MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration

Ehsan, Humaira; Sharaf, Mohamed A.; Chrysanthis, Panos K.

doi:10.1109/icde.2016.7498285

Cited by 46 publications

(48 citation statements)

References 19 publications

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“…The main idea underlying our first scheme Multi-Objective View Recommendation for Data Exploration (MuVE) is to use an incremental evaluation of the multi-objective utility function, where different objectives are computed progressively. Our results in [1] show that MuVE is able to prune a large number of unnecessary views, and in turn reduces the overall processing time for recommending the top-k views. However, that achieved pruning power is highly dependent on the order in which those views are presented to MuVE and might often limit its performance gains.…”

Section: Introductionmentioning

confidence: 86%

“…We propose the Multi-Objective View Recommendation for Data Exploration schemes [1,2]. The main idea underlying our first scheme Multi-Objective View Recommendation for Data Exploration (MuVE)) is to use an incremental evaluation of the multi-objective utility function, where different objectives are computed progressively.…”

Section: Multi-objective View Recommendation For Data Exploration (Mumentioning

confidence: 99%

“…Moreover, the proposed solutions [1][2][3]6] are shown to be effective in recommending interesting views under the assumption that the analyst is precise in their selection of analyzed data. That is, the analyst is able to formulate a well-defined input query that selects a subset of data, which contains interesting insights that can be revealed by the recommended visualizations.…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

“…To find top-k visualizations, she might use one of the existing approaches (e.g., [1,6]), in which the target and comparison views are generated and their deviation is computed by using a distance function. Fig.…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

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View recommendation for visual data exploration

Ehsan¹

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The widespread use of digital systems has resulted in an exponential increase in the volume of available data, with data being collected across all domains; from banking and finance to health and energy.However, this data is only beneficial if it is unlocked to generate insights. Visual data exploration plays an important role in this process of discovering insights. Typically, it involves an analyst going through the following steps: 1) selecting a subset of data for analysis, 2) generating different visualizations of Publications during candidature • Humaira Ehsan, Mohamed A. Sharaf, and Panos K. Chrysanthis: Muve: Efficient multi-objective view recommendation for visual data exploration.

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Section: Introductionmentioning

confidence: 86%

Section: Multi-objective View Recommendation For Data Exploration (Mumentioning

confidence: 99%

Section: Introduction 11 Overviewmentioning

confidence: 99%

Section: Introduction 11 Overviewmentioning

confidence: 99%

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View recommendation for visual data exploration

Ehsan¹

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“…Visualization systems that focus on displaying as many results as possible to provide users feedback as they refine their queries are also proposed [46,53]. Besides displaying query results, some visualization systems are designed to recommend interesting data visualizations to users automatically [34,69] Another approach to address the too-many-results problem is to present only representative query results. That is, extracting a few tuples from a query result to provide quick insights in the potentially huge answer space.…”

Section: Data Explorationmentioning

confidence: 99%

Scalable diversification for data exploration platforms

Khan¹

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Today, data exploration platforms are widely used to assist users in locating interesting objects within large volumes of scientific and business data. In those platforms, users try to make sense of the underlying data space by iteratively posing numerous queries over large databases.Hence, data exploration platforms rely on methods for the extraction of representative data to provide users with a concise and meaningful representation of query results. That is, extracting a few tuples from a query result to provide quick insights in the potentially huge answer space.In the past few years, importance of diversification while extracting representative subsets of data has been greatly emphasized. It has been shown that diverse subsets provide more effective representation of the underlying data by minimizing redundancy and increasing coverage.Meanwhile, search results diversification adds additional cost to an already computationally expensive exploration process.In this PhD thesis, we have focused on the design, implementation and evaluation of scalable diversification algorithms and schemes for the data exploration platforms. Particularly, this research stipulates that extracting diverse representative results during data exploration requires addressing several challenges including: 1) scaling to big volumes of high dimensional data, 2) large number of users, and 3) enabling real time continuous exploration. To address those challenges we focus on two broad aspects: 1) Diversification of high dimensional large data sets and 2) Diversification of multiple user queries.The existing work conducts diversification in two steps: first compute all relevant query results, and then diversify the query results to select a small diverse subset. Similarly, all the dimensional attributes of all the data points in a query result are considered for diversification. Such a generic approach would be a performance bottleneck in high-dimensional large databases. To efficiently compute diverse subsets of query results exhibiting both high dimensionality and high-cardinality, we have proposed the Progressive Diversification Scheme. Our proposed scheme, utilizes the partial distance computations to reduce the amount of CPU and I/O incurred during query diversification. Moreover, to avoid the overhead of computing all relevant results first, we propose embedding diversification in query evaluation step by utilizing column-based data storage systems. In addition to computational cost of diversification methods, we have also considered the complexity of diversity objective function in high dimensional databases. Often, computing diverse solutions along all the dimensions is not a realistic approach. In fact, users may have some pre-specified preferences over some dimensions of the data, while expecting good coverage over the other dimensions. Motivated by that need, we propose a novel scheme, which aims to generate representative data that balance the tradeoff iii between regret minimization and diversity maximization. Our scheme...

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Materialized View Selection for Aggregate View Recommendation

Ehsan

Sharaf

2019

Lecture Notes in Computer Science

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MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration

Cited by 46 publications

References 19 publications

View recommendation for visual data exploration

View recommendation for visual data exploration

Scalable diversification for data exploration platforms

Materialized View Selection for Aggregate View Recommendation

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