Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Paul W. Olsen"'
Publikováno v:
Journal of Logic and Computation. 27:961-983
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9185d6c1dd6c2464119bfc8dbf1c75e0
https://doi.org/10.1093/logcom/exv071
https://doi.org/10.1093/logcom/exv071
Publikováno v:
Encyclopedia of Database Systems ISBN: 9781489979933
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0239517f7226f310eed1ae1efbd4904
https://doi.org/10.1007/978-1-4899-7993-3_80703-1
https://doi.org/10.1007/978-1-4899-7993-3_80703-1
Publikováno v:
Encyclopedia of Database Systems ISBN: 9781489979933
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::173ac3e6d5297be0b07472a7198d47e1
https://doi.org/10.1007/978-1-4899-7993-3_80751-1
https://doi.org/10.1007/978-1-4899-7993-3_80751-1
Autor:
Jeremy Birnbaum, Jeong-Hyon Hwang, Sean R. Spillane, Alan G. Labouseur, Jayadevan Vijayan, Wook-Shin Han, Paul W. Olsen
Publikováno v:
Distributed and Parallel Databases. 33:479-514
From sensor networks to transportation infrastructure to social networks, we are awash in data. Many of these real-world networks tend to be large ("big data") and dynamic, evolving over time. Their evolution can be modeled as a series of graphs. Tra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a8b5b7befb179900c0861ac89b98891
https://doi.org/10.1007/s10619-014-7140-3
https://doi.org/10.1007/s10619-014-7140-3
Publikováno v:
GeoInformatica. 18:435-460
GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f364704dac7a56804cfecac69afaa29
https://doi.org/10.1007/s10707-013-0184-0
https://doi.org/10.1007/s10707-013-0184-0
Publikováno v:
WWW (Companion Volume)
Evolving networks can be modeled as series of graphs that represent those networks at different points in time. Our G* system enables efficient storage and querying of these graph snapshots by taking advantage of their commonalities. In extending G*
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f46399005b6d700f468f0828f9e51355
https://doi.org/10.1145/2567948.2577026
https://doi.org/10.1145/2567948.2577026
Publikováno v:
ICDE
Many of today's applications can benefit from the discovery of the most central entities in real-world networks. This paper presents a new technique that efficiently finds the k most central entities in terms of closeness centrality. Instead of compu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a3341b54c54ee1c2100017434ff9125
https://doi.org/10.1109/icde.2014.6816651
https://doi.org/10.1109/icde.2014.6816651
Autor:
Yuchao Ma, Paul W. Olsen, S. S. Ravi, Jayadevan Vijayan, Kyuseo Park, Jeremy Birnbaum, Jeong-Hyon Hwang, Jonathan Muckell, Catherine T. Lawson
Publikováno v:
SIGSPATIAL/GIS
Trajectory compression algorithms enable efficient transmission, storage, and processing of trajectory data by eliminating redundant information. While a large number of compression algorithms have been developed, there is no comprehensive and conven
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd4fc2edd8cdb339bafd85384d907ad8
https://doi.org/10.1145/2525314.2525326
https://doi.org/10.1145/2525314.2525326
Publikováno v:
COM.Geo
Trajectory compression algorithms eliminate redundant information in the history of a moving object. Such compression enables efficient transmission, storage, and processing of trajectory data. Although a number of compression algorithms have been pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd969c0b0018030d1486e9addaac8f48
https://doi.org/10.1109/comgeo.2013.5
https://doi.org/10.1109/comgeo.2013.5