Cube query interestingness: Novelty, relevance, peculiarity and surprise

IF 3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Dimos Gkitsakis , Spyridon Kaloudis , Eirini Mouselli , Veronika Peralta , Patrick Marcel , Panos Vassiliadis
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引用次数: 0

Abstract

In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of human behavior studies and computer science. We define the interestingness of a query as a vector of scores along different aspects, like novelty, relevance, surprise and peculiarity and complement this definition with a taxonomy of the information that can be used to assess each of these aspects of interestingness. We provide both syntactic (result-independent) and extensional (result-dependent) checks, measures and algorithms for assessing the different aspects of interestingness in a quantitative fashion. We also report our findings from a user study that we conducted, analyzing the significance of each aspect, its evolution over time and the behavior of the study’s participants.

立方体查询的趣味性:新颖性、相关性、特殊性和惊奇性
本文讨论了在数据立方体环境中评估查询趣味性的方法。我们假定有一个分层多维数据库,其中存储着数据立方体和层次结构。我们首先全面回顾了人类行为研究和计算机科学领域的相关工作。我们将查询的趣味性定义为不同方面的分数向量,如新颖性、相关性、惊奇性和特殊性,并用可用于评估趣味性各方面的信息分类法对这一定义进行补充。我们提供了句法(与结果无关)和扩展(与结果有关)检查、测量方法和算法,用于定量评估趣味性的不同方面。我们还报告了我们进行的一项用户研究的结果,分析了每个方面的重要性、其随时间的演变以及研究参与者的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Systems
Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
2.70%
发文量
112
审稿时长
53 days
期刊介绍: Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.
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