Pisa

Huang, Xiangdong; Wang, Jianmin; Wong, Raymond K.; Zhang, Jinrui; Wang, Chen

doi:10.1145/2983323.2983775

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…In Reference, 17 the proposed Bi‐temporal Timeline Index supports to process bi‐temporal operations by utilizing a single‐dimension temporal index and maintaining an application time Event Map and a set of Visibility Bitmaps for every application time dimension. This index outperforms other temporal and spatial indexes by orders of magnitudes 18‐20 . Beside, there are some other researches in traditional temporal DB.…”

Section: Related Workmentioning

confidence: 92%

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Timo: In‐memory temporal query processing for big temporal data

Zheng

Liu

Wang

et al. 2021

Concurrency and Computation

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Today's internet applications generate massive temporal data anywhere and anytime. Although some disk‐based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in‐memory temporal query and analytic model for big temporal data. Firstly, a space‐efficient temporal index is proposed to support more efficiently query performance with less memory space than the state‐of‐art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark‐based temporal systems in terms of both query latency and throughput.

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Section: Related Workmentioning

confidence: 92%

“…This index outperforms other temporal and spatial indexes by orders of magnitudes. [18][19][20] Beside, there are some other researches in traditional temporal DB. All of them are either disk-based or single-machine-based.…”

Section: Temporal Indexmentioning

confidence: 99%

Timo: In‐memory temporal query processing for big temporal data

Zheng

Liu

Wang

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

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Cloud based Real-Time and Low Latency Scientific Event Analysis

Yang

Meng

2018

2018 IEEE International Conference on Big Data (Big Data)

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Astronomy is well recognized as big data driven science. As the novel observation infrastructures are developed, the sky survey cycles have been shortened from a few days to a few seconds, causing data processing pressure to shift from offline to online. However, existing scientific databases focus on offline analysis of long-term historical data, not real-time and low latency analysis of large-scale newly arriving data.In this paper, a cloud based method is proposed to efficiently analyze scientific events on large-scale newly arriving data. The solution is implemented as a highly efficient system, namely Aserv. A set of compact data store and index structures are proposed to describe the proposed scientific events and a typical analysis pattern is formulized as a set of query operations. Domain aware filter, accuracy aware data partition, highly efficient index and frequently used statistical data designs are four key methods to optimize the performance of Aserv. Experimental results under the typical cloud environment show that the presented optimization mechanism can meet the low latency demand for both large data insertion and scientific event analysis. Aserv can insert 3.5 million rows of data within 3 seconds and perform the heaviest query on 6.7 billion rows of data also within 3 seconds. Furthermore, a performance model is given to help Aserv choose the right cloud resource setup to meet the guaranteed real-time performance requirement.

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