Load shedding for multi-way stream joins based on arrival order patterns

Kwon, Taehyung; Lee, Ki Yong; Kim, Myoung Ho

doi:10.1007/s10844-010-0138-z

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…They proposed MJoin, a multi-way symmetric hash join operator, and showed that the m-way hash join is faster than any tree of binary join operators. The idea has been developed to AMJoin proposed by Kwon et al (2009Kwon et al ( , 2011. AMJoin improves the performance of MJoin by detecting join failures in advance.…”

Section: Window Joinsmentioning

confidence: 99%

“…Most of the previous algorithms have considered this type of windows for a join, including Das et al (2003), Hammad et al (2003a), Golab and Oszu (2003b), Kang et al (2003), Kwon et al (2009Kwon et al ( , 2011, Srivastava and Widom (2004b), Viglas et al (2003). On the other hands, there are other type of windows, whose boundaries are updated periodically (e.g., whenever a predefined time elapses).…”

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confidence: 99%

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Time-slide window join over data streams

et al. 2014

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The join is an important operator in processing data streams. To produce outputs continuously over unbounded data streams, sliding windows are generally used to limit the scope of the join at a certain time. In the existing join algorithms, only a simple type of windows have been considered, which are updated whenever a new data item arrives on any input stream. On the other hand, a more common type of windows have not been addressed yet, whose intervals are updated periodically, i.e., slid by a predefined time interval. In this paper, we consider the time-slide windows in joining multiple data streams. The algorithm for the time-slide window join can vary according to (i) how frequently the join is evaluated and (ii) which structure is used for windowing. Regarding this, possible algorithms are discussed, and experimental results that compare their performances are provided in this paper.

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Section: Window Joinsmentioning

confidence: 99%

mentioning

confidence: 99%

Time-slide window join over data streams

et al. 2014

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“…They proposed MJoin, a multi-way symmetric hash join operator and showed that the m-way hash join is faster than any tree of binary join operators. That idea was developed into AMJoin, proposed by Kwon et al [28,29]. AMJoin improves the performance of MJoin, by detecting join failures in advance.…”

Section: Algorithm 1 Sliding Window Equijoin Whenever a New Tuple S mentioning

confidence: 99%

“…• Pattern-based model [29]: The priority of a tuple is determined based on its arrival pattern in data streams (e.g., the order of streams in which the tuple appears).…”

Section: Load Sheddingmentioning

confidence: 99%

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A Review of Window Query Processing for Data Streams

Kim¹,

Kim²

2013

Journal of Computing Science and Engineering

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In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time.The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control.

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Efficient Evaluation of Window Join Aggregates in Data Stream Processing

Kim

2015

Computer Science and Its Applications

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Load shedding for multi-way stream joins based on arrival order patterns

Cited by 7 publications

References 4 publications

Time-slide window join over data streams

Time-slide window join over data streams

A Review of Window Query Processing for Data Streams

Efficient Evaluation of Window Join Aggregates in Data Stream Processing

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