Similarity search and mining in uncertain spatial and spatio-temporal databases:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
2013
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| Schlagworte: | |
| Online-Zugang: | Volltext http://d-nb.info/1045152900/34 kostenfrei Inhaltsverzeichnis |
| Beschreibung: | XXIV, 397 S. Ill., graph. Darst. |
| Format: | Langzeitarchivierung gewährleistet, LZA |
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| 100 | 1 | |a Züfle, Andreas |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Similarity search and mining in uncertain spatial and spatio-temporal databases |c Andreas Züfle |
| 264 | 1 | |c 2013 | |
| 300 | |a XXIV, 397 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
| 502 | |a München, Univ., Diss., 2013 | ||
| 538 | |a Langzeitarchivierung gewährleistet, LZA | ||
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Datensatz im Suchindex
| _version_ | 1804152016203677696 |
|---|---|
| adam_text | CONTENTS
ABSTRACT XX
ZUSAMMENFASSUNG (ABSTRACT IN GERMAN) XXIII
I INTRODUCTION 1
II SPATIAL AND UNCERTAIN DATA: PRELIMINARIES 9
1 SPATIAL DATA 13
1.1 SPATIAL SIMILARITY QUERIES 14
1.1.1 THE SPATIAL RANGE QUERY 15
1.1.2 THE FC-NEAREST NEIGHBOR QUERY 16
1.1.3 THE REVERSE FC-NEAREST NEIGHBOR QUERY 18
2 UNCERTAIN DATA 19
2.1 DISCRETE AND CONTINUOUS MODELS FOR UNCERTAIN DATA 19
2.2 EXISTING MODELS FOR UNCERTAIN DATA 21
2.3 POSSIBLE WORLD SEMANTICS 24
2.4 PROBABILISTIC ANSWER SEMANTICS 27
2.4.1 OBJECT BASED PROBABILISTIC ANSWER SEMANTICS 27
2.4.2 RESULT BASED PROBABILISTIC ANSWER SEMANTICS 29
2.5 PROBABILISTIC QUERY PREDICATES 31
2.5.1 PROBABILISTIC THRESHOLD QUERIES 32
2.5.2 PROBABILISTIC TOPA; QUERIES 33
2.5.3 DISCUSSION 34
2.6 APPROXIMATE QUERIES 34
2.6.1 MONTE CARLO ALGORITHMS 34
2.6.2 PROBABILISTIC GUARANTEES 37
2.7 SUMMARY 38
HTTP://D-NB.INFO/1046754181
CONTENTS
3 THE PARADIGM OF EQUIVALENT WORLDS 41
3.1 EQUIVALENT WORLDS 42
3.2 EXPLOITING EQUIVALENT WORLDS FOR EFFICIENT ALGORITHMS . ? . 44
3.3 CASE STUDY: SUM OF INDEPENDENT BERNOULLI TRIALS 45
3.4 POISSON-BINOMIAL RECURRENCE 46 .
3.5 GENERATING FUNCTIONS 50
3.6 SUMMARY 52
III PROBABILISTIC SPATIAL QUERIES ON UNCERTAIN DATA 53
4 PROBABILISTIC RANGE QUERIES ON UNCERTAIN DATA 57
4.1 INTRODUCTION 57
4.2 RELATED WORK 60
4.3 PROBABILISTIC RANGE QUERIES ON UNCERTAIN DATA: CERTAIN QUERY 60
4.4 PROBABILISTIC RANGE QUERIES ON UNCERTAIN DATA: UNCERTAIN QUERY 62
4.5 RANGE COUNT QUERIES ON UNCERTAIN DATA 64
4.5.1 PROBABILISTIC HOT ITEMS 66
4.6 EXPERIMENTAL EVALUATION 68
4.6.1 BRUTE-FORCE ALGORITHM 68
*
4.6.2 BISECTION-BASED ALGORITHM 68
