{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:22Z","timestamp":1760242882779,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,10,7]],"date-time":"2016-10-07T00:00:00Z","timestamp":1475798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/informatics3040018","type":"journal-article","created":{"date-parts":[[2016,10,10]],"date-time":"2016-10-10T10:35:19Z","timestamp":1476095719000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets"],"prefix":"10.3390","volume":"3","author":[{"given":"Peng","family":"Mi","sequence":"first","affiliation":[{"name":"The Computer Science Department, Virginia Tech, Blacksburg, VA 24060, USA"}]},{"given":"Maoyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA"}]},{"given":"Moeti","family":"Masiane","sequence":"additional","affiliation":[{"name":"The Computer Science Department, Virginia Tech, Blacksburg, VA 24060, USA"}]},{"given":"Yong","family":"Cao","sequence":"additional","affiliation":[{"name":"The Boeing Company, 3455 Airframe Dr, North Charleston, SC 29418, USA"}]},{"given":"Chris","family":"North","sequence":"additional","affiliation":[{"name":"The Computer Science Department, Virginia Tech, Blacksburg, VA 24060, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/TVCG.2009.94","article-title":"Cross-Filtered views for multidimensional visual analysis","volume":"16","author":"Weaver","year":"2010","journal-title":"IEEE Trans. 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