Nearest Neighbor Search:: A Database Perspective
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| Format: | Electronic eBook |
|---|---|
| Language: | English |
| Published: |
Berlin
Springer US
2005
|
| Edition: | 1. Ed. |
| Series: | Series in Computer Science
|
| Subjects: | |
| Online Access: | BTU01 FHM01 UBG01 UBY01 UPA01 Volltext Inhaltsverzeichnis |
| Physical Description: | 1 Online-Ressource (XXII, 170 S.) 77 schw.-w. Ill. |
| ISBN: | 9780387229638 9780387275444 |
| DOI: | 10.1007/0-387-27544-4 |
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| 245 | 1 | 0 | |a Nearest Neighbor Search: |b A Database Perspective |c Apostolos N. Papadopoulos ; Yannis Manolopoulos |
| 250 | |a 1. Ed. | ||
| 264 | 1 | |a Berlin |b Springer US |c 2005 | |
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Record in the Search Index
| _version_ | 1804136452415553536 |
|---|---|
| adam_text | Contents
List of Figures ix
List of Tables xiii
Preface xvii
Acknowledgments xxi
Part I Fundamental Issues
1. SPATIAL DATABASE CONCEPTS 3
1 Introduction 3
2 Spatial Query Processing 4
3 Access Methods 6
4 Handling High Dimensional Data 8
5 Spatial Data Support in Commercial Systems 9
6 Summary 10
7 Further Reading 11
2. THE R TREE AND VARIATIONS 13
1 Introduction 13
2 The Original R tree 13
3 Dynamic R tree Variants 15
3.1 The R+ tree 15
3.2 TheR* tree 16
3.3 The Hilbert R tree 16
4 Static R tree Variants 17
4.1 The Packed R tree 18
4.2 The Hilbert Packed R tree 18
v
vi NEAREST NEIGHBOR SEARCH
4.3 The STR Packed R tree 18
5 Performance Issues 18
6 R trees in Emerging Applications 19
7 Summary 20
8 Further Reading 20
Part II Nearest Neighbor Search in Spatial and Spatiotemporal Databases
3. NEAREST NEIGHBOR QUERIES 25
1 Introduction 25
2 The Nearest Neighbor Problem 25
3 Applications 27
4 Nearest Neighbor Queries in R trees 28
5 Nearest Neighbor Queries in Multimedia Applications 31
6 Summary 34
7 Further Reading 34
4. ANALYSIS OF NEAREST NEIGHBOR QUERIES 37
1 Introduction 37
2 Analytical Considerations 38
2.1 Preliminaries 38
2.2 Estimation of dnn and dm 40
2.3 Performance Estimation 42
3 Performance Evaluation 44
3.1 Preliminaries ^
3.2 Experimental Results 45
4 Summary 45
5 Further Reading 47
5. NEAREST NEIGHBOR QUERIES
IN MOVING OBJECTS 49
1 Introduction
2 Organizing Moving Objects 50
3 Nearest Neighbor Queries 52
3.1 The NNS Algorithm 56
3.1.1 Algorithm NNS a 57
3.1.2 Algorithm NNS b 61
3.2 Query Processing with TPR trees 62
Contents vii
4 Performance Evaluation 66
4.1 Preliminaries 66
4.2 Experimental Results 68
5 Summary 71
6 Further Reading 72
Part III Nearest Neighbor Search with Multiple Resources
6. PARALLEL AND DISTRIBUTED DATABASES 75
1 Introduction 75
2 Multidisk Systems 76
3 Multiprocessor Systems 80
4 Distributed Systems 83
5 Summary 84
6 Further Reading 85
7. MULTIDISK QUERY PROCESSING 87
1 Introduction 87
2 Algorithms 88
2.1 The Branch and Bound Algorithm 88
2.2 Full Parallel Similarity Search 88
2.3 Candidate Reduction Similarity Search 91
2.4 Optimal Similarity Search 97
3 Performance Evaluation 98
3.1 Preliminaries 98
3.2 Experimental Results 102
3.3 Interpretation of Results 105
4 Summary 107
5 Further Reading 108
8. MULTIPROCESSOR QUERY PROCESSING 109
1 Introduction 109
2 Performance Estimation 110
3 Parallel Algorithms 111
3.1 Adapting BB NNF in Declustered R trees 111
3.2 The Parallel Nearest Neighbor Finding (P NNF) Method
113
3.3 When Statistics are not Available 116
viii NEAREST NEIGHBOR SEARCH
3.4 Correctness of P NNF Algorithms 117
4 Performance Evaluation 117
4.1 Preliminaries 117
4.2 The Cost Model 118
4.3 Experimental Results 120
4.4 Interpretation of Results 122
5 Summary 124
6 Further Reading 125
9. DISTRIBUTED QUERY PROCESSING 127