4.6.3 RUN-TIME EXPERIMENTS 69
4.7 CONCLUSIONS 71
5 OPTIMAL SPATIAL PRUNING 73
5.1 INTRODUCTION 73
5.2 THE PROBLEM OF DETECTING SPATIAL DOMINATION 76
5.3 EXISTING APPROACHES 77
5.3.1 THE MIN-/MAXDIST DECISION CRITERION 77
5.3.2 VORONOI-BASED DECISION CRITERION 78
5.3.3 CORNER-BASED DECISION CRITERION. 80
5.3.4 SUMMARY. 80
5.4 A CORRECT. COMPLETE, AND LINEAR-TIME DOMINATION DECISION CRITERION
... 81
5.5 DOMINATION COUNT COMPUTING * 86
5.5.1 PARTIAL DOMINATION 88
5.5.2 DOMINATION COUNT ESTIMATION 91
5.6 BOOSTING SIMILARITY QUERIES 94
5.7 EXPERIMENTAL EVALUATION 95
5.7.1 SINGLE OBJECT DOMINATION 95
5.7.2 DOMINATION COUNT ESTIMATION 97
5.7.3 IMPACT ON STANDARD SPATIAL QUERY PROCESSING METHODS 98
5.8 CONCLUSIONS 101
CONTENTS
F
IX
6 PROBABILISTIC K-NEAREST NEIGHBOR QUERIES ON UNCERTAIN DATA 103
6.1 INTRODUCTION
V
. 103
6.1.1 UNCERTAINTY MODEL 104
6.1.2 PROBLEM FORMULATION 105
6.1.3 BASIC IDEA 106
6.2 RELATED WORK 107
6.3 SIMILARITY DOMINATION ON UNCERTAIN DATA 107
6.3.1 COMPLETE DOMINATION 108
6.3.2 PROBABILISTIC DOMINATION 110
6.4 PROBABILISTIC DOMINATION COUNT 112
6.4.1 THE PROBLEM OF DOMINATION DEPENDENCIES 112
6.4.2 DOMINATION APPROXIMATIONS BASED ON INDEPENDENT OBJECTS .... 113
6.4.3 UNCERTAIN GENERATING FUNCTIONS (UGFS) 116
6.4.4 EFFICIENT DOMINATION COUNT APPROXIMATION USING UGFS 117
6.4.5 GENERATING FUNCTIONS VS UNCERTAIN GENERATING FUNCTIONS 118
6.4.6 EFFICIENT DOMINATION COUNT APPROXIMATION BASED ON DISJUNCTIVE
WORLDS 122
6.5 IMPLEMENTATION 123
6.6 EXPERIMENTAL EVALUATION 125
6.6.1 RUNTIME OF THE MONTE-CARLO-BASED APPROACH 125
6.6.2 OPTIMAL VS. MIN/MAX DECISION CRITERION 127
6.6.3 ITERATIVE DOMINATION COUNT APPROXIMATION 128
6.6.4 QUERIES WITH A PREDICATE 128
6.6.5 NUMBER OF INFLUENCEOBJECTS 128
6.7 CONCLUSIONS 129
7 PROBABILISTIC RANKING ON UNCERTAIN DATA 131
7.1 INTRODUCTION 131
7.1.1 CONTRIBUTIONS AND OUTLINE 133
7.2 RELATED WORK 134
7.3 PROBABILISTIC RANKING FRAMEWORK 136
7.3.1 DYNAMIC PROBABILITY COMPUTATION 137
7.3.2 INCREMENTAL PROBABILITY COMPUTATION 140
7.3.3 RUNTIME ANALYSIS 142
7.4 PROBABILISTIC RANKING ALGORITHM 144
7.5 PROBABILISTIC RANKING APPROACHES 147
7.5.1 EXPECTED SCORE AND EXPECTED RANKS 147
7.5.2 U-IFCRANKS 148
7.5.3 PT-A;
.* 148
7.5.4 GLOBAL TOP-A; 149
7.6 EXPERIMENTAL EVALUATION 149
7.6.1 DATASETS AND EXPERIMENTAL SETUP 150
7.6.2 SCALABILITY 150
CONTENTS
7.6.3 RANKING DEPTH K 153
7.6.4 INFLUENCE OF THE DEGREE OF UNCERTAINTY 153 -
7.6.5 SUMMARY . . . ., 154
7.7 CONCLUSIONS 155
8 PROBABILISTIC REVERSE K-NEAREST NEIGHBOR QUERIES ON UNCERTAIN DATA 157
8.1 INTRODUCTION 157
8.2 PROBLEM DEFINITION 159
8.2.1 UNCERTAINTY MODEL 159
