Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Tin, Vu"'
Autor:
Tin Vu, Ahmed Eldawy
Publikováno v:
Frontiers in Big Data, Vol 3 (2020)
The rapid growth of big spatial data urged the research community to develop several big spatial data systems. Regardless of their architecture, one of the fundamental requirements of all these systems is to spatially partition the data efficiently a
Externí odkaz:
https://doaj.org/article/72dabb27aa5c4009ba26370f08e2c814
Publikováno v:
Proceedings of the VLDB Endowment. 15:713-726
Big spatial data has become ubiquitous, from mobile applications to satellite data. In most of these applications, data is continuously growing to huge volumes. Existing systems for big spatial data organize records at either the record-level or bloc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a83bbaf47fd3b77cfce3c9c87dc61ff
https://doi.org/10.14778/3494124.3494150
https://doi.org/10.14778/3494124.3494150
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2786261aa0cea02016f74b6e99ab2ddf
https://doi.org/10.1145/3511808.3557712
https://doi.org/10.1145/3511808.3557712
Autor:
Akil Sevim, Ahmed Eldawy, Michael J. Carey, Tin Vu, Vassilis J. Tsotras, Mehnaz Tabassum Mahin, Ian Maxon
Publikováno v:
SpatialAPI@SIGSPATIAL
There is immense potential with spatial data, which is even more significant when combined with temporal or textual features, or both. However, it is expensive to store and analyze spatial data, and it is even more challenging with the combined featu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a48eb0d1a80bd183d1dfd1162a926fa
https://doi.org/10.1145/3486189.3490018
https://doi.org/10.1145/3486189.3490018
Autor:
Yaming Zhang, Ahmed Eldawy, Vagelis Hristidis, Samriddhi Singla, Saheli Ghosh, Akil Sevim, A. B. Siddique, Tin Vu, Majid Saeedan, Ganesh Sivaram
Publikováno v:
CIKM
This paper introduces the open-source Beast system for scalable exploratory data science on big spatio-temporal data. Beast is based on well-established research and has been released to assist the research community with analyzing big spatio-tempora
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::762d1a0c46181ff2da7ca37d4bf3d456
https://doi.org/10.1145/3459637.3481897
https://doi.org/10.1145/3459637.3481897
Publikováno v:
SIGSPATIAL/GIS
The importance and complexity of spatial join resulted in many join algorithms, some of which run on big-data platforms such as Hadoop and Spark. This paper proposes the first machine-learning-based query optimizer for spatial join operation which ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::713989f278fe156ee8bd2464e09ef7e1
https://hdl.handle.net/11562/1052183
https://hdl.handle.net/11562/1052183
Publikováno v:
SIGSPATIAL/GIS
User geocoding queries in map applications often contain noisy tokens such as typos in street, city name, wrong postal code, redundant words due to copy-paste action, etc. This issue becomes worse with the rapid growth of mobile devices, where errors
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f461afc79bffbb81251664dd139461b
https://doi.org/10.1145/3397536.3422201
https://doi.org/10.1145/3397536.3422201
Publikováno v:
SIGSPATIAL/GIS
This demonstration presents a web-based generator for spatial data. This generator allows users to choose from a wide range of spatial data distributions and configure the cardinality of the data and the distribution parameters. It then provides thre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd484cc584581aa55aeffac8a64e4d9e
http://hdl.handle.net/11562/1031395
http://hdl.handle.net/11562/1031395
This article explores the use of deep learning to choose an appropriate spatial partitioning technique for big data. The exponential increase in the volumes of spatial datasets resulted in the development of big spatial data frameworks. These systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d0f618eb8fb62291c5464c5b4aabd8b
https://hdl.handle.net/11562/1031363
https://hdl.handle.net/11562/1031363
Autor:
Tin Vu
Publikováno v:
SIGMOD Conference
In recent decades, we observed the rapid growth of several big data platforms. Each of them is designed for specific demands. For instance, Spark can efficiently process iterative queries, while Storm is designed for in-memory processing. In this con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ccb65a89c11ce60d932b6e7b0cd6ee5
https://doi.org/10.1145/3299869.3300104
https://doi.org/10.1145/3299869.3300104