1 Introduction 127
2 Query Evaluation Strategies 129
2.1 Algorithms 129
2.2 Theoretical Study 130
2.3 Analytical Comparison 139
3 The Impact of Derived Data 142
4 Performance Evaluation 146
4.1 Preliminaries 146
4.2 Cost Model Evaluation 146
4.3 Experimental Results 147
5 Discussion 150
6 Summary 151
7 Further Reading 151
Epilogue 153
References 157
List of Figures
1.1 Examples of spatial datasets. 4
1.2 Examples of range and NN queries in 2 d space. 5
1.3 Examples of spatial join queries. 6
1.4 A set of polygons and their corresponding MBRs. 7
1.5 Filter refinement query processing. 8
1.6 Intersection and containment queries. 8
1.7 Mapping time series to multidimensional vectors. 9
2.1 An R tree example. 14
2.2 An R+ tree example. 16
2.3 Examples of space filling curves in 2 d space. 17
2.4 MBRs of leaf nodes for R tree, R* tree and STR packed
R tree. 19
3.1 Examples of 2 NN and 4 NN queries using the Li norm. 26
3.2 Answering a 3 NN query by using repetitive range queries. 27
3.3 MINDIST and MINMAXDIST between a point P and
two rectangles R and i?2 30
3.4 NN search algorithm for R trees. 31
4.1 Two equivalent query execution plans. 37
4.2 (a): example of Proposition 4.1, (b): example of Propo¬
sition 4.2. 40
4.3 When the query point P coincides with a vertex of
the MBR, then the maximum difference ( r) between
MINDIST and MINMAXDIST is obtained. 42
4.4 Example of an enlarged data page. 43
4.5 Datasets used in the experiments. 44
ix
x NEAREST NEIGHBOR SEARCH
5.1 Generation of a moving bounding rectangle. 51
5.2 A NN query example in a moving dataset. 52
5.3 Visualization of the distance between a moving object
and a moving query. 55
5.4 Relative distance of objects with respect to a moving query. 55
5.5 Nearest neighbors of the moving query for k = 2 (top)
and k = 3 (bottom). 56
5.6 The NNS a algorithm. 59
5.7 The four different cases that show the relation of a new
object to the current nearest neighbors. 62
5.8 The NNS b algorithm. 63
5.9 The modify CNN list procedure. 64
5.10 Pruning techniques. 65
5.11 The NNS CON algorithm. 66
5.12 Results for different values of the number of nearest neighbors. 69
5.13 CPU cost over I/O cost. 69
5.14 Results for different buffer capacities. 70
5.15 Results for different values of the travel time. 71
5.16 Results for different space dimensions. 71
5.17 Results for different database size. 72
6.1 Parallel and distributed database systems. 75
6.2 Example of disk array architecture. 77
6.3 Independent R trees. 78
6.4 R tree with super nodes. 78
6.5 MX R tree example. 79
6.6 Parallel architectures. 81
6.7 R tree example. 82
6.8 Declustering an R tree over three sites. 82
6.9 Horizontal and vertical fragmentation. 83
6.10 Distributed database architecture. 84
7.1 MINDIST, MINMAXDISTmdMAXDISTbe
tween a point P and two rectangles R and R%. 90
7.2 Illustration of pruning and candidate selection. 91
7.3 Example of an R* tree with 13 nodes and 3 entries per node. 93
7.4 Illustration of the first three stages of the CRSS algo¬
rithm. Different candidate runs are separated by guards,
indicated by shaded boxes. 93
List of Figures xi
7.5 The most important code fragments of the CRSS algorithm. 95
7.6 Datasets used in performance evaluation. 99
7.7 The simulation model for the system under consideration. 100
7.8 Number of visited nodes vs. query size for 2 d data sets. 103
7.9 Number of visited nodes (normalized to WOPTSS) vs.
query size for synthetic data in 10 d space. 103
7.10 Response time (sees) vs. query arrival rate (A). 104
7.11 Response time (normalized to WOPTSS) vs. number
of disks (A=5 queries/sec, dimensions=5). 104
7.12 Response time (normalized to WOPTSS) vs. num¬
ber of nearest neighbors (Left: A=l queries/sec, Right:
