BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Human-Computer Interaction & Visual Analytics Reasearch Department (vis) at Darmstadt University of Applied Sciences (h_da) - ECPv6.15.14//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Human-Computer Interaction & Visual Analytics Reasearch Department (vis) at Darmstadt University of Applied Sciences (h_da)
X-ORIGINAL-URL:https://vis.h-da.de
X-WR-CALDESC:Events for Human-Computer Interaction & Visual Analytics Reasearch Department (vis) at Darmstadt University of Applied Sciences (h_da)
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20211031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20221030T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20231029T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20220426T140000
DTEND;TZID=Europe/Berlin:20220426T153000
DTSTAMP:20260513T004907
CREATED:20220419T064154Z
LAST-MODIFIED:20220328T004506Z
UID:8784-1650981600-1650987000@vis.h-da.de
SUMMARY:Thesis Presentation: Process Mining for Workflow-Driven Assistance in Visual Trend Analytics
DESCRIPTION:Where: TU Darmstadt / GRIS\, Zoom: https://tu-darmstadt.zoom.us/j/86845586436?pwd=RUJiWm1QdWJ4VDg3MU93WUNOWWFTQT09\nWho: Shahrukh Badar (Author)\, Prof. Dr. Arjan Kuijper (Supervisor)\, Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)\nWhat: Master Thesis – “Process Mining for Workflow-Driven Assistance in Visual Trend Analytics” \nAbstract: \nIn today’s data-driven world\, a large amount of data is being generated daily. This data is generated by different sources\, such as social networking platforms\, industrial machinery\, daily transactions\, etc. The companies or businesses are not only generating data but also utilizing them to improve their processes\, business decisions\, etc. There are several applications and tools that help users to analyze this big data in-depth\, by providing numerous ways to explore it\, including different types of visualization\, pivoting\, filtering\, grouping data\, etc. The challenge with such applications is that it creates long and heavy learning curves for users\, who need to work with such applications. Many systems are often designed for a specific purpose\, and therewith to know how a single system works is not enough. To enable a better work entrance with such an analytical system\, a kind of adaptive assistance would be helpful. So\, the system would hint the users regarding his previous work and interaction\, what next action might be useful. The thesis aims to face this challenge with process-driven assistance that is applied to the visual trend analytics domain. The goal is\, based on previous users interactions and solved tasks\, to assist further users in their work. Therefore\, a universal visual assistance model is defined and acts also as the main contribution\, based on defined interaction event taxonomy. This concept is applied on the Visual Trend Analytics domain on the SciTic reference system\, This “SciTic – Visual Trend Analytics” is connected with different data sources and provides analysis of scientific documents. The interaction model provides assistance in terms of recommendations\, where the user has an option either to apply a recommendation or ignore it. The solution provided in this thesis is model-based and utilizes the potential of Process Mining and Discovery techniques. It is started by creating an event taxonomy by identifying all possible ways of user interactions on the “SciTic – Visual Trend Analytics” web application. Next\, enable the “SciTic – Visual Trend Analytics” web application to start logging events chronologically based on predefined taxonomy. Later\, these events log is converted into Process Mining log format. Next\, it applies the Process Discovery algorithm “Heuristics Miner” on these log data to generate a process model\, which shows the overall flow of user interaction along with the frequencies. Later\, this process model is used to provide users with recommendations.
URL:https://vis.h-da.de/events/thesis-presentation-process-mining-for-workflow-driven-assistance-in-visual-trend-analytics
LOCATION:TU Darmstadt / GRIS\, Fraunhoferstraße 5\, Darmstadt\, Hessian\, 64283\, Germany
CATEGORIES:Scitics,Thesis
ATTACH;FMTTYPE=image/png:https://vis.h-da.de/wp-content/uploads/2018/12/symbolic_teaching.png
END:VEVENT
END:VCALENDAR