Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs
We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wei-Kleiner, Fang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
22 |
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| Übergeordnetes Werk: |
Enthalten in: 1190 poster EVALUATION OF DEFORMABLE IMAGE CO-REGISTRATION IN ADAPTIVE IMRT FOR HEAD AND NECK CANCER - 2011, JCSS, San Diego, Calif. [u.a.] |
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| Übergeordnetes Werk: |
volume:82 ; year:2016 ; number:1 ; pages:23-44 ; extent:22 |
| Links: |
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| DOI / URN: |
10.1016/j.jcss.2015.06.008 |
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ELV035599251 |
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| 520 | |a We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. | ||
| 520 | |a We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. | ||
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| 650 | 7 | |a Shortest path |2 Elsevier | |
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10.1016/j.jcss.2015.06.008 doi GBVA2016022000007.pica (DE-627)ELV035599251 (ELSEVIER)S0022-0000(15)00070-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 570 540 VZ Wei-Kleiner, Fang verfasserin aut Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs 2016transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. Graph indexing Elsevier k Nearest Neighbors problems Elsevier Shortest path Elsevier Tree decomposition Elsevier Graphs algorithms Elsevier Enthalten in Elsevier 1190 poster EVALUATION OF DEFORMABLE IMAGE CO-REGISTRATION IN ADAPTIVE IMRT FOR HEAD AND NECK CANCER 2011 JCSS San Diego, Calif. [u.a.] (DE-627)ELV010661603 volume:82 year:2016 number:1 pages:23-44 extent:22 https://doi.org/10.1016/j.jcss.2015.06.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_39 GBV_ILN_62 GBV_ILN_90 GBV_ILN_120 GBV_ILN_127 GBV_ILN_227 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2094 AR 82 2016 1 23-44 22 045F 004 |
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10.1016/j.jcss.2015.06.008 doi GBVA2016022000007.pica (DE-627)ELV035599251 (ELSEVIER)S0022-0000(15)00070-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 570 540 VZ Wei-Kleiner, Fang verfasserin aut Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs 2016transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. Graph indexing Elsevier k Nearest Neighbors problems Elsevier Shortest path Elsevier Tree decomposition Elsevier Graphs algorithms Elsevier Enthalten in Elsevier 1190 poster EVALUATION OF DEFORMABLE IMAGE CO-REGISTRATION IN ADAPTIVE IMRT FOR HEAD AND NECK CANCER 2011 JCSS San Diego, Calif. [u.a.] (DE-627)ELV010661603 volume:82 year:2016 number:1 pages:23-44 extent:22 https://doi.org/10.1016/j.jcss.2015.06.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_39 GBV_ILN_62 GBV_ILN_90 GBV_ILN_120 GBV_ILN_127 GBV_ILN_227 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2094 AR 82 2016 1 23-44 22 045F 004 |
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10.1016/j.jcss.2015.06.008 doi GBVA2016022000007.pica (DE-627)ELV035599251 (ELSEVIER)S0022-0000(15)00070-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 570 540 VZ Wei-Kleiner, Fang verfasserin aut Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs 2016transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. Graph indexing Elsevier k Nearest Neighbors problems Elsevier Shortest path Elsevier Tree decomposition Elsevier Graphs algorithms Elsevier Enthalten in Elsevier 1190 poster EVALUATION OF DEFORMABLE IMAGE CO-REGISTRATION IN ADAPTIVE IMRT FOR HEAD AND NECK CANCER 2011 JCSS San Diego, Calif. [u.a.] (DE-627)ELV010661603 volume:82 year:2016 number:1 pages:23-44 extent:22 https://doi.org/10.1016/j.jcss.2015.06.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_39 GBV_ILN_62 GBV_ILN_90 GBV_ILN_120 GBV_ILN_127 GBV_ILN_227 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2094 AR 82 2016 1 23-44 22 045F 004 |
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10.1016/j.jcss.2015.06.008 doi GBVA2016022000007.pica (DE-627)ELV035599251 (ELSEVIER)S0022-0000(15)00070-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 570 540 VZ Wei-Kleiner, Fang verfasserin aut Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs 2016transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. Graph indexing Elsevier k Nearest Neighbors problems Elsevier Shortest path Elsevier Tree decomposition Elsevier Graphs algorithms Elsevier Enthalten in Elsevier 1190 poster EVALUATION OF DEFORMABLE IMAGE CO-REGISTRATION IN ADAPTIVE IMRT FOR HEAD AND NECK CANCER 2011 JCSS San Diego, Calif. [u.a.] (DE-627)ELV010661603 volume:82 year:2016 number:1 pages:23-44 extent:22 https://doi.org/10.1016/j.jcss.2015.06.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_39 GBV_ILN_62 GBV_ILN_90 GBV_ILN_120 GBV_ILN_127 GBV_ILN_227 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2094 AR 82 2016 1 23-44 22 045F 004 |
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10.1016/j.jcss.2015.06.008 doi GBVA2016022000007.pica (DE-627)ELV035599251 (ELSEVIER)S0022-0000(15)00070-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 570 540 VZ Wei-Kleiner, Fang verfasserin aut Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs 2016transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. Graph indexing Elsevier k Nearest Neighbors problems Elsevier Shortest path Elsevier Tree decomposition Elsevier Graphs algorithms Elsevier Enthalten in Elsevier 1190 poster EVALUATION OF DEFORMABLE IMAGE CO-REGISTRATION IN ADAPTIVE IMRT FOR HEAD AND NECK CANCER 2011 JCSS San Diego, Calif. [u.a.] (DE-627)ELV010661603 volume:82 year:2016 number:1 pages:23-44 extent:22 https://doi.org/10.1016/j.jcss.2015.06.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_39 GBV_ILN_62 GBV_ILN_90 GBV_ILN_120 GBV_ILN_127 GBV_ILN_227 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2094 AR 82 2016 1 23-44 22 045F 004 |
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Enthalten in 1190 poster EVALUATION OF DEFORMABLE IMAGE CO-REGISTRATION IN ADAPTIVE IMRT FOR HEAD AND NECK CANCER San Diego, Calif. [u.a.] volume:82 year:2016 number:1 pages:23-44 extent:22 |
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Tree decomposition-based indexing for efficient shortest path and nearest neighbors query answering on graphs |
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We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. |
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We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. |
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We propose TEDI, an indexing for solving shortest path, and k Nearest Neighbors (kNN) problems. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node contains vertices. The shortest paths are stored in such nodes. These local shortest paths together with the tree structure constitute the index of the graph. Based on this index, algorithms can be executed to solve the shortest path, as well as the kNN problem more efficiently. Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering. |
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