A=20 queries/sec). 104
7.13 BBSS will visit all nodes associated with the branch of
R , leading to unnecessary accesses. 106
8.1 Declustering an R tree over three sites. 109
8.2 Measured and Estimated number of leaf accesses vs.
the number k of nearest neighbors. 112
8.3 Basic difference between BB NNF and P NNF methods. 114
8.4 MINDIST, MINMAXDIST and MAXDIST be¬
tween a point P and two rectangles R and #2 114
8.5 The IEEE 802.3 (CSMA/CD bus) frame layout. 118
8.6 Graphical representation of datasets used for experimentation. 119
8.7 Calculation of the Response Time of a query. 120
8.8 Response time (in msecs) vs. k (secondary sites=10,
NSeff = lOMbit/sec). 121
8.9 Number of transmitted frames, time to process the upper
R tree levels and number of transmitted objects, vs. k
(secondary sites=10, NSeff = lOMbit/sec). 122
8.10 Response time (in msecs) vs. number of secondary servers. 123
9.1 The abstract system architecture. 127
9.2 Performance of methods for scenario A (logarithmic scales). 140
9.3 Performance of methods for scenario B (logarithmic scales). 141
9.4 (a) Use of two MBBs for discrimination, (b) The nearest
neighbor of P is not in MBB1, (c) A query point P
enclosed by many MBBs. 143
9.5 Cost model evaluation (logarithmic scales). 147
9.6 Measured response time for scenario A (logarithmic scales). 148
9.7 Measured response time for scenario B (logarithmic scales). 150
List of Tables
3.1 Distances between a query object and some data objects. 33
4.1 Basic notations used throughout the analysis. 39
4.2 Number of leaf accesses vs. data population. Data=Uniform,
Fanout=50. 46
4.3 Number of leaf accesses vs. fanout. Data=Uniform,
Population=50,000. 46
4.4 Number of leaf accesses vs. fanout. Data=MG points,
Population » 9,000. 46
5.1 NN queries for different query and data characteristics. 54
5.2 Parameters and corresponding values. 67
5.3 Experiments conducted. 68
7.1 Description of query processing parameters. 101
7.2 Description of disk characteristics (model HP C220A) [108], 101
7.3 Scalability with respect to population growth: Response
time (sees) vs. population and number of disks, (set:
gaussian, dimensions: 5, NNs: 20, A=5 queries/sec). 105
7.4 Scalability with respect to query size growth: Response
time (sees) vs. number of nearest neighbors and number
of disks, (set: gaussian, dimensions: 5, population:
80,000, A=5 queries/sec). 105
7.5 Qualitative comparison of all algorithms (a y/ means
good performance). 107
8.1 Description of datasets. 118
8.2 Response Time vs. network speed (Secondary sites= 10,
NN requested = 10,100 and 200). 124
9.1 Symbols, definitions and corresponding values. 130
xiii
|
| adam_txt |
Contents
List of Figures ix
List of Tables xiii
Preface xvii
Acknowledgments xxi
Part I Fundamental Issues
1. SPATIAL DATABASE CONCEPTS 3
1 Introduction 3
2 Spatial Query Processing 4
3 Access Methods 6
4 Handling High Dimensional Data 8
5 Spatial Data Support in Commercial Systems 9
6 Summary 10
7 Further Reading 11
2. THE R TREE AND VARIATIONS 13
1 Introduction 13
2 The Original R tree 13
3 Dynamic R tree Variants 15
3.1 The R+ tree 15
3.2 TheR* tree 16
3.3 The Hilbert R tree 16
4 Static R tree Variants 17
4.1 The Packed R tree 18
4.2 The Hilbert Packed R tree 18
v
vi NEAREST NEIGHBOR SEARCH
4.3 The STR Packed R tree 18
5 Performance Issues 18
6 R trees in Emerging Applications 19
7 Summary 20
8 Further Reading 20
Part II Nearest Neighbor Search in Spatial and Spatiotemporal Databases
3. NEAREST NEIGHBOR QUERIES 25
1 Introduction 25
2 The Nearest Neighbor Problem 25
3 Applications 27
4 Nearest Neighbor Queries in R trees 28
5 Nearest Neighbor Queries in Multimedia Applications 31
6 Summary 34
7 Further Reading 34
4. ANALYSIS OF NEAREST NEIGHBOR QUERIES 37
1 Introduction 37
2 Analytical Considerations 38
2.1 Preliminaries 38
2.2 Estimation of dnn and dm 40
2.3 Performance Estimation 42
3 Performance Evaluation 44
