The MD-join: an operator for complex OLAP

Chatziantoniou, Damianos; Johnson, Theodore; Akinde, Michael O.; Kim, S.

doi:10.1109/icde.2001.914866

Cited by 35 publications

(36 citation statements)

References 9 publications

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“…This paper presents a new aggregation operator, the Temporal Multi-Dimensional Aggregation (TMDA) operator, that leverages recent advances in multi-dimensional query processing [1][2][3] to apply to interval-valued data. The TMDA operator generalizes a variety of previously proposed aggregation operators.…”

Section: Resultsmentioning

confidence: 99%

“…Our TMDA operator, which extends the multi-dimensional join operator [3] to support temporal aggregation, overcomes these limitations and generalizes the aggregation operators discussed above. It decouples the partitioning of the timeline from the grouping of the input tuples, thus allowing to specify result tuples over possibly overlapping intervals and to control the size of the result relation.…”

Section: Introductionmentioning

confidence: 99%

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Multi-dimensional Aggregation for Temporal Data

Böhlen

Gamper

Jensen

2006

Lecture Notes in Computer Science

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Abstract. Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state. This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses that the data holds for each point in the interval, as well as the case where the data holds only for the entire interval, but must be adjusted to apply to sub-intervals. The paper reports on an implementation of the new operator and on an empirical study that indicates that the operator scales to large data sets and is competitive with respect to other temporal aggregation algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-dimensional Aggregation for Temporal Data

Böhlen

Gamper

Jensen

2006

Lecture Notes in Computer Science

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“…Our naive approach is very similar to the SegmentApply operator, and hence our optimization follows some of the steps of [9]. When dealing with the universal operator, the plans generated by the definition of all(X, Y ) as |X| = |X ∩ Y | are very similar to those of [6]. They implement their approach using a new operator called the Multidimensional join operator.…”

Section: Related Researchmentioning

confidence: 99%

Efficient implementation of generalized quantification in relational query languages

Badia

Cao

2013

Proc. VLDB Endow.

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We present research aimed at improving our understanding of the use and implementation of quantification in relational query languages in general and SQL in particular. In order to make our results as general as possible, we use the framework of Generalized Quantification. Generalized Quantifiers (GQs) are high-level, declarative logical operators that in the past have been studied from a theoretical perspective. In this paper we focus on their practical use, showing how to incorporate a dynamic set of GQs in relational query languages, how to implement them efficiently and use them in the context of SQL. We present experimental evidence of the performance of the approach, showing that it improves over traditional (relational) approaches.

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“…Their use in online analytical processing (OLAP) has been investigated in the work of Chatziantoniou et al [10,11,9]. Single pass computation of summaries for correlated aggregates on streams was first considered by Gehrke, Korn, and Srivastava [17], who provided heuristics for approximating the correlated sum of elements, but these did not come with a provable bound on the quality of the answers.…”

Section: Related Workmentioning

confidence: 99%

A General Method for Estimating Correlated Aggregates Over a Data Stream

Tirthapura

Woodruff

2014

Algorithmica

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On a stream S" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">SS of two dimensional data items (x,y)" role="presentation" style="boxsizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">(x,y)(x,y) where x" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letterspacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">xx is an item identifier and y" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; minheight: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">yy is a numerical attribute, a correlated aggregate query C(σ,AGG,S)" role="presentation" style="box-sizing: border-box; display: inlinetable; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">C(σ,AGG,S)C(σ,AGG,S) asks to first apply a selection predicate σ" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">σσ along the y" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; minheight: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">yy dimension, followed by an aggregation AGG" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">AGGAGG along the x" role="presentation" style="...

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The MD-join: an operator for complex OLAP

Cited by 35 publications

References 9 publications

Multi-dimensional Aggregation for Temporal Data

Multi-dimensional Aggregation for Temporal Data

Efficient implementation of generalized quantification in relational query languages

A General Method for Estimating Correlated Aggregates Over a Data Stream

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