8.2.2 PRNN QUERIES IN UNCERTAIN DATABASES 160
8.2.3 RNN PRUNING 160
8.3 RELATED WORK 161
8.4 PRNN ALGORITHM SKETCH 162
8.4.1 APPROXIMATION OF OBJECTS 162
8.4.2 SPATIAL PRUNING 162
8.4.3 PROBABILISTIC PRUNING 163
8.4.4 VERIFICATION 163
8.4.5 FRAMEWORK IMPLEMENTATION: LC ALGORITHM 163
8.4.6 FRAMEWORK IMPLEMENTATION: CLWZP ALGORITHM 164
8.4.7 DISCUSSION 164
8.5 HIERARCHICAL PRNN PROCESSING 166
8.5.1 APPROXIMATION 166
8.5.2 SPATIAL PRUNING 166
8.5.3 PROBABILISTIC PRUNING 167
8.5.4 VERIFICATION 171
8.5.5 COMPLEXITY ANALYSIS 171
8.6 IMPLEMENTATION 172
8.6.1 OVERVIEW 172
8.6.2 SPATIAL PRUNING , 173
8.6.3 OBTAINING INFLUENCE OBJECTS 173 *
8.6.4 PROBABILISTIC PRUNING 173
8.7 CONTINUOUS DISTRIBUTIONS 174
8.8 PROBABILISTIC RFCNN QUERIES 176
8.9 EXPERIMENTS 178
8.9.1 SPATIAL PRUNING 179
8.9.2 I/O-COST 179
8.9.3 CPU-COST . 180
8.10 CONCLUSIONS 182
CONTENTS R XI
IV MINING SPATIAL CO-LOCATIONS IN UNCERTAIN SPATIAL DATA 183
9 PRELIMINARIES 187
9.1 SPATIAL CO-LOCATION MINING ON CERTAIN SPATIAL DATA 188
9.2 SPATIAL CO-LOCATION MINING ON UNCERTAIN SPATIAL DATA 192
9.2.1 PROBLEM DEFINITION 195
9.2.2 PROBABILISTIC FREQUENT ITEMSET MINING 197
10 PROBABILISTIC FREQUENT ITEMSET MINING 199
10.1 RELATED WORK 200
10.2 PROBABILISTIC FREQUENT ITEMSETS 201
10.2.1 PROBABILISTIC SUPPORT 203
10.2.2 FREQUENTNESS PROBABILITY 204
10.3 EFFICIENT COMPUTATION OF PROBABILISTIC FREQUENT ITEMSETS 205
10.3.1 EFFICIENT COMPUTATION OF PROBABILISTIC SUPPORT 205
10.3.2 PROBABILISTIC FILTER STRATEGIES 208
10.4 PROBABILISTIC FREQUENT ITEMSET MINING (PFIM) 209
10.5 INCREMENTAL PROBABILISTIC FREQUENT ITEMSET MINING (I-PFIM) 210
10.5.1 INCREMENTAL PROBABILISTIC FREQUENT ITEMSET MINING ALGORITHM . . .
210
10.5.2 TOP-FC
PROBABILISTIC FREQUENT ITEMSETS QUERY 211
10.6 EXPERIMENTAL EVALUATION 212
10.6.1 EVALUATION OF THE FREQUENTNESS PROBABILITY CALCULATIONS 212
10.6.2 EVALUATION OF THE PROBABILISTIC FREQUENT ITEMSET MINING
ALGORITHMS 216
10.7 CONCLUSION 217
11 APPROXIMATE SPATIAL COLLOCATION MINING 219
11.1 APPROXIMATION OF THE SUPPORT PDF OF AN ITEMSET 219
11.1.1 APPROXIMATION BY EXPECTED SUPPORT 220
11.1.2 POISSON DISTRIBUTION-BASED APPROXIMATION 221
11.1.3 NORMAL DISTRIBUTION-BASED APPROXIMATION 222
11.1.4 DISCUSSION .* 223
11.2 THEORETICAL BOUNDS ON THE APPROXIMATION QUALITY 224
11.2.1 QUALITY OF THE POISSON APPROXIMATION 225
11.2.2 QUALITY OF THE NORMAL APPROXIMATION 226
11.3 EXPERIMENTAL RESULTS 226
11.3.1 ACCURACY 228
11.3.2 EFFICIENCY 233
11.4 CONCLUSIONS 234
11.4.1 EXPECTED SUPPORT: 234
11.4.2 POISSON APPROXIMATION: 235
11.4.3 NORMAL APPROXIMATION: . . . 235
XII
R
CONTENTS
