Detecting summarizability in OLAP

Niemi, Tapio; Niinimäki, Marko; Thanisch, Peter; Nummenmaa, Jyrki

doi:10.1016/j.datak.2013.11.001

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…In [6], Mazón et al reviewed this problem in the context of multidimensional modeling. Niemi et al [18], based on the work of [3] and [19] propose a new categorization of measures, and an algorithm to detect if an OLAP query satisfies these new summarizability conditions, for the case of additivity. Additional references in the field addressing this issue can be found in [4,5].…”

Section: Summarizabilitymentioning

confidence: 99%

Efficient repair of dimension hierarchies under inconsistent reclassification

Caniupan¹,

Vaisman²,

Arredondo³

2015

Data & Knowledge Engineering

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Section: Summarizabilitymentioning

confidence: 99%

Efficient repair of dimension hierarchies under inconsistent reclassification

Caniupan¹,

Vaisman²,

Arredondo³

2015

Data & Knowledge Engineering

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“…(1) In Section 3, we extend the notion of aggregable properties introduced in [32], as a general means to express, for any attribute of an analytic table, which aggregation functions are correctly applicable along which sets of dimension attributes. We use aggregable properties to express the semantic properties of measures previously defined in [19,26,30,36,40,50] and provide default rules to minimize the effort of end users for defining aggregable properties on analytic tables built from source data (i.e., non-analytic data). We then provide a first set of propagation rules to automatically compute aggregable properties in the results of interactive analytic queries.…”

Section: Research Contributionsmentioning

confidence: 99%

“…Describing when this aggregation is possible has been extensively studied for statistical and OLAP databases (see [34] for a survey). Focusing on function SUM, [26], [19] and [36] proposed that the designer of a fact table declares the additivity category of each measure: fully-additive measures can be summed along any dimension, semi-additive…”

Section: Aggregability Of Attributes In Analytic Tablesmentioning

confidence: 99%

“…Item 1 and the definition of the forbidden attributes X 𝑓 in Definition 9 cover the "information semantics" of an attribute A and restrict the functions and the dimensions for the aggregation of the attribute. Different categorizations have been proposed in the literature to determine the aggregation functions which are applicable to some measure attribute, such as a statistic classification of measurements [36,50,52], the attribute's aggregation behaviour [39,40], or the compatibility between the type of dimensions and the type of a measure [30]. These categorizations can also be used in our context to determine both the "applicability" of a function F and the set X 𝑓 of forbidden dimensions for a given measure attribute.…”

Section: Aggregable Properties Of Attributesmentioning

confidence: 99%

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Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries

Simon¹,

Amann²,

Liu³

et al. 2021

Preprint

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We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and BI tools to automatically detect semantically incorrect aggregate queries on analytic tables and views built by using the common analytic operations including filter, project, join, aggregate, union, difference, and pivot. We introduce aggregable properties to describe for any attribute of an analytic table, which aggregation functions correctly aggregate the attribute along which sets of dimension attributes. These properties can also be used to formally identify attributes which are summarizable with respect to some aggregation function along a given set of dimension attributes. This is particularly helpful to detect incorrect aggregations of measures obtained through the use of non-distributive aggregation functions like average and count. We extend the notion of summarizability by introducing a new generalized summarizability condition to control the aggregation of attributes after any analytic operation. Finally, we define propagation rules which transform aggregable properties of the query input tables into new aggregable properties for the result tables, preserving summarizability and generalized summarizability.

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“…In [62], the authors identify three parts of an aggregate query as: the aggregation operation, the measure that is to be aggregated, and either an aggregation level or a member for each dimension. They argue that a query can only produce meaningful results if: (1) The aggregation operation is appropriate for the measure, and (2) The measure is appropriate for the aggregation levels in the cube's dimensions.…”

Section: Definition and Characterization Of Summarizabilitymentioning

confidence: 99%

A Multidimensional Data Model with Subcategories for Expressing and Repairing Summarizability

Ariyan¹

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In multidimensional (MD) databases summarizability is a key property for obtaining interactive response times. With summarizable dimensions, pre-computed and materialized aggregate query results at lower levels of the dimension hierarchy can be used to correctly compute results at higher levels of the same hierarchy, improving efficiency. Being summarizability such a desirable property, we argue that established MD models cannot properly model the summarizability condition, and this is a consequence of the limited expressive power of the modeling languages. In addition, because of limitations in existing MD models, algorithms for deciding summarizability and cube view selection are not efficient or practical.We propose an extension to the Hurtado-Meldelzon (HM) MD model, the EHM model, that includes subcategories and explore its properties specially in addressing issues related to summarizability. We investigate the extended model as a way to directly model MD-DBs, with some clear advantages over HM models. Most importantly, EHM is -in a precise technical sense-more expressive than HM for modeling MDDBs that are subject to summarizability conditions. Moreover, given an MD aggregate query in an EHM database, we can determine in a practical way (that only requires processing the dimension schema as opposed to the instance), from which minimal subset of pre-computed cube views it can be correctly computed.Our extended model allows for a repair approach that transforms non-summarizable HM dimensions into summarizable EHM dimensions. We propose and formalize a twostep process that involves modifying both the schema and the instance of a non-summarizable HM dimension.ii

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Detecting summarizability in OLAP

Cited by 12 publications

References 25 publications

Efficient repair of dimension hierarchies under inconsistent reclassification

Efficient repair of dimension hierarchies under inconsistent reclassification

Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries

A Multidimensional Data Model with Subcategories for Expressing and Repairing Summarizability

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