In-situ visual exploration over big raw data

Bikakis, Nikos; Maroulis, Stavros; Papastefanatos, George; Vassiliadis, Panos

doi:10.1016/j.is.2020.101616

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…Nadal et al (2019) addressed the issue of big data evolution, however only in the context of big data ontology and not its application framework. Bikakis et al (2021) created the RawVis framework for in-situ visualization of huge raw data, made feasible by dynamically creating the main-memory index and changing the index structure based on user input. This was an attempt to address the issue of how huge data evolves over time (Velocity), however the time dimension was not made apparent.…”

Section: Discussion Conclusion and Recommendations For Future Researchmentioning

confidence: 99%

“…This problem of big data evolution was discussed in (Nadal et al, 2019), but the scope of this study is limited to the ontology of big data and does not examine the framework for its implementation. Bikakis et al (2021) created a framework called RawVis for the in-situ viewing of large amounts of raw data, which is made feasible by dynamically generating the index in the main memory, as well as changing the index structure through the use of user-driven approaches. Although this is a response of sorts to the changes that big data undergoes over time (Velocity), the time dimension is not expressed directly.…”

Section: Literature Reviewmentioning

confidence: 99%

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Reception of the New Framework for Implementing Temporal Big Data Analytics in Organizations

Mach‐Król¹

2022

Informatyka Ekonomiczna

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The main goal of this study was to present the reception of the new framework for implementing temporal big data analytics (TBDA) in organizations. This research also aimed at verifying the correctness and usefulness of the proposed framework by means of a focus group interview. The need for TBDA is described, and the proposed framework briefly outlined. Finally, the results of the focus group interview are presented. The proposed conceptual framework was positively verified. The most important findings of this study are: proving that effective implementation of big data analytics in companies requires consideration of time; demonstrating the usefulness of the leagile approach in the implementation of TBDA in companies; positive verification of the comprehensive conceptual framework for TBDA implementation in organizations.

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Section: Discussion Conclusion and Recommendations For Future Researchmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Reception of the New Framework for Implementing Temporal Big Data Analytics in Organizations

Mach‐Król¹

2022

Informatyka Ekonomiczna

View full text Add to dashboard Cite

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“…Although large citation indexes, like Scopus, Web of Science (WoS), Google Scholar, provide advanced search mechanisms, they return long list of results, data, and metrics, and poorly support researchers in their usual tasks (Bikakis et al, 2021; Sultanum et al, 2020). Starting from these exigences, research has been developed in many directions with the aim of automatically generating related work sections of writing papers (Chen & Zhuge, 2019); identifying the relationships between metadata and criteria to judge the relevance of scientific papers (Zhang et al, 2021); offering context‐aware citation recommendations (Jeong et al, 2020); creating interactive storyboard for exploring visual information in scientific publications (Zeng et al, 2020); realizing visual tools for analyzing citation networks (Van Eck & Waltman, 2014) or exploring bibliographies (Federico et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assisting researchers in bibliographic tasks: A new usable, real‐time tool for analyzing bibliographies

Dattolo

Corbatto

2021

Asso for Info Science & Tech

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The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time-consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real-time visual platform, designed using a zz-structure-based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real-life uses. The results of the evaluation are positive and describe an effective and usable platform.

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Big Data Visualization Tools

Bikakis

2012

Encyclopedia of Big Data Technologies

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In-situ visual exploration over big raw data

Cited by 10 publications

References 18 publications

Reception of the New Framework for Implementing Temporal Big Data Analytics in Organizations

Reception of the New Framework for Implementing Temporal Big Data Analytics in Organizations

Assisting researchers in bibliographic tasks: A new usable, real‐time tool for analyzing bibliographies

Big Data Visualization Tools

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