Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Kanat Tangwongsan"'
Publikováno v:
The VLDB Journal. 30:933-957
Sliding-window aggregation is a widely-used approach for extracting insights from the most recent portion of a data stream. While aggregations of interest can usually be expressed as binary operators that are associative, they are not necessarily com
Publikováno v:
Proceedings of the VLDB Endowment. 12:1167-1180
Sliding-window aggregation derives a user-defined summary of the most-recent portion of a data stream. For in-order streams, each window change can be handled in O (1) time even when the aggregation operator is not invertible. But streaming data ofte
Publikováno v:
SPAA
Algorithms for dynamically maintaining minimum spanning trees (MSTs) have received much attention in both the parallel and sequential settings. While previous work has given optimal algorithms for dense graphs, all existing parallel batch-dynamic alg
Autor:
Srikanta Tirthapura, Kanat Tangwongsan
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030293994
Euro-Par
Euro-Par
This paper investigates parallel random sampling from a potentially-unending data stream whose elements are revealed in a series of element sequences (minibatches). While sampling from a stream was extensively studied sequentially, not much has been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c92b28723414f1ad677fc754fb1e07bf
https://doi.org/10.1007/978-3-030-29400-7_32
https://doi.org/10.1007/978-3-030-29400-7_32
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-1
We present new algorithms for k-means clustering on a data stream with a focus on providing fast responses to clustering queries. Compared to the state-of-the-art, our algorithms provide substantial improvements in the query time for cluster centers
Publikováno v:
Encyclopedia of Big Data Technologies ISBN: 9783319639628
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a350b267cffa7bc072d21dbf03b9494
https://doi.org/10.1007/978-3-319-63962-8_157-1
https://doi.org/10.1007/978-3-319-63962-8_157-1
Publikováno v:
Proceedings of the VLDB Endowment. 8:702-713
Stream processing is gaining importance as more data becomes available in the form of continuous streams and companies compete to promptly extract insights from them. In such applications, sliding-window aggregation is a central operator, and increme
Publikováno v:
DEBS
Stream processing is important for analyzing continuous streams of data in real time. Sliding-window aggregation is both needed for many streaming applications and surprisingly hard to do efficiently. Picking the wrong aggregation algorithm causes po
Publikováno v:
DEBS
Sliding-window aggregation is a widely-used approach for extracting insights from the most recent portion of a data stream. The aggregations of interest can usually be cast as binary operators that are associative, but they are not necessarily commut
Publikováno v:
ICDE
We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. Compared to the current state-of-the-art, our methods provide substantial improvement in the query time for cluster centers while re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cc0661218d9f254403705bd72e88017