AstroShelf

Neophytou, Panayiotis; Gheorghiu, Roxana; Hachey, Rebecca; Luciani, Timothy; Bao, Di; Labrinidis, Alexandros; Marai, Elisabeta; Chrysanthis, Panos K.

doi:10.1145/2213836.2213940

Cited by 10 publications

(1 citation statement)

References 7 publications

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“…Visualoptimizationtoolssupportuser-interactionsviacontrolledandoptimizedvisualexploration, based on inherent data semantics and relevance measures. The exploratory search optimization incorporatestheuser'sinitialsearchinterestandintermediateinteractionsinretrieval.Theuser's intermediateinteractionsaremodeledusingthreerelevanceartifacts,Implicit, Explicit,andPseudorelevance.Animplicit relevanceinferredfromuserbehaviour,andexplicit relevancefromuseractions appliesexternally(e.g.feedback)duringthesearch.Inrecentyears,theroleofpseudo-relevanceis realizedforthevisualsearch,viacapturedrelevancefeedbackandestimatedviaexistinginherent retrievalapproach,suchastop-kresults (Pike,Stasko,Chang&O'connell,2009).Therelevance measuresrevealthedatasemanticshelptheuserinunderstandingtheinformation.Thevisualtools, suchasuRank,SearchBar (Morris,RingelMorris&Venolia,2008)andWebSearchViz (Nguyen & Zhang, 2006), Lyberworld (Hemmje, Kunkel & Willett, 1994) employed implicit relevance and CourseNavigator (Li, Papaemmanouil & Koutrika, 2016), AstroSelf (Neophytou, Gheorghiu & Chrysanthis, 2012), Foresight (Demiralp, Haas & Pedapati, 2017), SpaceOddussy (Pavlovic, Zacharatou, Sidlauskas, Heinis & Ailamaki, 2016), Aqua (Acharya, Gibbons, & Poosala, 1999), KnowMiner (Rauch,Klieber,Wozelka,Singh&Sabol,2015)andScatter/Gather (Cutting,Karger, Pedersen&Tukey,1992)inferredexplicit,whilePeerChooser,TalkExplorer,Insyder (Reiterer,Tullius & Mann, 2005), RankSpiral, GalaxyOfNews (Rennison, 1994), and CRUISE (Para, Taramigkou, Bothos,Apostolou&Mentzas,2014)regardtheimportanceofrelevancetoaidexploration.…”

Section: Visual Optimization Toolsmentioning

confidence: 99%

Visual Task Characterization for Exploratory Search

Singh

2020

International Journal of Information Systems and Social Change

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Humans are living in the age of big data, advanced analytics, and human information interaction. The tremendous growth of ‘the data' presents enormous challenges to information-seeking. A naïve user adapts several strategies (e.g., analytical search, search by analogy, etc.) to aggregate the knowledge. Augmented visualization plays a pivotal role via visual cognitive support in user searches (i.e., simple visual search, visual exploration, etc.). Though it is hard for a user to look for specific tasks or visual metaphors, as evaluation of visual task is rarely undertaken, it is unclear what role, if any, information visualizations play in the user search. In this paper, a visual taxonomy is adopted to systematically review key visualization tools to understand the visual tasks and inherent functions and viability. The key contribution is to trace the current trends and advancements on the dimensions (i.e., visual accomplishments and visual implications) and to assert that annotations will provide a vision on the state of exploratory visual analytics, evolving frameworks, and tools.

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Section: Visual Optimization Toolsmentioning

confidence: 99%

Visual Task Characterization for Exploratory Search

Singh

2020

International Journal of Information Systems and Social Change

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Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study

2019

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When exploring big amounts of data without a clear target, providing an interactive experience becomes really difficult, since this tentative inspection usually defeats any early decision on data structures or indexing strategies. This is also true in the physics domain, specifically in high-energy physics, where the huge volume of data generated by the detectors are normally explored via C++ code using batch processing, which introduces a considerable latency. An interactive tool, when integrated into the existing data management systems, can add a great value to the usability of these platforms. Here, we intend to review the current state-of-the-art of interactive data exploration, aiming at satisfying three requirements: access to raw data files, stored in a distributed environment, and with a reasonably low latency. This paper follows the guidelines for systematic mapping studies, which is well suited for gathering and classifying available studies. We summarize the results after classifying the 242 papers that passed our inclusion criteria. While there are many proposed solutions that tackle the problem in different manners, there is little evidence available about their implementation in practice. Almost all of the solutions found by this paper cover a subset of our requirements, with only one partially satisfying the three. The solutions for data exploration abound. It is an active research area and, considering the continuous growth of data volume and variety, is only to become harder. There is a niche for research on a solution that covers our requirements, and the required building blocks are there.INDEX TERMS Big data applications, data analysis, data engineering, data exploration, database systems, interactive systems, systematic mapping study. APPENDIX RESULTS OF THE MAPPING STUDYSee Tables.

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Understanding Data Exploration Search A brief Study of User’s Exploratory Search facets

Kumar

Singh

2018

2018 4th International Conference on Computing Communication and Automation (ICCCA)

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AstroShelf

Cited by 10 publications

References 7 publications

Visual Task Characterization for Exploratory Search

Visual Task Characterization for Exploratory Search

Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study

Understanding Data Exploration Search A brief Study of User’s Exploratory Search facets

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