| 摘要 | 第4-6页 |
| Abstract | 第6-8页 |
| Chapter 1 Introduction | 第16-48页 |
| 1.1 Introduction | 第16-18页 |
| 1.2 Trajectory query | 第18-27页 |
| 1.2.1 Similarity introduction | 第20-24页 |
| 1.2.2 Spatio-temporal similarity | 第24-27页 |
| 1.3 Trajectory big data processing | 第27-33页 |
| 1.3.1 Spatial data performance based on Map Reduce | 第27-29页 |
| 1.3.2 Trajectory data processing based on distributed platforms | 第29-33页 |
| 1.4 Trajectory query processing based on distributed platforms | 第33-42页 |
| 1.4.1 Spatial query based on Hadoop system | 第34-36页 |
| 1.4.2 Spatial query based on Spark system | 第36-37页 |
| 1.4.3 Mining trajectory big data processing | 第37-40页 |
| 1.4.4 Mining frequent trajectory big data processing | 第40-42页 |
| 1.5 The position of our work | 第42-45页 |
| 1.6 The main research content for dissertation | 第45-48页 |
| Chapter 2 Top-K Query Processing On Trajectory Data | 第48-80页 |
| 2.1 Introduction | 第48-50页 |
| 2.2 The structure of the spatial index (DTR-TREE index) | 第50-60页 |
| 2.2.1 The construction of the index | 第51-56页 |
| 2.2.2 The maintenance of the index | 第56-60页 |
| 2.3 Query processing based on the distance and activity set | 第60-63页 |
| 2.4 Optimization method of trajectory query algorithm | 第63-66页 |
| 2.5 Experiments | 第66-79页 |
| 2.5.1 Experimental settings | 第66-70页 |
| 2.5.2 Experimental results | 第70-79页 |
| 2.6 Summary | 第79-80页 |
| Chapter 3 Frequent Activity-set Query Processing In Big Trajectory Data | 第80-107页 |
| 3.1 Introduction | 第80-85页 |
| 3.2 Mining frequent trajectory based on query distance | 第85-87页 |
| 3.3 Spatial mining index based on distributed R-Tree indexes | 第87-91页 |
| 3.3.1 DMTR-Tree structure | 第88-89页 |
| 3.3.2 Inverted lists optimization | 第89-91页 |
| 3.4 Parallel query processing based on frequent activity set | 第91-97页 |
| 3.4.1 Phase one | 第92-95页 |
| 3.4.2 Phase two | 第95-97页 |
| 3.5 Performance evaluation | 第97-106页 |
| 3.5.1 Speed performance analysis | 第97-100页 |
| 3.5.2 Query processing evaluation | 第100-106页 |
| 3.6 Summary | 第106-107页 |
| Chapter 4 Skyline Query Processing For Trajectory Data | 第107-133页 |
| 4.1 Introduction | 第107-110页 |
| 4.2 The retrieval functions for trajectory query problem | 第110-112页 |
| 4.2.1 The trajectory spatial retrieval function | 第110-111页 |
| 4.2.2 The trajectory activity retrieval function | 第111-112页 |
| 4.3 Trajectory skyline query processing algorithm | 第112-119页 |
| 4.4 Skyline Query Processing For Incomplete Historical Activity Data | 第119-120页 |
| 4.5 Problem definition for incomplete activity data based on optimized functions105 | 第120-121页 |
| 4.6 An optimized trajectory skyline algorithm based on missing activity-setand distance metric | 第121-126页 |
| 4.6.1 Index traversing algorithm | 第121-122页 |
| 4.6.2 Parallel algorithm for missing activity problem in short trajectories | 第122-124页 |
| 4.6.3 Handling the missing activity problem in long trajectories | 第124-126页 |
| 4.7 Experimental results and evaluations | 第126-132页 |
| 4.7.1 Experimental settings | 第126-127页 |
| 4.7.2 Experimental results | 第127页 |
| 4.7.3 Experimental results of trajectory skyline query processing algorithm | 第127-128页 |
| 4.7.4 Experimental results of trajectory skyline algorithm based on missingactivity-set and distance metric | 第128-132页 |
| 4.8 Summary | 第132-133页 |
| Conclusion and future works | 第133-136页 |
| References | 第136-146页 |
| Papers published in the period of PH.D. education | 第146-149页 |
| Acknowledgements | 第149-150页 |
| Resume | 第150页 |