V QUERYING AND MINING UNCERTAIN SPATIO-TEMPORAL DATA 237
12 MODELING UNCERTAIN SPATIO-TEMPORAL DATA 243
12.1 STATE-OF-THE-ART . . . . 244
12.1.1 INTERPOLATION MODELS 244
12.1.2 MODELS IGNORING TIME DEPENDENCIES 245
12.2 MODELING UNCERTAIN SPATIO-TEMPORAL DATA 247
13 SPATIO-TEMPORAL WINDOW QUERIES 253
13.1 PROBLEM DEFINITION 253
13.2 PROBABILISTIC SPATIO-TEMPORAL QUERY PROCESSING USING THE
MARKOV-CHAIN
MODEL 254*
13.2.1 OBJECT-BASED QUERY PROCESSING 256
13.2.2 QUERY-BASED QUERY PROCESSING 258
13.2.3 DISCUSSION 259
13.3 MULTIPLE OBSERVATIONS 260
13.4 ADDITIONAL SPATIO-TEMPORAL QUERIES 264
* 13.5 CONCLUSION 266
14 SPATIO-TEMPORAL NEAREST NEIGHBOR QUERIES 267
14.1 RELATED WORK 268
14.2 PROBLEM DEFINITION 268
14.3 THEORETICAL ANALYSIS _ 270
14.3.1 THE P3NN QUERY 270
14.3.2 THE PVNN QUERY 272
14.3.3 THE PCNN QUERY 279.
15 INDEXING UNCERTAIN SPATIO-TEMPORAL DATA 281
15.1 APPROXIMATING UNCERTAIN SPATIO-TEMPORAL OBJECTS 281
15.1.1 UST-OBJECT APPROXIMATION 282
15.1.2 SPATIO-TEMPORAL FILTER 284
15.1.3 PROBABILISTIC UST-OBJECT APPROXIMATION 286
15.1.4 FINDING THE OPTIMAL PROBABILISTIC DIAMOND 290
15.1.5 APPROXIMATING PROBABILISTIC DIAMONDS . . : 292
15.1.6 PROBABILISTIC FILTER 293
15.2 THE UST-TREE 295
15.2.1 ARCHITECTURE 295
15.2.2 QUERY EVALUATION 296
15.3 CONCLUSIONS 297
16 UNIVERSAL SAMPLING OF UNCERTAIN SPATIO-TEMPORAL DATA 299
16.1 TRADITIONAL SAMPLING 300
16.2 ADAPTING THE MODEL TO OBSERVATIONS 301
CONTENTS XIII
16.2.1 EFFICIENT MODEL ADAPTION 301
16.2.2 FORWARD-PHASE 303
16.3 RESEARCH DIRECTIONS . : 309
17 EXPERIMENTAL EVALUATION 311
17.1 EXPERIMENTAL SETUP 311
17.2 SPATIO-TEMPORAL WINDOW QUERIES 313
17.2.1 IMPACT OF THE UST-TREE INDEX 314 *
17.2.2 UST-TREE CONSTRUCTION 315
17.2.3 QUERY PERFORMANCE 317
17.3 SPATIO-TEMPORAL NEAREST NEIGHBOR QUERIES 320
17.3.1 SAMPLING EFFICIENCY. 323
17.3.2 SAMPLING PRECISION AND EFFECTIVENESS 324
17.3.3 EFFECTIVENESS OF THE FORWARD-BACKWARD MODEL 326
17.3.4 CONTINUOUS QUERIES 326
17.4 SUMMARY 328
18 STATISTICAL TRAFFIC PREDICTION IN ROAD NETWORKS 329
18.1 INTRODUCTION 330
18.2 RELATED WORK 331
18.3 STATISTICAL TRAFFIC MODEL 333
18.3.1 TRAFFIC DENSITY IN A NETWORK 334
18.3.2 THE SHORTEST PATH ASSUMPTION 336
18.4 EFFICIENT TRAFFIC PREDICTION 338
18.4.1 TRAFFIC DENSITY PREDICTION 338
18.4.2 A SHORTEST PATH SUFFIX TREE 340
18.5 EXPERIMENTAL EVALUATION 342
18.5.1 EXPERIMENTS ON QUALITY OF THE TRAFFIC DENSITY PREDICTION ......
342
18.5.2 EXPERIMENTS CONCERNING THE EFFICIENCY 346
18.6 CONCLUSIONS 347
VI FUTURE VISIONS 349
19 PROBABILISTIC RANKING IN FUZZY OBJECT DATABASES 353
19.1 INTRODUCTION 354
19.2 PRELIMINARIES 355
