QueRIE: Collaborative Database Exploration

Eirinaki, Magdalini; Abraham, Suju; Polyzotis, Neoklis; Shaikh, Naushin

doi:10.1109/tkde.2013.79

Cited by 63 publications

(44 citation statements)

References 21 publications

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“…• hybrid: combination of the two above types. QueRIE is an example of a hybrid query recommender system [32], where a "mixing factor" is used to determine the importance of the content-based strategy with respect to the collaborative one.…”

Section: Suggestmentioning

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%

See 1 more Smart Citation

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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Section: Suggestmentioning

confidence: 99%

Section: Query Recommendationsmentioning

confidence: 99%

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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“…For instance, in [11], the authors propose to describe the data space covered by a session with a vector of the tuples accessed by the queries of the session. In [23], the authors propose the notion of access area to capture the portion of the dataset a user is interested in.…”

Section: Related Workmentioning

confidence: 99%

Benchmarking Exploratory OLAP

Djedaini

Furtado

Labroche

et al. 2017

Performance Evaluation and Benchmarking. Traditional - Big Data - Interest of Things

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Abstract. Supporting interactive database exploration (IDE) is a problem that attracts lots of attention these days. Exploratory OLAP (OnLine Analytical Processing) is an important use case where tools support navigation and analysis of the most interesting data, using the best possible perspectives. While many approaches were proposed (like query recommendation, reuse, steering, personalization or unexpected data recommendation), a recurrent problem is how to assess the effectiveness of an exploratory OLAP approach. In this paper we propose a benchmark framework to do so, that relies on an extensible set of user-centric metrics that relate to the main dimensions of exploratory analysis. Namely, we describe how to model and simulate user activity, how to formalize our metrics and how to build exploratory tasks to properly evaluate an IDE system under test (SUT). To the best of our knowledge, this is the first proposal of such a benchmark. Experiments are two-fold: first we evaluate the benchmark protocol and metrics based on synthetic SUTs whose behavior is well known. Second, we concentrate on two different recent SUTs from IDE literature that are evaluated and compared with our benchmark. Finally, potential extensions to produce an industrystrength benchmark are listed in the conclusion.

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“…The fragment-based instantiation of the recommender system framework works in a similar manner to the tuple-based one [1]. The two main differences lie in the representation of the session summaries and the formulation of similarities.…”

Section: Fragment-based Query Recommendationsmentioning

confidence: 99%

Matrix Factorization Based Query Recommendation

2015

IJSR

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Database exploration is always a tedious task for the person who lacks skill in writing complex SQL queries. In order to aid such people, SQL recommendations are provided with the help of an interactive query recommendation system. The recommendations will be based on the current query, queries previously submitted by the user and the queries submitted by other users to the system. Based on this, the recommendation engine recommends the recommendation query to the user. The user can use this query as a template to formulate the query he wanted or he can submit the same. The recommended query will be like the query the user may want to write. The recommendation users the general concept of collaborative filtering method in which the recommendations will be based on the relationships between the queries submitted and the interests of the user. The use matrix factorization further improves the recommendation accuracy and thereby a better result for the user.

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QueRIE: Collaborative Database Exploration

Cited by 63 publications

References 21 publications

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

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

Benchmarking Exploratory OLAP

Matrix Factorization Based Query Recommendation

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