Evaluation of ad hoc OLAP: in-place computation

Chatziantoniou, Damianos

doi:10.1109/ssdm.1999.787619

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…The idea of grouping sets was carried over to data cubes by Ross et al [32]. EMF-SQL (extended multi-feature syntax) [6,7,28] extends the multi-feature syntax by permitting the definition of customized aggregate conditions using the definition of grouping variables.…”

Section: Aggregate Resultsmentioning

confidence: 99%

multi-dimensional aggregation

Akinde

Böhlen

Chatziantoniou

et al. 2011

Information Systems

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a b s t r a c tThe SQL:2003 standard introduced window functions to enhance the analytical processing capabilities of SQL. The key concept of window functions is to sort the input relation and to compute the aggregate results during a scan of the sorted relation. For multi-dimensional OLAP queries with aggregation groups defined by a general y condition an appropriate ordering does not exist, though, and hence expensive join-based solutions are required.In this paper we introduce y-constrained multi-dimensional aggregation (y-MDA), which supports multi-dimensional OLAP queries with aggregation groups defined by inequalities.y-MDA is not based on an ordering of the data relation. Instead, the tuples that shall be considered for computing an aggregate value can be determined by a general y condition.This facilitates the formulation of complex queries, such as multi-dimensional cumulative aggregates, which are difficult to express in SQL because no appropriate ordering exists. We present algebraic transformation rules that demonstrate how the y-MDA interacts with other operators of a multi-set algebra. Various techniques for achieving an efficient evaluation of the y-MDA are investigated, and we integrate them into concrete evaluation algorithms and provide cost formulas. An empirical evaluation with data from the TPC-H benchmark confirms the scalability of the y-MDA operator and shows performance improvements of up to one order of magnitude over equivalent SQL implementations.

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Section: Aggregate Resultsmentioning

confidence: 99%

multi-dimensional aggregation

Akinde

Böhlen

Chatziantoniou

et al. 2011

Information Systems

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show abstract

“…For each distinct value s of column state we should define a set variable as the rows of table Sales having state=s and month=1. A grouping variable, first introduced in [17] and then refined in [10,11,28], depicts precisely this idea. A grouping variable is attached to a group by clause and for each distinct value of the grouping attributes a new set variable is instantiated.…”

Section: Syntax Of Grouping Variablesmentioning

confidence: 99%

“…Dependency analysis between correlated aggregates has been treated in [11,29,16] and many ideas are common. However it is valuable to formulate this concept as an algebraic transformation.…”

Section: Combination Of Md-joinsmentioning

confidence: 99%

Using grouping variables to express complex decision support queries

Chatziantoniou

2007

Data & Knowledge Engineering

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“…The EMF query evaluation algorithm in Algorithm 4.1 is a naive implementation-many optimizations can be made (presented in length in [5]) that greatly improve performance. For example, Algorithm 4.1 can become very expensive if the mf-structure has a large number of entries, since on each scan, for every tuple, all H's entries are examined, resulting in an implicit nested-loop join.…”

Section: Optimizationmentioning

confidence: 99%

“…Extended multi-feature SQL [4][5][6] introduces a minimal, yet powerful extension of SQL, based on the concept of grouping variable [13]. EMF SQL has proven useful in expression and evaluation of complex OLAP queries in various domains (e.g.…”

Section: Introducing Extended Multi-feature Syntaxmentioning

confidence: 99%