19.2.1 FUZZY OBJECTS 355
19.3 FUZZY RANKING 357
19.3.1 IDENTIFYING THE DISTANCE REPRESENTATIVE 358
19.3.2 TRANSLATION TO PROBABILISTIC OBJECTS 360
19.4 CONCLUSIONS 362
19.5 RESEARCH DIRECTIONS 362
XIV
T
CONTENTS
20 SEMANTICALLY RICH GEO-SPATIIL DATA 363
20.1 OVERVIEW 363
20.2 RESEARCH DIRECTIONS 366
/
VII SUMMARY 369
ACKNOWLEDGEMENTS 377
BIBLIOGRAPHY
378
|
| any_adam_object | 1 |
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| publishDateSort | 2013 |
| record_format | marc |
| spelling | Züfle, Andreas Verfasser aut Similarity search and mining in uncertain spatial and spatio-temporal databases Andreas Züfle 2013 XXIV, 397 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier München, Univ., Diss., 2013 Langzeitarchivierung gewährleistet, LZA Mit einer Zssfassung in dt. Sprache (DE-588)4113937-9 Hochschulschrift gnd-content Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:19-162779 https://nbn-resolving.org/urn:nbn:de:bvb:19-162779 Resolving-System kostenfrei Volltext http://d-nb.info/1045152900/34 Langzeitarchivierung Nationalbibliothek http://edoc.ub.uni-muenchen.de/16277/ Verlag kostenfrei DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027179091&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Züfle, Andreas Similarity search and mining in uncertain spatial and spatio-temporal databases |
| subject_GND | (DE-588)4113937-9 |
| title | Similarity search and mining in uncertain spatial and spatio-temporal databases |
| title_auth | Similarity search and mining in uncertain spatial and spatio-temporal databases |
| title_exact_search | Similarity search and mining in uncertain spatial and spatio-temporal databases |
| title_full | Similarity search and mining in uncertain spatial and spatio-temporal databases Andreas Züfle |
| title_fullStr | Similarity search and mining in uncertain spatial and spatio-temporal databases Andreas Züfle |
| title_full_unstemmed | Similarity search and mining in uncertain spatial and spatio-temporal databases Andreas Züfle |
| title_short | Similarity search and mining in uncertain spatial and spatio-temporal databases |
| title_sort | similarity search and mining in uncertain spatial and spatio temporal databases |
| topic_facet | Hochschulschrift |
| url | https://nbn-resolving.org/urn:nbn:de:bvb:19-162779 http://d-nb.info/1045152900/34 http://edoc.ub.uni-muenchen.de/16277/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027179091&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zufleandreas similaritysearchandmininginuncertainspatialandspatiotemporaldatabases |