counts -- 5.4 User Linkage Between Different Social Networks -- 5.5 Scalability -- 6 Conclusions -- References -- Big Spatial Data -- Sphinx: Empowering Impala for Efficient Execution of SQL Queries on Big Spatial Data.
1 Introduction -- 2 Background on Impala -- 3 Architecture -- 4 Query Parser -- 4.1 Geometry Data Type -- 4.2 Spatial Functions -- 4.3 Spatial Operations -- 4.4 Spatial Indexing -- 5 Spatial Indexing -- 5.1 Index Construction in Sphinx -- 5.2 Importing SpatialHadoop Indexes -- 6 Query Planner -- 6.1 Range Query Plans -- 6.2 Spatial Join Plans -- 7 Query Executor -- 7.1 R-tree Scanner -- 7.2 Spatial Join Operator -- 8 Experiments -- 8.1 Index Construction -- 8.2 Range Query -- 8.3 Spatial Join -- 9 Related Work -- 10 Conclusion -- References -- ST-Hadoop: A MapReduce Framework for Spatio-Temporal Data -- 1 Introduction -- 2 Related Work -- 3 ST-Hadoop Architecture -- 4 Language Layer -- 5 Indexing Layer -- 5.1 Concept of Hierarchy -- 5.2 Index Construction -- 5.3 Phase I: Sampling -- 5.4 Phase II: Temporal Slicing -- 5.5 Phase III: Spatial Indexing -- 5.6 Phase IV: Physical Writing -- 6 Operations Layer -- 6.1 Spatio-Temporal Range Query -- 6.2 Spatio-Temporal Join -- 7 Experiments -- 7.1 Spatiotemporal Range Query -- 7.2 Index Construction -- 7.3 Spatiotemporal Join -- 8 Conclusion -- References -- GeoWave: Utilizing Distributed Key-Value Stores for Multidimensional Data -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Contributions -- 3.1 Locality Preservation of Multi-dimensional Values -- 3.2 Spatial Subsampling for Map Rendering -- 3.3 Managing Data Variety and Complexity -- 3.4 Key-Value Store Parity -- 4 Experimental Evaluation -- 4.1 Locality Preservation Performance -- 4.2 Map Pixel-Based Spatial Subsampling Performance -- 4.3 Differences in Multi-range Scans Among Key-Value Stores -- 5 Conclusions -- References -- Indexing and Aggregation -- Sweeping-Based Temporal Aggregation -- 1 Introduction -- 2 Related Work -- 3 Problem Formalization -- 3.1 Temporal Relations -- 3.2 Temporal Aggregation on Constant Intervals. 3.3 Temporal Aggregation on Fixed Intervals -- 4 Sweeping-Based Temporal Aggregation -- 4.1 Endpoint Index -- 4.2 Temporal Aggregation on Constant Intervals -- 4.3 Temporal Aggregation on Fixed Intervals -- 5 Empirical Evaluation -- 5.1 Environment -- 5.2 Competitors -- 5.3 Test Workloads -- 5.4 Results -- 6 Conclusion -- References -- Indexing the Pickup and Drop-Off Locations of NYC Taxi Trips in PostgreSQL -- Lessons from the Road -- 1 Introduction -- 2 Studied Spatial Database Indexing Schemes -- 2.1 Generalized Search Tree (GiST-Spatial) -- 2.2 Block Range Index (BRIN-Spatial) -- 2.3 Hippo-Spatial -- 3 Experimental Environment -- 4 Studying the Indexing Overhead -- 4.1 Index Size -- 4.2 Index Initialization Time -- 5 Evaluating the Query Response Time -- 5.1 Varying the Spatial Range Query Selectivity Factor -- 5.2 Varying the Spatial Range Area Size -- 6 Studying the Index Maintenance Overhead -- 6.1 Insertion Time -- 6.2 Deletion Time -- 6.3 Hybrid Workload Performance -- 7 Summary of Results -- 8 Key Insights and Learned Lessons -- References -- Towards Spatially- and Category-Wise k-Diverse Nearest Neighbors Queries -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Proposed Solutions -- 4.1 Recursive Range Filtering (RRF) -- 4.2 Pair Graph Solution (PG) -- 4.3 From Conventional Skylines to Linear Skylines -- 5 Experiments -- 5.1 Spatial Diversity -- 5.2 Categorical Diversity -- 5.3 Effectiveness and Efficiency -- 6 Conclusion -- References -- Spatio-Temporal Functional Dependencies for Sensor Data Streams -- 1 Introduction -- 2 Preliminaries -- 3 Database Modeling for Sensor Data -- 3.1 Granularity Aware Sensor Database -- 3.2 Spatio-Temporal Functional Dependency (STFD) -- 3.3 Reasoning on STFDs -- 3.4 Normalization -- 3.5 Semantic Value Assumption -- 4 Prototype for Sensor Database -- 4.1 Implementation. 