Aggregates in the temporal query language TQuel

Snodgrass, Richard T.; Gómez, Silvia Alicia; McKenzie, L. Edwin

doi:10.1109/69.243512

Cited by 55 publications

(26 citation statements)

References 36 publications

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“…The idea of maximally-fragmented slicing is similar to that used to define the semantics of τ XQuery queries [19], which adapted the idea of constant periods originally introduced to evaluate (sequenced) temporal aggregates [20].…”

Section: Maximally-fragmented Slicingmentioning

confidence: 99%

Temporal Support for Persistent Stored Modules

Snodgrass

Gao

Zhang

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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Abstract-We show how to extend temporal support of SQL to the Turing-complete portion of SQL, that of persistent stored modules (PSM). Our approach requires minor new syntax beyond that already in SQL/Temporal to define and to invoke PSM procedures and functions, thereby extending the current, sequenced, and non-sequenced semantics of queries to such routines. Temporal upward compatibility (existing applications work as before when one or more tables are rendered temporal) is ensured. We provide a transformation that converts Temporal SQL/PSM to conventional SQL/PSM. To support sequenced evaluation of stored functions and procedures, we define two different slicing approaches, maximal slicing and per-statement slicing. We compare these approaches empirically using a comprehensive benchmark and provide a heuristic for choosing between them.

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Section: Maximally-fragmented Slicingmentioning

confidence: 99%

Temporal Support for Persistent Stored Modules

Snodgrass

Gao

Zhang

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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“…Moving-window temporal aggregation (MWTA) (first introduced in TSQL [9] and later also termed cumulative temporal aggregation [11,16]), extends ITA aggregation by computing for each time instant t the aggregate functions over all tuples that hold in a window "around" t. Just like ITA it is prone to returning large result relations.…”

Section: Related Workmentioning

confidence: 99%

“…Span temporal aggregation (STA) [11] allows to control the result size by partitioning the time line into predefined intervals. For each such interval, a single result tuple is produced by evaluating the aggregate functions over all argument tuples that overlap that interval.…”

Section: Related Workmentioning

confidence: 99%

Parsimonious temporal aggregation

2011

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Temporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n2pc) time and O(n2) space, where n is the size of the input relation and p is the number of aggregation functions in the query. Second, the more efficient gPTA algorithm computes an approximate solution by greedily merging the most similar ITA result tuples, which, however, does not guarantee a compression with a minimal total error. gPTA intermingles the two steps of PTA and avoids large intermediate results. The compression step of gPTA runs in O(np log(c + ?)) time and O(c + ?) space, where ? is a small buffer for ""look ahead"". An empirical evaluation shows good results: considerable reductions of the result size introduce only small errors, and gPTA scales to large data sets and is only slightly worse than the exact solution of PTA. Parsimonious Temporal Aggregation Juozas Gordevičius Johann Gamper Michael BöhlenFree University of Bozen-Bolzano, Italy {gordevicius,gamper,boehlen}@inf.unibz.it ABSTRACTTemporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n 2 pc) time and O(n 2 ) space, where n is the size of th...

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“…The importance of such need has been recognized by several database research groups, and temporal database models and query languages have been developed and reported in the literature [11], [22]. In fact, there are several temporal query languages supporting temporal aggregation [20]. However, temporal data and queries provide many unique characteristics and challenges for query processing and optimization.…”

mentioning

confidence: 99%

“…Then, aggregate values are computed over these groups. In general, temporal grouping is done by two types of partitioning [20]: span grouping and instant grouping. Span grouping is based on a defined length in time, such as week or month, and is independent of temporal attribute values of database tuples.…”

mentioning

confidence: 99%

Efficient algorithms for large-scale temporal aggregation

Moon¹,

Lopez

Immanuel³

2003

IEEE Trans. Knowl. Data Eng.

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Abstract-The ability to model time-varying natures is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping. In this paper, we introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory. Third, the parallel algorithm designed on a shared-nothing architecture achieves scalable performance by delivering nearly linear scale-up and speed-up, even at the presence of data skew. The contributions made in this paper are particularly important because the rate of increase in database size and response time requirements has out-paced advancements in processor and mass storage technology.Index Terms-Temporal databases, temporal aggregation, scalable query processing, data partitioning, balanced tree algorithm, merge-sort algorithm, temporal query processing, aggregate queries.

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Aggregates in the temporal query language TQuel

Cited by 55 publications

References 36 publications

Temporal Support for Persistent Stored Modules

Temporal Support for Persistent Stored Modules

Parsimonious temporal aggregation

Efficient algorithms for large-scale temporal aggregation

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