Overlap interval partition join

Dignös, Anton; Böhlen, Michael H.; Gamper, Johann

doi:10.1145/2588555.2612175

Cited by 38 publications

(58 citation statements)

References 15 publications

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“…The computation of such intervals in temporal databases is performed by adjusting the intervals of each input relation based on the tuples of the other input relation that are valid. Combining the adjusted results to identify the intervals when, for example, tuples of both relations are valid [14], and concatenating their lineages for probability computation [1], [14] must be performed with joins. In this section, we introduce the lineage-aware temporal window, a novel mechanism that directly associates candidate output intervals with the lineage expressions of the valid input tuples of both relations.…”

Section: Lineage-aware Temporal Windowsmentioning

confidence: 99%

“…The flexibility of lineage-aware temporal windows relies on two characteristics: the lineages of valid tuples of each input relation are directly associated with a maximal interval, and they are separately recorded. These two characteristics allow for an efficient computation of the output tuples by using simple filtering conditions and lineage-concatenating functions instead of the additional joins performed in related approaches [1], [14]. Given a TP set operation, λ r and λ s can be used to determine whether fact F and interval [winTs, winTe) yield an output tuple.…”

Section: Lineage-aware Temporal Windowsmentioning

confidence: 99%

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Supporting Set Operations in Temporal-Probabilistic Databases

Papaioannou

Theobald

Boehlen

2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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In temporal-probabilistic (TP) databases, the combination of the temporal and the probabilistic dimension adds significant overhead to the computation of set operations. Although set queries are guaranteed to yield linearly sized output relations, existing solutions exhibit quadratic runtime complexity. They suffer from redundant interval comparisons and additional joins for the formation of lineage expressions. In this paper, we formally define the semantics of set operations in TP databases and study their properties. For their efficient computation, we introduce the lineage-aware temporal window, a mechanism that directly binds intervals with lineage expressions. We suggest the lineage-aware window advancer (LAWA) for producing the windows of two TP relations in linearithmic time, and we implement all TP set operations based on LAWA. By exploiting the flexibility of lineage-aware temporal windows, we perform direct filtering of irrelevant intervals and finalization of output lineage expressions and thus guarantee that no additional computational cost or buffer space is needed. A series of experiments over both synthetic and real-world datasets show that (a) our approach has predictable performance, depending only on the input size and not on the number of time intervals per fact or their overlap, and that (b) it outperforms state-of-the-art approaches in both temporal and probabilistic databases.

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Section: Lineage-aware Temporal Windowsmentioning

confidence: 99%

Section: Lineage-aware Temporal Windowsmentioning

confidence: 99%

Supporting Set Operations in Temporal-Probabilistic Databases

Papaioannou

Theobald

Boehlen

2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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“…The management of temporal data in DBMSs has been an active research area for several decades, focusing primarily on temporal data models and query languages (e.g., [Abiteboul et al 1996;Böhlen and Jensen 2003;Date and Darwen 2002;]) as well as efficient algorithms for specific operators (e.g., temporal join [Segev 1993;Dignös et al 2014;Piatov et al 2016] and temporal aggregation [Böhlen et al 2006b;Vega Lopez et al 2005;.…”

Section: Related Workmentioning

confidence: 99%

Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries

Dignös

Böhlen

Gamper

et al. 2016

ACM Trans. Database Syst.

