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SUMMARY;CHARSET=UTF-8:Summer Academy Session: Human Aware Data Analysis –
  Visual & Guided Data Analytics
URL:https://www.sfg.at/e/summer-academy-session-human-aware-data-analysis-v
 isual-guided-data-analytics/
DESCRIPTION;CHARSET=UTF-8:Human Aware Data Analysis – Visual &amp\; Guide
 d Data Analytics\nThe recent advances in technology resulted in enormous i
 ncrease of personal and industrial data. Although this data obtains valuab
 le information that can be used to better support humans and machines\, it
  is a tedious and time-consuming task to extract and use this information 
 to gain valuable insights and draw correct conclusions. Hence\, we need to
  understand the importance and the correct use of Human Aware Data Analysi
 s \n\nHuman Aware Data Analysis – Visual &amp\; Guided Data Analytics\n\
 n26.08.2020 15:00 &#8211\; 17:00 | Language: English\n\n\nTarget group:\nR
 esearchers\, engineers and everyone else facing the task of exploring\, na
 vigating\, and analyzing data using interactive tools and systems. While s
 ome of the presented techniques are generally applicable\, others may targ
 et a particular application domain\, such as industry\, media or health.\n
 Abstract:\nThe recent advances in technology resulted in enormous increase
  of personal and industrial data. Although this data obtains valuable info
 rmation that can be used to better support humans and machines\, it is a t
 edious and time-consuming task to extract and use this information to gain
  valuable insights and draw correct conclusions. Although\, there exist se
 veral methods that can help to effectively mine the data (e.g.\, AI method
 s) they often require users to possess certain skills. For example\, AI me
 thods requires users to select the suitable features for data profiling\, 
 define the correct parameter space for the learning process etc. Unfortuna
 tely\, average users do not bring adequate experience and have serious dif
 ficulties when using such complex methods. More importantly\, users rarely
  know which method to use to e.g.\, analyze outliers\, trends\, distributi
 on and the root-cause of certain events. To support users when analyzing t
 heir data\, we propose methods which (i) recommend a sequence of interacti
 ons (previously provided by the experts) that shows which (AI) methods are
  best suited for the current analytical process and (ii) displays the fina
 l outcome of the analysis visually using an appropriate chart selected by 
 the system with regard to visual encoding rules and perceptual guidelines.
  Yet\, our assistance does not end here. Out methods further provide means
  to help users locate the visual patterns (i.e.\, outliers\, trends\, corr
 elations etc.) and navigate them to these interesting areas on the visuali
 zations.\nAfter the event you will know:\nAbout advanced methods for inter
 active data analysis using visual tools and systems\, e.g.\,\n\n\n\nPerson
 alised visualization and user interfaces\n\n\n\n\n\n\nGuided data analytic
 s\n\n\n\n\n\n\nAI to support users in analytical processes\n\n\n\n\n\n\nDo
 main-specific visual analytics techniques (e.g. for health or media)\n\n\n
 \n\n\n\nBig data visualization (with focus on time-series data)\n\n\n\n\n\
 n\nGathering user feedback to improve AI algorithms\n\n\n\nYou will also l
 earn about\n\n\n\nPrevious success stories on interactive data exploration
  and visual analytics\n\n\n\n\n\n\nOngoing projects and future research pl
 ans\n\n\n\n\nSpeaker\n\n\n\n\n\nVedran Sabol\nResearch Area Manager Knowle
 dge Visualization\n\n\n\n\n\n\n\nBelgin Mutlu\n\n\n\n\n\n\n\nTobias Schrec
 k\nProfessor\, TU Graz\n\n\n\n\n\n\n\nChristian Partl\nSenior Researcher K
 nowledge Visualization\n\n\n\n\n
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