Overview of Data Exploration Techniques

Idreos, Stratos; Papaemmanouil, Olga; Chaudhuri, Surajit

doi:10.1145/2723372.2731084

Cited by 201 publications

(110 citation statements)

References 56 publications

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“…Since the 1970s, "querying" became known as a paradigm where "the user knows what she is looking for, and asks for it." Recently, however, the need for a new search paradigm for the exploration of large datasets has been recognized and faced from different perspectives [4]. Differently from the past, nowadays more dynamic data-driven applications are built that do not always have the same requirements as traditional database systems.…”

Section: State Of the Artmentioning

confidence: 99%

“…The clear advantage is that the properties of the dataset are "shown," thus giving support to an immediate overall understanding of a dataset; however, the approach mainly focuses on visualization without giving much space to features such as supporting exploration as a process. Data Exploration [4], on the other hand, is about efficiently extracting knowledge from data, and its techniques range from data storage to user interaction. Mainly three lines of research are involved: (i) visualization tools for data exploration, (ii) novel optimizations for interactive exploration times and (iii) re-examination of the database architecture to match the features of the new exploration workloads.…”

Section: State Of the Artmentioning

confidence: 99%

“…4 Such largescale collections of data (e.g., thousands of TBs) make the use of traditional analysis tools and techniques infeasible or not adequate, e.g., due to constraints on the response time, or to memory/technological limitations. The fundamental requirements listed in the previous sections drive the main challenges-obviously strictly related to one another-for database researchers: Let us reason on these three aspects on the example of the medical data about thyroid diseases.…”

Section: A Database Perspectivementioning

confidence: 99%

“…The aim of EC-that, from the database researcher's viewpoint, becomes a genre of database exploration [4]-is instead to assist the so-called data enthusiasts [5]: inexperienced, non-computer-savvy users who need the support of a sophisticated system that guides them in the inspection. The result is a reconnaissance path where, at each step, the system presents the users with relevant and concise facts about the data in order for them to understand better and progress to the next exploration step.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploratory computing: a comprehensive approach to data sensemaking

Blas

Mazuran

Puppa

et al. 2016

Int J Data Sci Anal

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The Big Data challenge has made the issue of "making sense" of data urgent and unavoidable. This paper introduces exploratory computing (EC), a novel paradigm whose aim is to support a comprehensive "exploratory" experience for the user. "Exploratory" because it supports search and discovery of information through various tasks (investigation, knowledge seeking, serendipitous discovery, comparison of information…) in a dynamic interaction, where meaningful feedbacks from the system play a crucial role, closely resembling a human-to-human dialogue. "Computing" because a complex interaction as the one outlined above requires powerful computational strength for the user to be able to fully profit from, and even enjoy, the interaction. EC is not associated with a predefined set of techniques: Rather, it is an approach that can be concretized in different ways. In the paper, two different implementations of the EC approach are presented, both of which interpret the EC highlevel requirements. It is the authors' hope that others will follow.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: A Database Perspectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploratory computing: a comprehensive approach to data sensemaking

Blas

Mazuran

Puppa

et al. 2016

Int J Data Sci Anal

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show abstract

“…In this setting, analysts have no idea how to formulate their queries. Thus, interactive exploration of large time series and multidimensional datasets is a key ingredient for knowledge discovery [9].…”

Section: Exploration Of Multidimensional Datasetsmentioning

confidence: 99%

AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets

Sun

Masiane

et al. 2016

Informatics

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This paper presents the Animated VISualization Tool (AVIST), an exploration-oriented data visualization tool that enables rapidly exploring and filtering large time series multidimensional datasets. AVIST highlights interactive data exploration by revealing fine data details. This is achieved through the use of animation and cross-filtering interactions. To support interactive exploration of big data, AVIST features a GPU (Graphics Processing Unit)-centric design. Two key aspects are emphasized on the GPU-centric design: (1) both data management and computation are implemented on the GPU to leverage its parallel computing capability and fast memory bandwidth; (2) a GPU-based directed acyclic graph is proposed to characterize data transformations triggered by users' demands. Moreover, we implement AVIST based on the Model-View-Controller (MVC) architecture. In the implementation, we consider two aspects: (1) user interaction is highlighted to slice big data into small data; and (2) data transformation is based on parallel computing. Two case studies demonstrate how AVIST can help analysts identify abnormal behaviors and infer new hypotheses by exploring big datasets. Finally, we summarize lessons learned about GPU-based solutions in interactive information visualization with big data.

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Using data science for sustainable development in higher education

Filho

Eustachio

Danila

et al. 2023

Sustainable Development

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Despite the abundance of studies focused on how higher education institutions (HEIs) are implementing sustainable development (SD) in their educational programmes, there is a paucity of interdisciplinary studies exploring the role of technology, such as data science, in an SD context. Further research is thus needed to identify how SD is being deployed in higher education (HE), generating positive externalities for society and the environment. This study aims to address this research gap by exploring various ways in which data science may support university efforts towards SD. The methodology relied on a bibliometric analysis to understand and visualise the connections between data science and SD in HE, as well as reporting on selected case studies showing how data science may be deployed for creating SD impact in HE and in the community. The results from the bibliometric analysis unveil five research strands driving this field, and the case studies exemplify them. This study can be considered innovative since it follows previous research on artificial intelligence and SD. Moreover, the combination of bibliometric analysis and case studies provides an overview of trends, which may be useful to researchers and decision‐makers who wish to explore the use of data science for SD in HEIs. Finally, the findings highlight how data science can be used in HEIs, combined with a framework developed to support further research into SD in HE.

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Overview of Data Exploration Techniques

Cited by 201 publications

References 56 publications

Exploratory computing: a comprehensive approach to data sensemaking

Exploratory computing: a comprehensive approach to data sensemaking

AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets

Using data science for sustainable development in higher education

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