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Many databases contain temporal, or time-referenced, data and use intervals to capture the temporal aspect. While SQL-based database management systems (DBMSs) are capable of supporting the management of interval data, the support they offer can be improved considerably. A range of proposed temporal data models and query languages offer ample evidence to this effect. Natural queries that are very difficult to formulate in SQL are easy to formulate in these temporal query languages. The increased focus on analytics over historical data where queries are generally more complex exacerbates the difficulties and thus the potential benefits of a temporal query language. Commercial DBMSs have recently started to offer limited temporal functionality in a step-by-step manner, focusing on the representation of intervals and neglecting the implementation of the query evaluation engine. This article demonstrates how it is possible to extend the relational database engine to achieve a full-fledged, industrial-strength implementation of sequenced temporal queries, which intuitively are queries that are evaluated at each time point. Our approach reduces temporal queries to nontemporal queries over data with adjusted intervals, and it leaves the processing of nontemporal queries unaffected. Specifically, the approach hinges on three concepts: interval adjustment, timestamp propagation, and attribute scaling. Interval adjustment is enabled by introducing two new relational operators, a temporal normalizer and a temporal aligner, and the latter two concepts are enabled by the replication of timestamp attributes and the use of so-called scaling functions. By providing a set of reduction rules, we can transform any temporal query, expressed in terms of temporal relational operators, to a query expressed in terms of relational operators and the two new operators. We prove that the size of a transformed query is linear in the number of temporal operators in the original query. An integration of the new operators and the transformation rules, along with query optimization rules, into the kernel of PostgreSQL is reported. Empirical studies with the resulting temporal DBMS are covered that offer insights into pertinent design properties of the article's proposal. Many databases contain temporal, or time-referenced, data and use intervals to capture the temporal aspect. While SQL-based database management systems (DBMSs) are capable of supporting the management of interval data, the support they offer can be improved considerably. A range of proposed temporal data models and query languages offer ample evidence to this effect. Natural queries that are very difficult to formulate in SQL are easy to formulate in these temporal query languages. The increased focus on analytics over historical data where queries are generally more complex exacerbates the difficulties and thus the potential benefits of a temporal query language. Commercial DBMSs have recently started to offer limited temporal functionality in a step-by-step manner, ...

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“…The website makes a prediction for each time point and there is no other tuple in a that predicts the probability of 'Jim visiting Wengen' over an interval overlapping with [7,10). In order to manage supply and demand, we determine the probability with which the client will find available accommodation at their preferred The answer tuple ('Ann, ZAK, hotel 1 ', a 1 ∧ b 3 , [4,6), 0.49) expresses that, with probability 0.49, Ann wants to visit Zakynthos (a 1 ) and stay at hotel 1 in Zakynthos (b 3 ) during interval [4,6). It is valid over the intersection of the intervals of tuples a 1 and b 3 and it is true when both these tuples are true.…”

Section: Introductionmentioning

confidence: 99%

“…Answer tuple ('Ann, ZAK, -', a 1 , [2,4), 0.7) expresses that, with probability 0.7, Ann wants to visit Zakynthos (a 1 ) but there is no hotel available to stay there. Although the lineage and the output probability are both determined by tuple a 1 , i.e., the only tuple valid during [2,4), the interval of this output tuple is influenced by the starting point of tuple b 3 , a tuple not valid over [2,4). Over the interval [5,6) there is 0.084 probability that Ann wants to visit Zakynthos but finds no accommodation.…”

Section: Introductionmentioning

confidence: 99%

Outer and Anti Joins in Temporal-Probabilistic Databases

Papaioannou

Theobald

Boehlen

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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The result of a temporal-probabilistic (TP) join with negation includes, at each time point, the probability with which a tuple of a positive relation p matches none of the tuples in a negative relation n, for a given join condition . For the computation of TP joins with negation, we introduce generalized lineage-aware temporal windows, a mechanism that binds an interval to the lineages of all the matching valid tuples of each input relation. We compute these windows in an incremental manner, and we show that pipelined computations allow for the direct integration of our approach into PostgreSQL. We thereby alleviate the prevalent redundancies in the interval computations of existing approaches, which is proven by an extensive experimental evaluation with real-world datasets.Abstract-The result of a temporal-probabilistic (TP) join with negation includes, at each time point, the probability with which a tuple of a positive relation p matches none of the tuples in a negative relation n, for a given join condition θ. For the computation of TP joins with negation, we introduce generalized lineage-aware temporal windows, a mechanism that binds an interval to the lineages of all the matching valid tuples of each input relation. We compute these windows in an incremental manner, and we show that pipelined computations allow for the direct integration of our approach into PostgreSQL. We thereby alleviate the prevalent redundancies in the interval computations of existing approaches, which is proven by an extensive experimental evaluation with real-world datasets.

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Overlap interval partition join

Cited by 38 publications

References 15 publications

Supporting Set Operations in Temporal-Probabilistic Databases

Supporting Set Operations in Temporal-Probabilistic Databases

Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries

Outer and Anti Joins in Temporal-Probabilistic Databases

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