| Preface | p. ix |
| Spatio-Temporal Data Mining and Knowledge Discovery: Issues Overview | p. 1 |
| Introduction | p. 1 |
| Background | p. 2 |
| Data Mining | p. 2 |
| Spatial Data Mining | p. 3 |
| Gidb Data Mining | p. 4 |
| Data Mining Effort at Nrl Damp | p. 4 |
| Geospatial Information Database (GIDB) | p. 5 |
| Data | p. 7 |
| Vector Data - Nima | p. 7 |
| Miscellaneous Data Repositories | p. 9 |
| Oceanographic Data | p. 10 |
| Model Output | p. 10 |
| Observational Data - Argus Sites | p. 11 |
| 2.5D and 3D Data | p. 11 |
| Data Issues | p. 11 |
| Spatio-Temporal Data Issues | p. 12 |
| Data Source Issues | p. 15 |
| Nima Data | p. 15 |
| Model Output | p. 16 |
| Observation Data | p. 17 |
| Conclusions | p. 17 |
| Indexing of Objects on the Move | p. 21 |
| Introduction | p. 21 |
| Problem Statement and Related Work | p. 23 |
| Problem Statement | p. 23 |
| Previous Work | p. 25 |
| The Tpr-Tree | p. 26 |
| Index Structure and Time-Parameterized Bounding Rectangles | p. 26 |
| Heuristics for Tree Organization | p. 28 |
| Indexing Approaches Related to the Tpr-Tree | p. 29 |
| The R[superscript exp]-Tree | p. 30 |
| Representation of Points and Bounding Rectangles | p. 30 |
| One-Dimensional Optimal Time-Parameterized Bounding Rectangles | p. 30 |
| Multi-Dimensional Time-Parameterized Bounding Rectangles | p. 32 |
| Removal of Expired Entries | p. 35 |
| Summary of Performance Experiments | p. 38 |
| Conclusions | p. 39 |
| Efficient Storage of Large Volume Spatial and Temporal Point-Data in an Object-Oriented Database | p. 43 |
| Introduction | p. 43 |
| The Gidb System | p. 45 |
| The Problem Domain | p. 45 |
| An Object-Oriented Solution | p. 46 |
| Requirements | p. 48 |
| Towards a Solution | p. 48 |
| The Design | p. 49 |
| A Flexible Framework | p. 52 |
| Sample Applications | p. 55 |
| Evaluation | p. 56 |
| Future Developments | p. 58 |
| Conclusions | p. 59 |
| A Typology of Spatiotemporal Information Queries | p. 63 |
| Introduction | p. 63 |
| Spatiotemporal Information for the Dynamic World | p. 65 |
| A Typology of Spatiotemporal Queries | p. 67 |
| Attribute Query | p. 67 |
| Three Spatial Query Types | p. 68 |
| Three Temporal Query Types | p. 70 |
| Four Spatiotemporal Query Types | p. 72 |
| Conclusions | p. 78 |
| Visual Query of Time-Dependent 3D Weather in a Global Geospatial Environment | p. 83 |
| Introduction | p. 83 |
| 4D Data Model for the Visual Earth | p. 84 |
| Relevant Work | p. 85 |
| The Dynamic Data Model | p. 87 |
| System Organization | p. 90 |
| Scalable, Hierarchical 3D Data Structure | p. 92 |
| The Data Structure | p. 92 |
| Results for Acquiring and Visualizing Time-Dependent Data | p. 96 |
| Interactive, Accurate Visualization of Nonuniform Data | p. 100 |
| STQL--A Spatio-Temporal Query Language | p. 105 |
| Introduction | p. 105 |
| Related Work | p. 107 |
| The Data Model | p. 109 |
| Moving Objects | p. 110 |
| Temporal Lifting | p. 110 |
| Spatio-Temporal Predicates and Developments | p. 111 |
| Querying with Spatio-Temporal Operations | p. 113 |
| Design Aspects and Application Scenarios | p. 114 |
| Temporal Selections | p. 115 |
| Projections to Space and Time | p. 115 |
| Aggregations | p. 116 |
| Temporally Lifted Operations | p. 117 |
| Querying Developments in STQL | p. 118 |
| Motivation | p. 119 |
| Querying | p. 120 |
| Visual Querying | p. 123 |
| Conclusions | p. 124 |
| Tripod: A Spatio-Historical Object Database System | p. 127 |
| Introduction | p. 128 |
| Case Study: UK National Land Use Database | p. 129 |
| The Tripod Object Model | p. 130 |
| Spatial Literals | p. 131 |
| Timestamp Literals | p. 133 |
| Histories | p. 134 |
| Architecture | p. 136 |
| The Language Bindings | p. 138 |
| Query Processing | p. 139 |
| Logical Optimization | p. 140 |
| Physical Optimization and Query Evaluation | p. 141 |
| Related Work | p. 145 |
| Conclusions | p. 146 |
| Spatio-Temporal Subgroup Discovery | p. 149 |
| Introduction: Spatial Subgroup Mining | p. 149 |
| Application Example | p. 152 |
| Representation of Spatio-Temporal Data and of Spatial Subgroups | p. 154 |
| Representation of Spatial Data | p. 154 |
| Representation of Spatio-Temporal Data | p. 156 |
| Representation of Spatial Subgroups | p. 157 |
| Representation of Spatial Subgroups in Query Languages | p. 159 |
| Spatio-Temporal Analyses | p. 160 |
| Analyses | p. 160 |
| Statistical Methods | p. 162 |
| Database Integration | p. 164 |
| Conclusions and Future Work | p. 166 |
| Table of Contents provided by Ingram. All Rights Reserved. |