Advances in Applied Data Science: Bridging Theory and Practice

A special issue of Analytics (ISSN 2813-2203).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 912

Special Issue Editor


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Guest Editor
Smith College, Northampton, MA, USA
Interests: visual analytics; visualization; human-computer interaction; machine learning; data science
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Special Issue Information

Dear Colleagues,

The rapid evolution of the modern AI/ML landscape has led to remarkable opportunities for advancement in the delivery of personalized, proactive information tailored to individual or organizational needs. However, moving recent innovations from theory into practice involves further development across areas such as modeling real-time objectives and interests, managing information scale, efficiently prioritizing relevant data, and designing effective user interfaces for evaluating tailored information in context. The second annual Summer Conference on Applied Data Science (SCADS), hosted by the Laboratory for Analytic Sciences (LAS) at North Carolina State University (NCSU) in 2023, was organized around this theme. Expanding on the insights generated at this event, this Special Issue addresses the challenge of managing the vast amount of information available to knowledge workers across several layers of the information ecosystem, and the potential for recent innovations in machine learning, multimodal analytics (combining text, audio, imagery, sensor data, etc.), recommendation systems, data triage, LLMs, and human–machine collaborative systems to advance the state of the art in this emerging area. To that end, this Special Issue is an open call for advances in theoretical models, novel methodologies, and case studies of practical applications of personalized analytics, with the ultimate goal of encouraging breakthrough innovation in tailored reporting for knowledge workers across all data-driven domains.

Dr. R. Jordan Crouser
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Analytics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • personalized analytics
  • recommendation systems
  • machine learning
  • artificial intelligence
  • multimodal data
  • human–machine teaming

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Published Papers (1 paper)

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Research

19 pages, 4076 KiB  
Article
The Analyst’s Hierarchy of Needs: Grounded Design Principles for Tailored Intelligence Analysis Tools
by Antonio E. Girona, James C. Peters, Wenyuan Wang and R. Jordan Crouser
Analytics 2024, 3(4), 406-424; https://doi.org/10.3390/analytics3040022 - 29 Oct 2024
Viewed by 399
Abstract
Intelligence analysis involves gathering, analyzing, and interpreting vast amounts of information from diverse sources to generate accurate and timely insights. Tailored tools hold great promise in providing individualized support, enhancing efficiency, and facilitating the identification of crucial intelligence gaps and trends where traditional [...] Read more.
Intelligence analysis involves gathering, analyzing, and interpreting vast amounts of information from diverse sources to generate accurate and timely insights. Tailored tools hold great promise in providing individualized support, enhancing efficiency, and facilitating the identification of crucial intelligence gaps and trends where traditional tools fail. The effectiveness of tailored tools depends on an analyst’s unique needs and motivations, as well as the broader context in which they operate. This paper describes a series of focus discovery exercises that revealed a distinct hierarchy of needs for intelligence analysts. This reflection on the balance between competing needs is of particular value in the context of intelligence analysis, where the compartmentalization required for security can make it difficult to group design patterns in stakeholder values. We hope that this study will enable the development of more effective tools, supporting the well-being and performance of intelligence analysts as well as the organizations they serve. Full article
(This article belongs to the Special Issue Advances in Applied Data Science: Bridging Theory and Practice)
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