Query Recommendations for OLAP Discovery-Driven Analysis

Abstract

Recommending database queries is an emerging and promising field of research and is of particular interest in the domain of OLAP systems, where the user is left with the tedious process of navigating large datacubes. In this paper, the authors present a framework for a recommender system for OLAP users that leverages former users’ investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by (1) analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and (2) analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments were conducted to assess the quality of the recommendations in terms of precision and recall, as well as the efficiency of their on-line computation.

Towards Introducing User Preferences in OLAP Reporting Tool

“…There are some distinctive features in the approach proposed by the authors of this paper comparing to [6] and [7]. We may notice that in [6] authors analyze unexpected differences in data; however, in this paper we analyze logical structure of the reports.…”

“…We may notice that in [6] authors analyze unexpected differences in data; however, in this paper we analyze logical structure of the reports. In [7] both data preferences and preferences on logical structure of the reports are taken into account, however, in [7] to get recommendations, user has to state his/her preferences in a user profile first.…”

“…Users may express their preferences on OLAP queries [5]; in such case, the problem of performing time-consuming OLAP operations to find the necessary data is significantly improved. The other method of personalizing OLAP systems is to provide query recommendations to data warehouse users via investigating former sessions of the same user [6], or via collecting user preferences into a profile and processing it, while generating query recommendations [7]. Another aspect of OLAP personalization is the visual representation of data [8]: multiple layouts and visualization techniques may be interactively used for various analysis tasks.…”

Semantics and Usage Statistics for Multi-dimensional Query Expansion

“…Methods based on expectations aim at determining and guiding the user toward zones of a cube that present unexpected data, for instance by maximizing the entropy [13]. Finally, these approaches may be combined in various ways with hybrid methods [5].…”

Benchmarking Exploratory OLAP

“…First we evaluate the benchmark protocol and metrics based on synthetic SUTs whose behavior is well known. Second, we show that it is possible Input Category DB instance Query log Current query Output [5] Automatic exploration Tuples [1] Automatic exploration Sequence of queries [12] Automatic exploration Queries [3] Automatic exploration Queries [13] Visual optimization Queries Automatic exploration result highlighting [14] Visual optimization Query [29] Data prefetching Tuples [28,30] Data prefetching Tuples [18] Data prefetching Sequence of queries [27] Data prefetching Queries Table 1. Interactive cube exploration techniques signatures and meaningful to compare two state-of-the-art SUT from IDE literature [1,13] with our benchmark.…”