4.2 Ongoing Experiments -- 5 Related Work -- 6 Conclusion -- References -- Recommendation -- Location-Aware Query Recommendation for Search Engines at Scale -- 1 Introduction -- 2 Preliminaries and Definitions -- 2.1 Query Log -- 2.2 Obtaining Locations from a Query Log -- 2.3 Query-Flow Graph -- 2.4 Term-Query-Flow Graph -- 3 Location-Aware Query Recommendation -- 4 Location-Aware PPR -- 4.1 BCA with Online Transition Matrix -- 5 Experimental Evaluation -- 5.1 Dataset -- 5.2 Methodology -- 5.3 User Study -- 5.4 Effectiveness -- 5.5 Efficiency -- 6 Related Work -- 7 Conclusion -- References -- Top-k Taxi Recommendation in Realtime Social-Aware Ridesharing Services -- 1 Introduction -- 2 Related Work -- 2.1 Static Ridesharing -- 2.2 Dynamic Ridesharing -- 2.3 Trust-Conscious Ridesharing -- 3 Problem Formulation -- 3.1 Definitions -- 3.2 Spatial and Social Scores -- 3.3 Solution Overview -- 4 Candidate Taxis Searching -- 4.1 Edge-Based Candidates Selection -- 4.2 Grid-Based Candidates Selection -- 5 Taxi Scheduling and Top-k Taxi Selection -- 5.1 Overall Procedure of Top-k Taxis Selection -- 5.2 Spatial Score Upper Bounds -- 5.3 Time-Dependent Fastest Path Calculation -- 5.4 Optimal Schedule -- 5.5 Hopping Algorithm -- 6 Experimental Evaluation -- 6.1 Experimental Settings -- 6.2 Experimental Results -- 7 Conclusion -- References -- P-LAG: Location-Aware Group Recommendation for Passive Users -- 1 Introduction -- 2 Problem Definition -- 3 Vector Extraction -- 3.1 Topic Vector Extraction -- 3.2 Topic Vector Analysis -- 4 Indexing and Search for P-LAG -- 4.1 Basic R-tree Approach -- 4.2 TAR-tree Approach: Topic-Aware R-tree -- 4.3 Vector Compression in the TAR-tree -- 5 Experimental Evaluation -- 5.1 Datasets -- 5.2 Efficiency Analysis -- 5.3 Effectiveness -- 5.4 Storage Requirements -- 6 Related Work -- 7 Conclusion -- References -- Data Mining. Grid-Based Colocation Mining Algorithms on GPU for Big Spatial Event Data: A Summary of Results -- 1 Introduction -- 2 Problem Statement -- 2.1 Basic Concepts -- 2.2 Problem Definition -- 3 Proposed Approach -- 3.1 Algorithm Overview -- 3.2 Cell-Aggregate-Based Upper Bound Filter -- 3.3 Refinement Algorithms -- 4 Evaluation -- 4.1 Results on Synthetic Data -- 4.2 Results on Real World Dataset -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Detecting Isodistance Hotspots on Spatial Networks: A Summary of Results -- 1 Introduction -- 2 Problem Statement -- 2.1 Basic Concepts -- 2.2 Problem Formulation -- 3 BaseNIHD: A Baseline Algorithm Using Known Algorithmic Refinements -- 4 NPP: An Algorithm Based on Network Partitioning and Upper-Bound Pruning -- 5 Theoretical Analysis -- 6 Case Studies on Real World Crime Data -- 6.1 Robberies Occurred in Pinellas County, Florida -- 6.2 Assaults Occurred in Fremont, Washington -- 7 Experimental Evaluation -- 7.1 Experimental Setup -- 7.2 Experimental Results -- 8 Conclusion and Future Work -- References -- Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data -- 1 Introduction -- 1.1 Roadmap -- 2 Framework for Natural Hazards Detection -- 2.1 Environmental Tensor Factorization -- 2.2 Classifying