3.1 Preliminaries ^
3.2 Experimental Results 45
4 Summary 45
5 Further Reading 47
5. NEAREST NEIGHBOR QUERIES
IN MOVING OBJECTS 49
1 Introduction
2 Organizing Moving Objects 50
3 Nearest Neighbor Queries 52
3.1 The NNS Algorithm 56
3.1.1 Algorithm NNS a 57
3.1.2 Algorithm NNS b 61
3.2 Query Processing with TPR trees 62
Contents vii
4 Performance Evaluation 66
4.1 Preliminaries 66
4.2 Experimental Results 68
5 Summary 71
6 Further Reading 72
Part III Nearest Neighbor Search with Multiple Resources
6. PARALLEL AND DISTRIBUTED DATABASES 75
1 Introduction 75
2 Multidisk Systems 76
3 Multiprocessor Systems 80
4 Distributed Systems 83
5 Summary 84
6 Further Reading 85
7. MULTIDISK QUERY PROCESSING 87
1 Introduction 87
2 Algorithms 88
2.1 The Branch and Bound Algorithm 88
2.2 Full Parallel Similarity Search 88
2.3 Candidate Reduction Similarity Search 91
2.4 Optimal Similarity Search 97
3 Performance Evaluation 98
3.1 Preliminaries 98
3.2 Experimental Results 102
3.3 Interpretation of Results 105
4 Summary 107
5 Further Reading 108
8. MULTIPROCESSOR QUERY PROCESSING 109
1 Introduction 109
2 Performance Estimation 110
3 Parallel Algorithms 111
3.1 Adapting BB NNF in Declustered R trees 111
3.2 The Parallel Nearest Neighbor Finding (P NNF) Method
113
3.3 When Statistics are not Available 116
viii NEAREST NEIGHBOR SEARCH
3.4 Correctness of P NNF Algorithms 117
4 Performance Evaluation 117
4.1 Preliminaries 117
4.2 The Cost Model 118
4.3 Experimental Results 120
4.4 Interpretation of Results 122
5 Summary 124
6 Further Reading 125
9. DISTRIBUTED QUERY PROCESSING 127
1 Introduction 127
2 Query Evaluation Strategies 129
2.1 Algorithms 129
2.2 Theoretical Study 130
2.3 Analytical Comparison 139
3 The Impact of Derived Data 142
4 Performance Evaluation 146
4.1 Preliminaries 146
4.2 Cost Model Evaluation 146
4.3 Experimental Results 147
5 Discussion 150
6 Summary 151
7 Further Reading 151
Epilogue 153
References 157
List of Figures
1.1 Examples of spatial datasets. 4
1.2 Examples of range and NN queries in 2 d space. 5
1.3 Examples of spatial join queries. 6
1.4 A set of polygons and their corresponding MBRs. 7
1.5 Filter refinement query processing. 8
1.6 Intersection and containment queries. 8
1.7 Mapping time series to multidimensional vectors. 9
2.1 An R tree example. 14
2.2 An R+ tree example. 16
2.3 Examples of space filling curves in 2 d space. 17
2.4 MBRs of leaf nodes for R tree, R* tree and STR packed
R tree. 19
3.1 Examples of 2 NN and 4 NN queries using the Li norm. 26
3.2 Answering a 3 NN query by using repetitive range queries. 27
3.3 MINDIST and MINMAXDIST between a point P and
two rectangles R\ and i?2 30
3.4 NN search algorithm for R trees. 31
4.1 Two equivalent query execution plans. 37
4.2 (a): example of Proposition 4.1, (b): example of Propo¬
sition 4.2. 40
4.3 When the query point P coincides with a vertex of
the MBR, then the maximum difference ( r) between
MINDIST and MINMAXDIST is obtained. 42
4.4 Example of an enlarged data page. 43
4.5 Datasets used in the experiments. 44
ix
x NEAREST NEIGHBOR SEARCH
5.1 Generation of a moving bounding rectangle. 51
5.2 A NN query example in a moving dataset. 52
5.3 Visualization of the distance between a moving object
and a moving query. 55
5.4 Relative distance of objects with respect to a moving query. 55
5.5 Nearest neighbors of the moving query for k = 2 (top)
and k = 3 (bottom). 56
5.6 The NNS a algorithm. 59
5.7 The four different cases that show the relation of a new
object to the current nearest neighbors. 62
5.8 The NNS b algorithm. 63
5.9 The modify CNN list procedure. 64
5.10 Pruning techniques. 65
5.11 The NNS CON algorithm. 66
5.12 Results for different values of the number of nearest neighbors. 69
5.13 CPU cost over I/O cost. 69
5.14 Results for different buffer capacities. 70
5.15 Results for different values of the travel time. 71
5.16 Results for different space dimensions. 71
