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Kimutai Kimeli

dblp:23/4649 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data stream processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data stream processing
distributed data streams
0.112006
SlidingWindow based Multi-Join Algorithms over Distributed Data Streams · ICDE 2006
Data stream processing › stream join
sliding window join
0.112006
SlidingWindow based Multi-Join Algorithms over Distributed Data Streams · ICDE 2006

Methods — techniques the papers use, named apart from their topics

predicate filtering · 0.1distributed stream transfer · 0.1
YearPublicationVenuePosition
2006 SlidingWindow based Multi-Join Algorithms over Distributed Data Streams
abstract
This paper focuses on multi-way sliding window join (SWJoin) processing over distributed data streams. A novel Join algorithm is proposed based on two distributed data stream transfer models. To reduce the communication cost and lighten the workload on the central processor node, the algorithm filters out tuples that can’t contribute to multiway SWJoin results by transforming the join conditions of SWJoin into filtering conditions during data stream transfer. Furthermore, the algorithm guarantees that all necessary data for generating exact multi-way SWJoin results can be transmitted to the central processor node.
Jianzhong Li 0001, Kimutai Kimeli, Weiping Wang 0001
ICDE3