Natural Hazards -- 3 Spatio-Temporal Tensor Sparsification -- 3.1 Algorithmic procedure -- 4 Experimental Evaluation -- 4.1 Global Climate Data -- 4.2 Finding Synthetic Spatio-Temporal Outliers -- 4.3 Finding Natural Hazards on Real Data -- 5 Related Work -- 5.1 Spatio-Temporal Outlier Detection -- 5.2 Classification and Prediction -- 5.3 Tensor Factorization -- 6 Conclusion -- References -- Localization and Spatial Allocation -- FF-SA: Fragmentation-Free Spatial Allocation -- 1 Introduction -- 2 Problem Statement: Fragmentation-Free Spatial Allocation -- 3 Challenges. 4 Related Work and Limitations. |
| Record Nr. | UNISA-996466176303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Spatial and Temporal Databases : 15th International Symposium, SSTD 2017, Arlington, VA, USA, August 21 – 23, 2017, Proceedings / / edited by Michael Gertz, Matthias Renz, Xiaofang Zhou, Erik Hoel, Wei-Shinn Ku, Agnes Voisard, Chengyang Zhang, Haiquan Chen, Liang Tang, Yan Huang, Chang-Tien Lu, Siva Ravada
| Advances in Spatial and Temporal Databases : 15th International Symposium, SSTD 2017, Arlington, VA, USA, August 21 – 23, 2017, Proceedings / / edited by Michael Gertz, Matthias Renz, Xiaofang Zhou, Erik Hoel, Wei-Shinn Ku, Agnes Voisard, Chengyang Zhang, Haiquan Chen, Liang Tang, Yan Huang, Chang-Tien Lu, Siva Ravada |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIV, 454 p. 206 illus.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Artificial intelligence Computer science—Mathematics Data mining Database Management Artificial Intelligence Discrete Mathematics in Computer Science Data Mining and Knowledge Discovery |
| ISBN | 3-319-64367-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Routing and Trajectories -- Multi-user Itinerary Planning for Optimal Group Preference -- 1 Introduction -- 2 Problem Definition and Preliminaries -- 3 Proposed Solutions -- 3.1 Meeting Graph and Node Profit -- 3.2 Greedy Itinerary Construction -- 3.3 Optimal Itinerary Construction -- 3.4 Acceleration via Graph Compression -- 4 Related Work -- 5 Experiments -- 5.1 Experiment Design -- 5.2 Experimental Results -- 6 Discussions and Conclusion -- References -- Hybrid Best-First Greedy Search for Orienteering with Category Constraints -- 1 Introduction -- 2 Related Work -- 3 Problem Formalization -- 4 Best-First Search Strategy -- 4.1 Potential Score -- 4.2 Our Algorithm -- 4.3 Further Optimizations -- 5 Approximation Algorithms -- 5.1 Bounding the Score -- 5.2 Bounding the Run Time -- 6 Properties and Bounds -- 6.1 Correctness of Pruning -- 6.2 Lower Bounding the Score -- 6.3 Upper Bounding the Run Time -- 7 Experimental Evaluation -- 7.1 Data Sets -- 7.2 Effects of Parameters -- 7.3 Comparison with Competitors -- 8 Conclusion and Future Work -- References -- On Privacy in Spatio-Temporal Data: User Identification Using Microblog Data -- 1 Introduction -- 2 Related Work -- 2.1 User Identification -- 2.2 User Linkage -- 2.3 Spatial Privacy -- 3 Problem Definition -- 4 Trajectory Based User Identification -- 4.1 Trace Profile Modeling -- 4.2 Set Descriptors -- 4.3 Transition Descriptors -- 4.4 Classification -- 4.5 User Linkage -- 5 Experimental Evaluation -- 5.1 Accuracy Using Set Descriptors -- 5.2 Accuracy Using Frequent Transitions -- 5.3 Accuracy for Different Observation Counts -- 5.4 User Linkage Between Different Social Networks -- 5.5 Scalability -- 6 Conclusions -- References -- Big Spatial Data -- Sphinx: Empowering Impala for Efficient Execution of SQL Queries on Big Spatial Data.
