{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Visual Analytics for Relation Discovery in Multivariate Data","metadata":[{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/78501"},{"label":"dcterms.abstract","value":"The growth of digital data is tremendous. These data come from many aspects\nof life and matter such as medicine, science, environment monitoring, business,\nfinance, social networks, etc. When the data is multivariate, or the dimensionality\nof the data becomes high, it can be a challenge for analysts to understand\nthe intricate relations among the data. The data types not only consist of static\ndata, but also dynamic data, geospatial data, network data etc. The various types\nmake it even more difficult for the analysis. Visual analytics can offer powerful\nmechanisms to assist humans in the exploration of these complex data, by mining\nthe relations from the raw data and sculpting them as visualizations to help\nhumans gain insight. In the thesis, we focus on relation discovery in multivariate\nstatic, dynamic, geospatial, and network data via several new visual analytics approaches.\nFirst, we analyze the relations among the static multivariate data and\npropose the data context map which can illustrate the relations among data items\nand attributes. Then we extend the mapping to the dynamic case, aiming to capture\nand visualize the attribute relation behaviors in dynamic flows with our tool\nStreamVisND. Next, we move to the geospatial data to recover the relations in the\ngeospatial data. To achieve this, we developed the ColorMapND framework to visualize\nand colorize multi-field, multi-channel, multi-spectral data on the geospatial\nor image domain. Finally, we consider the complex topology that shapes the multivariate data, such as network data and visualize the relations in this kind of\ncomplex network topology. We first study the relations of common networks by\nmodified spectral embedding and then extend our work to multi-dimensional torus\nnetworks with the proposed framework TorusTra f f icND."},{"label":"dcterms.available","value":"2018-11-12T17:50:34Z"},{"label":"dcterms.contributor","value":"Committee members: Gu, Xianfeng; Kaufman, Arie; Yan, Hanfei"},{"label":"dcterms.creator","value":"Cheng, Shenghui"},{"label":"dcterms.date","value":"2018"},{"label":"dcterms.dateAccepted","value":"2018-11-12T17:50:34Z"},{"label":"dcterms.dateSubmitted","value":"2018-11-12T17:50:34Z"},{"label":"dcterms.description","value":"Department of Computer Science."},{"label":"dcterms.extent","value":"234 pages"},{"label":"dcterms.format","value":"application/pdf"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/78501"},{"label":"dcterms.issued","value":"2018-01-01"},{"label":"dcterms.language","value":"en"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2018-11-12T17:50:34Z (GMT). No. of bitstreams: 1\nCheng_grad.sunysb_0771E_13734.pdf: 40767911 bytes, checksum: 8011a98d63c498e55f569ebf4529fd69 (MD5)\n  Previous issue date: 2018-01-05"},{"label":"dcterms.publisher","value":"Stony Brook University"},{"label":"dcterms.title","value":"Visual Analytics for Relation Discovery in Multivariate Data"},{"label":"dcterms.type","value":"Text"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/88%2F70%2F84%2F88708459581965342544328857844314169729/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/88%2F70%2F84%2F88708459581965342544328857844314169729","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}