Sweeping-Based Temporal Aggregation

Piatov, Danila; Helmer, Sven

doi:10.1007/978-3-319-64367-0_7

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…We introduced a cache-optimized algorithm for the overlap interval join, based on the Endpoint Index, and showed that the Timeline Index is not only a universal index that supports many operations, but that it can also outperform the state-ofthe-art specialized indexes (including [14]). The technique of sweeping-based algorithms has recently been applied to temporal aggregation as well [9,35], extending the set of operations supported by the Timeline Index even further.…”

Section: Overlap Interval Joinsmentioning

confidence: 99%

Cache-efficient sweeping-based interval joins for extended Allen relation predicates

et al. 2021

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We develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

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Section: Overlap Interval Joinsmentioning

confidence: 99%

Cache-efficient sweeping-based interval joins for extended Allen relation predicates

et al. 2021

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“…This data structure was later adapted and optimized for processing interval overlap joins [26]. Piatov et al [25] present a collection of plane-sweep algorithms that extend the timeline index with other forms of temporal aggregation, such as aggregation over fixed intervals, sliding window aggregates, and MIN/MAX aggregates. A domain partitioning technique for interval joins was proposed in [5,7], which can also be adapted and used for interval range queries, as we discuss in the next section.…”

Section: Related Workmentioning

confidence: 99%

HINT: A Hierarchical Index for Intervals in Main Memory

Christodoulou¹,

Bouros²,

Mamoulis³

2021

Preprint

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Indexing intervals is a fundamental problem, finding a wide range of applications. Recent work on managing large collections of intervals in main memory focused on overlap joins and temporal aggregation problems. In this paper, we propose novel and efficient in-memory indexing techniques for intervals, with a focus on interval range queries, which are a basic component of many search and analysis tasks. First, we propose an optimized version of a single-level (flat) domain-partitioning approach, which may have large space requirements due to excessive replication. Then, we propose a hierarchical partitioning approach, which assigns each interval to at most two partitions per level and has controlled space requirements. Novel elements of our techniques include the division of the intervals at each partition into groups based on whether they begin inside or before the partition boundaries, reducing the information stored at each partition to the absolutely necessary, and the effective handling of data sparsity and skew. Experimental results on real and synthetic interval sets of different characteristics show that our approaches are typically one order of magnitude faster than the state-of-the-art.

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“…Implementations of Temporal Operators. A large body of work has focused on the implementation of individual temporal algebra operators such as joins [11,17,32] and aggregation [5,31,33]. Some exceptions supporting multiple operators are [13,18,25].…”

Section: Related Workmentioning

confidence: 99%

Snapshot semantics for temporal multiset relations

et al. 2019

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Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K-relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations and significantly outperforms such implementations on queries that involve aggregation.

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Sweeping-Based Temporal Aggregation

Cited by 15 publications

References 10 publications

Cache-efficient sweeping-based interval joins for extended Allen relation predicates

Cache-efficient sweeping-based interval joins for extended Allen relation predicates

HINT: A Hierarchical Index for Intervals in Main Memory

Snapshot semantics for temporal multiset relations

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