1 Introduction -- 2 Background on Impala -- 3 Architecture -- 4 Query Parser -- 4.1 Geometry Data Type -- 4.2 Spatial Functions -- 4.3 Spatial Operations -- 4.4 Spatial Indexing -- 5 Spatial Indexing -- 5.1 Index Construction in Sphinx -- 5.2 Importing SpatialHadoop Indexes -- 6 Query Planner -- 6.1 Range Query Plans -- 6.2 Spatial Join Plans -- 7 Query Executor -- 7.1 R-tree Scanner -- 7.2 Spatial Join Operator -- 8 Experiments -- 8.1 Index Construction -- 8.2 Range Query -- 8.3 Spatial Join -- 9 Related Work -- 10 Conclusion -- References -- ST-Hadoop: A MapReduce Framework for Spatio-Temporal Data -- 1 Introduction -- 2 Related Work -- 3 ST-Hadoop Architecture -- 4 Language Layer -- 5 Indexing Layer -- 5.1 Concept of Hierarchy -- 5.2 Index Construction -- 5.3 Phase I: Sampling -- 5.4 Phase II: Temporal Slicing -- 5.5 Phase III: Spatial Indexing -- 5.6 Phase IV: Physical Writing -- 6 Operations Layer -- 6.1 Spatio-Temporal Range Query -- 6.2 Spatio-Temporal Join -- 7 Experiments -- 7.1 Spatiotemporal Range Query -- 7.2 Index Construction -- 7.3 Spatiotemporal Join -- 8 Conclusion -- References -- GeoWave: Utilizing Distributed Key-Value Stores for Multidimensional Data -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Contributions -- 3.1 Locality Preservation of Multi-dimensional Values -- 3.2 Spatial Subsampling for Map Rendering -- 3.3 Managing Data Variety and Complexity -- 3.4 Key-Value Store Parity -- 4 Experimental Evaluation -- 4.1 Locality Preservation Performance -- 4.2 Map Pixel-Based Spatial Subsampling Performance -- 4.3 Differences in Multi-range Scans Among Key-Value Stores -- 5 Conclusions -- References -- Indexing and Aggregation -- Sweeping-Based Temporal Aggregation -- 1 Introduction -- 2 Related Work -- 3 Problem Formalization -- 3.1 Temporal Relations -- 3.2 Temporal Aggregation on Constant Intervals. 3.3 Temporal Aggregation on Fixed Intervals -- 4 Sweeping-Based Temporal Aggregation -- 4.1 Endpoint Index -- 4.2 Temporal Aggregation on Constant Intervals -- 4.3 Temporal Aggregation on Fixed Intervals -- 5 Empirical Evaluation -- 5.1 Environment -- 5.2 Competitors -- 5.3 Test Workloads -- 5.4 Results -- 6 Conclusion -- References -- Indexing the Pickup and Drop-Off Locations of NYC Taxi Trips in PostgreSQL -- Lessons from the Road -- 1 Introduction -- 2 Studied Spatial Database Indexing Schemes -- 2.1 Generalized Search Tree (GiST-Spatial) -- 2.2 Block Range Index (BRIN-Spatial) -- 2.3 Hippo-Spatial -- 3 Experimental Environment -- 4 Studying the Indexing Overhead -- 4.1 Index Size -- 4.2 Index Initialization Time -- 5 Evaluating the Query Response Time -- 5.1 Varying the Spatial Range Query Selectivity Factor -- 5.2 Varying the Spatial Range Area Size -- 6 Studying the Index Maintenance Overhead -- 6.1 Insertion Time -- 6.2 Deletion Time -- 6.3 Hybrid Workload Performance -- 7 Summary of Results -- 8 Key Insights and Learned Lessons -- References -- Towards Spatially- and Category-Wise k-Diverse Nearest Neighbors Queries -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Proposed Solutions -- 4.1 Recursive Range Filtering (RRF) -- 4.2 Pair Graph Solution (PG) -- 4.3 From Conventional Skylines to Linear Skylines -- 5 Experiments -- 5.1 Spatial Diversity -- 5.2 Categorical Diversity -- 5.3 Effectiveness and Efficiency -- 6 Conclusion -- References -- Spatio-Temporal Functional Dependencies for Sensor Data Streams -- 1 Introduction -- 2 Preliminaries -- 3 Database Modeling for Sensor Data -- 3.1 Granularity Aware Sensor Database -- 3.2 Spatio-Temporal Functional Dependency (STFD) -- 3.3 Reasoning on STFDs -- 3.4 Normalization -- 3.5 Semantic Value Assumption -- 4 Prototype for Sensor Database -- 4.1 Implementation. 