5.17 Results for different database size. 72
6.1 Parallel and distributed database systems. 75
6.2 Example of disk array architecture. 77
6.3 Independent R trees. 78
6.4 R tree with super nodes. 78
6.5 MX R tree example. 79
6.6 Parallel architectures. 81
6.7 R tree example. 82
6.8 Declustering an R tree over three sites. 82
6.9 Horizontal and vertical fragmentation. 83
6.10 Distributed database architecture. 84
7.1 MINDIST, MINMAXDISTmdMAXDISTbe
tween a point P and two rectangles R\ and R%. 90
7.2 Illustration of pruning and candidate selection. 91
7.3 Example of an R* tree with 13 nodes and 3 entries per node. 93
7.4 Illustration of the first three stages of the CRSS algo¬
rithm. Different candidate runs are separated by guards,
indicated by shaded boxes. 93
List of Figures xi
7.5 The most important code fragments of the CRSS algorithm. 95
7.6 Datasets used in performance evaluation. 99
7.7 The simulation model for the system under consideration. 100
7.8 Number of visited nodes vs. query size for 2 d data sets. 103
7.9 Number of visited nodes (normalized to WOPTSS) vs.
query size for synthetic data in 10 d space. 103
7.10 Response time (sees) vs. query arrival rate (A). 104
7.11 Response time (normalized to WOPTSS) vs. number
of disks (A=5 queries/sec, dimensions=5). 104
7.12 Response time (normalized to WOPTSS) vs. num¬
ber of nearest neighbors (Left: A=l queries/sec, Right:
A=20 queries/sec). 104
7.13 BBSS will visit all nodes associated with the branch of
R\, leading to unnecessary accesses. 106
8.1 Declustering an R tree over three sites. 109
8.2 Measured and Estimated number of leaf accesses vs.
the number k of nearest neighbors. 112
8.3 Basic difference between BB NNF and P NNF methods. 114
8.4 MINDIST, MINMAXDIST and MAXDIST be¬
tween a point P and two rectangles R\ and #2 114
8.5 The IEEE 802.3 (CSMA/CD bus) frame layout. 118
8.6 Graphical representation of datasets used for experimentation. 119
8.7 Calculation of the Response Time of a query. 120
8.8 Response time (in msecs) vs. k (secondary sites=10,
NSeff = lOMbit/sec). 121
8.9 Number of transmitted frames, time to process the upper
R tree levels and number of transmitted objects, vs. k
(secondary sites=10, NSeff = lOMbit/sec). 122
8.10 Response time (in msecs) vs. number of secondary servers. 123
9.1 The abstract system architecture. 127
9.2 Performance of methods for scenario A (logarithmic scales). 140
9.3 Performance of methods for scenario B (logarithmic scales). 141
9.4 (a) Use of two MBBs for discrimination, (b) The nearest
neighbor of P is not in MBB1, (c) A query point P
enclosed by many MBBs. 143
9.5 Cost model evaluation (logarithmic scales). 147
9.6 Measured response time for scenario A (logarithmic scales). 148
9.7 Measured response time for scenario B (logarithmic scales). 150
List of Tables
3.1 Distances between a query object and some data objects. 33
4.1 Basic notations used throughout the analysis. 39
4.2 Number of leaf accesses vs. data population. Data=Uniform,
Fanout=50. 46
4.3 Number of leaf accesses vs. fanout. Data=Uniform,
Population=50,000. 46
4.4 Number of leaf accesses vs. fanout. Data=MG points,
Population » 9,000. 46
5.1 NN queries for different query and data characteristics. 54
5.2 Parameters and corresponding values. 67
5.3 Experiments conducted. 68
7.1 Description of query processing parameters. 101
7.2 Description of disk characteristics (model HP C220A) [108], 101
7.3 Scalability with respect to population growth: Response
time (sees) vs. population and number of disks, (set:
gaussian, dimensions: 5, NNs: 20, A=5 queries/sec). 105
7.4 Scalability with respect to query size growth: Response
time (sees) vs. number of nearest neighbors and number
of disks, (set: gaussian, dimensions: 5, population:
80,000, A=5 queries/sec). 105
7.5 Qualitative comparison of all algorithms (a y/ means
good performance). 107
8.1 Description of datasets. 118
