Recommending Multidimensional Spatial OLAP Queries

Layouni, Olfa; Alahmari, Fahad; Akaichi, Jalel

doi:10.1007/978-3-319-39345-2_35

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Assume now that the Ministry of Labor, based on the data of a previous census, has set-up some goals for the improvement of labor. Assume also that per combination of education type and workplace, a specific goal, say W eekly W orking T arget, has been assigned, saying that if the average number of weekly hours at work is in the area of [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] for any category, then this is Expected behavior, whereas any other amount outside this domain is either Low, or Excessive. This is exactly what business analysts call a Key Performance Indicator (KPI) Then, assume that the analyst of the Ministry wishes to evaluate the situation based on these goals, and in fact, in more details than the aggregate summary of Example 1.…”

Section: The Vision In a Nutshellmentioning

confidence: 99%

“…The suggestion can be based on the user's profile, history of queries, history of other users' queries, or other information. Query recommendation has recently attracted many attentions for Interactive Database Exploration [44,27,32] and particularly in the case of OLAP exploration of data cubes [45,46,47,30,31,48,49], including approaches applied to spatial OLAP [50,51]. See [44,52,31,53] for a broader discussion.…”

Section: Query Recommendationsmentioning

confidence: 99%

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Beyond roll-up’s and drill-down’s: An intentional analytics model to reinvent OLAP

Vassiliadis

Marcel

Rizzi

2019

Information Systems

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This paper structures a novel vision for OLAP by fundamentally redefining several of the pillars on which OLAP has been based for the last 20 years. We redefine OLAP queries, in order to move to higher degrees of abstraction from roll-up's and drill-down's, and we propose a set of novel intentional OLAP operators, namely, describe, assess, explain, predict, and suggest, which express the user's need for results. We fundamentally redefine what a query answer is, and escape from the constraint that the answer is a set of tuples; on the contrary, we complement the set of tuples with models (typically, but not exclusively, results of data mining algorithms over the involved data) that concisely represent the internal structure or correlations of the data. Due to the diverse nature of the involved models, we come up (for the first time ever, to the best of our knowledge) with a unifying framework for them, that places its pillars on the extension of each data cell of a cube with information about the models that pertain to it -practically converting the small parts that build up the models to data that annotate each cell. We exploit this data-to-model mapping to provide highlights of the data, by isolating data and models that maximize the delivery of new information to the user. We introduce a novel method for assessing the surprise that a new query result brings to the user, with respect to the information contained in previous results the user has seen via a new interestingness measure. The individual parts of our proposal are integrated in a new data model for OLAP, which we call the Intentional Analytics Model. We complement our contribution with a list of significant open problems for the community to address.

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