QuRVe: Query Refinement for View Recommendation in Visual Data Exploration

Ehsan, Humaira; Sharaf, Mohamed A.; Demartini, Gianluca

doi:10.1007/978-3-030-54623-6_14

Cited by 6 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data-View Recommendation: Our work in this paper is related to the topic of automati-cally identifying and recommending interesting data views to facilitate data exploration, with the intent of maximizing insights from, and/or the value of, the underlying data to the users, see, e.g., [18][19][20][21][22][23][24][25][26]. Existing approaches are often based on data cubes [6,7] and work with a variety of granularity levels of the views to be identified.…”

Section: Related Workmentioning

confidence: 99%

“…Existing works in this area also vary in their definitions of scoring functions that de-termine the level of interestingness of candidate views. Options that have been considered include the level of deviation of a view from reference views [20], as well as the outputs of statistical analyses [23,24] or of techniques based on machine learning [25], among oth-ers [22,21].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Trend Surfing: Effective and Efficient Retrieval of Unusual Temporal Trends

Ao,

Schatz,

Chirkova

2023

Machine Learning Techniques and NLP

View full text Add to dashboard Cite

Locating unusual temporal trends in data cubes is a recurrent task in a variety of application domains. We consider a version of this problem in which one looks for the data dimensions that are best correlated with the given unusual temporal trends. Our goal is to make such data-cube navigation in search of unusual temporal trends both effective and efficient. Challenges in achieving this goal arise from the rarity of the trends to be located, as well as from the combinatorics involved in locating in data cubes nodes with unusual trends. We show that exhaustive solutions are worst-case intractable, and introduce tractable heuristic algorithms that enable effective and efficient data-cube navigation in a particular manner that we call trend surfing. We report the results of testing the proposed algorithms on three real-life data sets; these results showcase the effectiveness and efficiency of the algorithms against the exhaustive baseline.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Trend Surfing: Effective and Efficient Retrieval of Unusual Temporal Trends

Ao,

Schatz,

Chirkova

2023

Machine Learning Techniques and NLP

View full text Add to dashboard Cite

show abstract

“…After the QuRVe framework was first introduced in the second author's PhD thesis [8], our later work in [11] showed that QuRVe is able to efficiently reduce the prohibitively large search space of possible views by utilizing some of the salient characteristics of our multi-objective optimization problem described above. In this work, we expand on our basic QuRVe, and propose two new novel schemes that are able to further reduce the search space of refined queries, and in turn minimize the query execution cost incurred in the process of recommending those interesting aggregate visualizations.…”

Section: Introductionmentioning

confidence: 99%

“…This allows QuRVe to prune a large number of unnecessary views, and in turn reduces the overall processing time for recommending the top-k views. However, our original QuRVe [11] utilizes a theoretical loose bound when estimating that maximum possible utility provided by a view. That theoretical bound is oblivious to the characteristics of the analyzed data, and in turn tends to overestimate the utility provided by each view.…”

Section: Introductionmentioning

confidence: 99%

Efficient Query Refinement for View Recommendation in Visual Data Exploration

Sharaf

Ehsan

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

The need for efficient and effective data exploration has resulted in several solutions that automatically recommend interesting visualizations. The main idea underlying those solutions is to automatically generate all possible views of data, and recommend the top-k interesting views. However, those solutions assume that the analyst is able to formulate a well-defined query that selects a subset of data, which contains insights. Meanwhile, in reality, it is typically a challenging task to pose an exploratory query, which can immediately reveal some insights. To address that challenge, in this work we propose utilizing query refinement as one technique that allows to automatically adjust the analyst's input query to discover such valuable insights. However, a naive query refinement, in addition to generating a prohibitively large search space, also raises other problems such as deviating from the user's preference and recommending statistically insignificant views. In this paper, we address those problems and propose a novel suit of schemes, which efficiently navigate the refined queries search space to recommend the top-k insights that meet all of the analyst's pre-specified criteria.

show abstract

Quality Matters: Understanding the Impact of Incomplete Data on Visualization Recommendation

Mafrur

Sharaf

Zuccon

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

In insight recommendation systems, obtaining timely and high-quality recommended visual analytics over incomplete data is challenging due to the difficulties in cleaning and processing such data. Failing to address data incompleteness results in diminished recommendation quality, compelling users to impute the incomplete data to a cleaned version through a costly imputation strategy. This paper introduces VizPut scheme, an insight-aware selective imputation technique capable of determining which missing values should be imputed in incomplete data to optimize the effectiveness of recommended visualizations within a specified imputation budget. The VizPut scheme determines the optimal allocation of imputation operations with the objective of achieving maximal effectiveness in recommended visual analytics. We evaluate this approach using real-world datasets, and our experimental results demonstrate that VizPut effectively maximizes the efficacy of recommended visualizations within the user-defined imputation budget.

show abstract

QuRVe: Query Refinement for View Recommendation in Visual Data Exploration

Cited by 6 publications

References 13 publications

Trend Surfing: Effective and Efficient Retrieval of Unusual Temporal Trends

Trend Surfing: Effective and Efficient Retrieval of Unusual Temporal Trends

Efficient Query Refinement for View Recommendation in Visual Data Exploration

Quality Matters: Understanding the Impact of Incomplete Data on Visualization Recommendation

Contact Info

Product

Resources

About