4.2 Ongoing Experiments -- 5 Related Work -- 6 Conclusion -- References -- Recommendation -- Location-Aware Query Recommendation for Search Engines at Scale -- 1 Introduction -- 2 Preliminaries and Definitions -- 2.1 Query Log -- 2.2 Obtaining Locations from a Query Log -- 2.3 Query-Flow Graph -- 2.4 Term-Query-Flow Graph -- 3 Location-Aware Query Recommendation -- 4 Location-Aware PPR -- 4.1 BCA with Online Transition Matrix -- 5 Experimental Evaluation -- 5.1 Dataset -- 5.2 Methodology -- 5.3 User Study -- 5.4 Effectiveness -- 5.5 Efficiency -- 6 Related Work -- 7 Conclusion -- References -- Top-k Taxi Recommendation in Realtime Social-Aware Ridesharing Services -- 1 Introduction -- 2 Related Work -- 2.1 Static Ridesharing -- 2.2 Dynamic Ridesharing -- 2.3 Trust-Conscious Ridesharing -- 3 Problem Formulation -- 3.1 Definitions -- 3.2 Spatial and Social Scores -- 3.3 Solution Overview -- 4 Candidate Taxis Searching -- 4.1 Edge-Based Candidates Selection -- 4.2 Grid-Based Candidates Selection -- 5 Taxi Scheduling and Top-k Taxi Selection -- 5.1 Overall Procedure of Top-k Taxis Selection -- 5.2 Spatial Score Upper Bounds -- 5.3 Time-Dependent Fastest Path Calculation -- 5.4 Optimal Schedule -- 5.5 Hopping Algorithm -- 6 Experimental Evaluation -- 6.1 Experimental Settings -- 6.2 Experimental Results -- 7 Conclusion -- References -- P-LAG: Location-Aware Group Recommendation for Passive Users -- 1 Introduction -- 2 Problem Definition -- 3 Vector Extraction -- 3.1 Topic Vector Extraction -- 3.2 Topic Vector Analysis -- 4 Indexing and Search for P-LAG -- 4.1 Basic R-tree Approach -- 4.2 TAR-tree Approach: Topic-Aware R-tree -- 4.3 Vector Compression in the TAR-tree -- 5 Experimental Evaluation -- 5.1 Datasets -- 5.2 Efficiency Analysis -- 5.3 Effectiveness -- 5.4 Storage Requirements -- 6 Related Work -- 7 Conclusion -- References -- Data Mining. Grid-Based Colocation Mining Algorithms on GPU for Big Spatial Event Data: A Summary of Results -- 1 Introduction -- 2 Problem Statement -- 2.1 Basic Concepts -- 2.2 Problem Definition -- 3 Proposed Approach -- 3.1 Algorithm Overview -- 3.2 Cell-Aggregate-Based Upper Bound Filter -- 3.3 Refinement Algorithms -- 4 Evaluation -- 4.1 Results on Synthetic Data -- 4.2 Results on Real World Dataset -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Detecting Isodistance Hotspots on Spatial Networks: A Summary of Results -- 1 Introduction -- 2 Problem Statement -- 2.1 Basic Concepts -- 2.2 Problem Formulation -- 3 BaseNIHD: A Baseline Algorithm Using Known Algorithmic Refinements -- 4 NPP: An Algorithm Based on Network Partitioning and Upper-Bound Pruning -- 5 Theoretical Analysis -- 6 Case Studies on Real World Crime Data -- 6.1 Robberies Occurred in Pinellas County, Florida -- 6.2 Assaults Occurred in Fremont, Washington -- 7 Experimental Evaluation -- 7.1 Experimental Setup -- 7.2 Experimental Results -- 8 Conclusion and Future Work -- References -- Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data -- 1 Introduction -- 1.1 Roadmap -- 2 Framework for Natural Hazards Detection -- 2.1 Environmental Tensor Factorization -- 2.2 Classifying Natural Hazards -- 3 Spatio-Temporal Tensor Sparsification -- 3.1 Algorithmic procedure -- 4 Experimental Evaluation -- 4.1 Global Climate Data -- 4.2 Finding Synthetic Spatio-Temporal Outliers -- 4.3 Finding Natural Hazards on Real Data -- 5 Related Work -- 5.1 Spatio-Temporal Outlier Detection -- 5.2 Classification and Prediction -- 5.3 Tensor Factorization -- 6 Conclusion -- References -- Localization and Spatial Allocation -- FF-SA: Fragmentation-Free Spatial Allocation -- 1 Introduction -- 2 Problem Statement: Fragmentation-Free Spatial Allocation -- 3 Challenges. 4 Related Work and Limitations. |
| Record Nr. | UNINA-9910483017603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||