8.2 Response Time vs. network speed (Secondary sites= 10,
NN requested = 10,100 and 200). 124
9.1 Symbols, definitions and corresponding values. 130
xiii |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author_GND | (DE-588)123955297 |
| building | Verbundindex |
| bvnumber | BV022393275 |
| classification_rvk | ST 270 ST 134 |
| collection | ZDB-2-SCS |
| ctrlnum | (OCoLC)873412544 (DE-599)BVBBV022393275 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.1007/0-387-27544-4 |
| edition | 1. Ed. |
| format | Electronic eBook |
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| id | DE-604.BV022393275 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:15:37Z |
| indexdate | 2024-07-09T20:56:37Z |
| institution | BVB |
| isbn | 9780387229638 9780387275444 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015602066 |
| oclc_num | 873412544 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-739 DE-706 DE-M347 DE-634 |
| owner_facet | DE-473 DE-BY-UBG DE-739 DE-706 DE-M347 DE-634 |
| physical | 1 Online-Ressource (XXII, 170 S.) 77 schw.-w. Ill. |
| psigel | ZDB-2-SCS |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Springer US |
| record_format | marc |
| series2 | Series in Computer Science |
| spelling | Nearest Neighbor Search: A Database Perspective Apostolos N. Papadopoulos ; Yannis Manolopoulos 1. Ed. Berlin Springer US 2005 1 Online-Ressource (XXII, 170 S.) 77 schw.-w. Ill. txt rdacontent c rdamedia cr rdacarrier Series in Computer Science Vektorraum (DE-588)4130622-3 gnd rswk-swf Nächste-Nachbarn-Problem (DE-588)4376579-8 gnd rswk-swf Abfrage (DE-588)4198788-3 gnd rswk-swf Suchverfahren (DE-588)4132315-4 gnd rswk-swf Datenbanksystem (DE-588)4113276-2 gnd rswk-swf Datenbanksystem (DE-588)4113276-2 s Suchverfahren (DE-588)4132315-4 s Abfrage (DE-588)4198788-3 s Vektorraum (DE-588)4130622-3 s Nächste-Nachbarn-Problem (DE-588)4376579-8 s DE-604 Papadopoulos, Apostolos N. Sonstige oth Manolopoulos, Yannis 1957- Sonstige (DE-588)123955297 oth Erscheint auch als Druck-Ausgabe, Hardcover 0-387-22963-9 https://doi.org/10.1007/0-387-27544-4 Verlag Volltext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015602066&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nearest Neighbor Search: A Database Perspective Vektorraum (DE-588)4130622-3 gnd Nächste-Nachbarn-Problem (DE-588)4376579-8 gnd Abfrage (DE-588)4198788-3 gnd Suchverfahren (DE-588)4132315-4 gnd Datenbanksystem (DE-588)4113276-2 gnd |
| subject_GND | (DE-588)4130622-3 (DE-588)4376579-8 (DE-588)4198788-3 (DE-588)4132315-4 (DE-588)4113276-2 |
| title | Nearest Neighbor Search: A Database Perspective |
| title_auth | Nearest Neighbor Search: A Database Perspective |
| title_exact_search | Nearest Neighbor Search: A Database Perspective |
| title_exact_search_txtP | Nearest Neighbor Search: A Database Perspective |
| title_full | Nearest Neighbor Search: A Database Perspective Apostolos N. Papadopoulos ; Yannis Manolopoulos |
| title_fullStr | Nearest Neighbor Search: A Database Perspective Apostolos N. Papadopoulos ; Yannis Manolopoulos |
| title_full_unstemmed | Nearest Neighbor Search: A Database Perspective Apostolos N. Papadopoulos ; Yannis Manolopoulos |
| title_short | Nearest Neighbor Search: |
| title_sort | nearest neighbor search a database perspective |
| title_sub | A Database Perspective |
| topic | Vektorraum (DE-588)4130622-3 gnd Nächste-Nachbarn-Problem (DE-588)4376579-8 gnd Abfrage (DE-588)4198788-3 gnd Suchverfahren (DE-588)4132315-4 gnd Datenbanksystem (DE-588)4113276-2 gnd |
| topic_facet | Vektorraum Nächste-Nachbarn-Problem Abfrage Suchverfahren Datenbanksystem |
| url | https://doi.org/10.1007/0-387-27544-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015602066&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT papadopoulosapostolosn nearestneighborsearchadatabaseperspective AT manolopoulosyannis nearestneighborsearchadatabaseperspective |