Being Happy with the Least: Achieving α-happiness with Minimum Number of Tuples

Xie, Min; Wong, Raymond Chi-Wing; Peng, Peng; Tsotras, Vassilis J.

doi:10.1109/icde48307.2020.00092

Cited by 19 publications

(24 citation statements)

References 20 publications

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“…The problem of interactive regret minimization that aimed to enhance the regret minimization problem with user interactions was studied in [36], [37]. Xie et al [38] proposed a variation of min-size RMS called α-happiness query. Since these variations have different formulations from the original k-RMS problem, the algorithms proposed for them cannot be directly applied to the k-RMS problem.…”

Section: Related Workmentioning

confidence: 99%

A Fully Dynamic Algorithm for k-Regret Minimizing Sets

Wang

Wong

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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Section: Related Workmentioning

confidence: 99%

A Fully Dynamic Algorithm for k-Regret Minimizing Sets

Wang

Wong

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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“…Other data-oriented strategies have been proposed for cases where user preferences are unknown. Regret-minimization strategies combine features from top-K and skyline methods [10]. They return a set of K answers which maximizes the minimal satisfaction of any user with any preference function.…”

Section: Related Workmentioning

confidence: 99%

Dealing with Plethoric Answers of SPARQL Queries

Parkin

Chardin

Jean

et al. 2021

Lecture Notes in Computer Science

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When querying Knowledge Bases (KBs), users are faced with large sets of data, often without knowing their underlying structures. It follows that users may make mistakes when formulating their queries, therefore receiving an unhelpful response. In this paper, we address the plethoric answers problem, the situation where a query produces significantly more results than the user was expecting. We deal with this problem by identifying the parts of the failing query, called Minimal Failure Inducing Subqueries (MFIS), that cause plethoric answers. As long as the query contains an MFIS, it will fail to reach a sufficiently low amount of answers. Thanks to these MFIS, interactive and automatic approaches can be set up to help the user reformulate their query. The dual notion of MFIS, maXimal Succeeding Subqueries (XSS), is also useful. They are queries with the most parts of the original query that return non plethoric answers. Our goal is to compute MFIS and XSS efficiently, so that they may be used to solve the plethoric answers problem. We propose two algorithms that leverage query and data properties to compute MFIS and XSS. We show experimentally that our algorithms clearly outperform a baseline method on generated queries as well as real user-submitted queries.

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“…Suppose the user preference is modeled by an unknown utility function, which is a mapping f : [0, 1] d → R + and assigns a non-negative utility f (t) to each tuple t ∈ D. To avoid several complicated but uninteresting "boundary cases", we assume that no two tuples have the same utility in D. Following [1]- [3], [25], [26], [30], we focus on the popularin-practice linear utility functions, shown to effectively model the way users evaluate trade-offs in real-life multi-objective decision-making [21]. A utility function f is linear, if…”

Section: Problem Definitionmentioning

confidence: 99%

“…Follow this setting in [30] and assume each vector in S has the same probability of being used by a user, i.e., for any user, his/her utility vector is a random variable and obeys a uniform distribution on S. We relax the assumption later and show that the analysis can be applied when considering any other distribution. Under the assumption, for any user, Rat k (S) is the probability that S contains at least one of the top-k tuples w.r.t.…”

Section: A Algorithm Preparationmentioning

confidence: 99%

Rank-Regret Minimization

Xiao¹,

Li²

2021

Preprint

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Multi-criteria decision-making often requires finding a small representative subset from the database. A recently proposed method is the regret minimization set (RMS) query. RMS returns a fixed size subset S of dataset D that minimizes the regret ratio of S (the difference between the score of top-1 in S and the score of top-1 in D, for any possible utility function). Existing work showed that the regret-ratio is not able to accurately quantify the "regret" level of a user. Further, relative to the regret-ratio, users do understand the notion of rank. Consequently, it considered the problem of finding a minimal set S with at most k rank-regret (the minimal rank of tuples of S in the sorted list of D).Corresponding to RMS, we focus on the dual version of the above problem, defined as the rank-regret minimization (RRM) problem, which seeks to find a fixed size set S that minimizes the maximum rank-regret for all possible utility functions. Further, we generalize RRM and propose the restricted rank-regret minimization (RRRM) problem to minimize the rank-regret of S for functions in a restricted space. The solution for RRRM usually has a lower regret level and can better serve the specific preferences of some users. In 2D space, we design a dynamic programming algorithm 2DRRM to find the optimal solution for RRM. In HD space, we propose an algorithm HDRRM for RRM that bounds the output size and introduces a double approximation guarantee for rank-regret. Both 2DRRM and HDRRM can be generalized to the RRRM problem. Extensive experiments are performed on the synthetic and real datasets to verify the efficiency and effectiveness of our algorithms.

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Being Happy with the Least: Achieving α-happiness with Minimum Number of Tuples

Abstract: If it is the author's pre-published version, changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published version.

Cited by 19 publications

References 20 publications

A Fully Dynamic Algorithm for k-Regret Minimizing Sets

A Fully Dynamic Algorithm for k-Regret Minimizing Sets

Dealing with Plethoric Answers of SPARQL Queries

Rank-Regret Minimization

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