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-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:20180325T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20181028T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20190331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20191027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20201025T010000
END:STANDARD
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:20260411T160840
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
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20220328T140000
DTEND;TZID=Europe/Berlin:20220328T153000
DTSTAMP:20260411T160840
CREATED:20220328T003937Z
LAST-MODIFIED:20220328T003937Z
UID:9478-1648476000-1648481400@vis.h-da.de
SUMMARY:Thesis Presentation: Visual Analytics on Enterprise Reports for Investment and Strategical Analysis
DESCRIPTION:Where: TU Darmstadt / GRIS\, Zoom: https://tu-darmstadt.zoom.us/j/83620126004?pwd=a1hyUkprRWpMVXd3eEpNRTBVYk9tUT09\nWho: Sibgha Nazir (Author)\, Prof. Dr. Arjan Kuijper (Supervisor)\, Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)\nWhat: Master Thesis – “Visual Analytics on Enterprise Reports for Investment and Strategical Analysis” \nAbstract: \nGiven the availability of enormous data in today’s time\, suitable analysis techniques and graphical tools are required to derive knowledge in order to make this data useful. Scientists and developers have come up with visual analytical systems that combine machine learning technologies\, such as text mining with interactive data visualization\, to provide fresh insights into the present and future trends. Data visualization has progressed to become a cutting-edge method for displaying and interacting with graphics on a single screen. Using visualizations\, decision-makers may unearth insights in minutes\, and teams can spot trends and significant outliers in minutes [1]. A vast variety of automatic data analysis methods have been developed during the previous few decades. For investors\, researchers\, analysts\, and decision-makers\, these developments are significant in terms of innovation\, technology management\, and strategic decision-making. \nThe financial business is only one of many that will be influenced by the habits of the next generation\, and it must be on the lookout for new ideas. Using cutting-edge financial analytics tools will\, of course\, have a significant commercial impact. Visual analytics\, when added to the capabilities\, can deliver relevant and helpful insights. By collecting financial internal information from different organizations\, putting them in one place\, and incorporating visual analytics tools\, financial analytics software will address crucial business challenges with unprecedented speed\, precision\, and ease. \nThe goal of the thesis is to make use of visual analytics for the fundamental analysis of a business to support investors and business decision-makers. The idea is to collect the financial reports\, extract the data and feed them to this visual analytics system. Financial reports are PDF documents published by public companies annually and quarterly which are readily available on companies’ websites containing the values of all financial indicators which fully and vividly paint the picture of a companies’ business. The financial indicators in those reports make the basis of fundamental analysis. The thesis focuses on those manually collected reports from the companies’ websites and conceptualizes and implements a pipeline that gathers text and facts from the reports\, processes them\, and feeds them to a visual analytics dashboard. Furthermore\, the thesis uses state-of-the-art visualization tools and techniques to implement a visual analytics dashboard as the proof of concept and extends the visualization interface with interaction capability by giving them options to choose the parameter of their choice allowing the analyst to filter and view the available data. The dashboard fully integrates with the data transformation pipeline to consume the data that has been collected\, structured\, and processed and aims to display the financial indicators as well as allow the user to display them graphically. It also implements a user interface for manual data correction ensuring continuous data cleansing. \nThe presented application makes use of state-of-the-art financial analytics and information visualization techniques to enable visual trend analysis. The application is a great tool for investors and business analysts for gaining insights into a business and analyzing historical trends of its earnings and expenses and several other use-cases where financial reports of the business are a primary source of valuable information.
URL:https://vis.h-da.de/events/thesis-presentation-visual-analytics-on-enterprise-reports-for-investment-and-strategical-analysis
LOCATION:TU Darmstadt / GRIS\, Fraunhoferstraße 5\, Darmstadt\, Hessian\, 64283\, Germany
CATEGORIES:Scitics,Thesis
ATTACH;FMTTYPE=image/jpeg:https://vis.h-da.de/wp-content/uploads/2019/08/Teaching.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200317T160000
DTEND;TZID=Europe/Berlin:20200317T163000
DTSTAMP:20260411T160840
CREATED:20200313T131036Z
LAST-MODIFIED:20211215T033248Z
UID:6490-1584460800-1584462600@vis.h-da.de
SUMMARY:Thesis Presentation: Named-Entity Recognition on Publications and Raw-Text for Meticulous Insight at Visual Trend Analytics
DESCRIPTION:Where: TU Darmstadt / GRIS\, Fraunhoferstr. 5 (Darmstadt)\, Room tba \n!!!!! Due to the Corona crisis and the accompanying restrictions at the TU Darmstadt\, the exam will be non-public! !!!!! \nWho: Ubaid Rana (Author)\, Prof. Dr. Arjan Kuijper (Supervisor)\, Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)\nWhat: Master Thesis – “Named-Entity Recognition on Publications and Raw-Text for Meticulous Insight at Visual Trend Analytics” \nAbstract: \nIn the modern data-driven era\, a massive amount of research documents are available from publicly accessible digital libraries in the form of academic papers\, journals and publications. This plethora of data does not lead to new insights or knowledge. Therefore\, suitable analysis techniques and graphical tools are needed to derive knowledge in order to get insight of this big data. To address this issue\, researchers have developed visual analytical systems along with machine learning methods\, e.g text mining with interactive data visualization\, which leads to gain new insights of current and upcoming technology trends. These trends are significant for researchers\, business analysts\, and decision-makers for innovation\, technology management and to make strategic decisions.\nNearly every existing search portal uses the traditional meta-information e.g only about the author and title to find the documents that match a search request and overlook the opportunity of extracting content-related information. It limits the possibility of discovering most relevant publications\, moreover it lacks the knowledge required for trend analysis. To collect this very concrete information\, named entity recognition must be used to be able to better identify the results and trends. The state-of-the-art systems use static approach for named entity recognition which means that upcoming technologies remain undetected. Modern techniques like distant supervision methods leverage big existing community-maintained data sources\, such as Wikipedia\, to extract entities dynamically. Nonetheless\, these methods are still unstable and have never been tried on complex scenarios such as trend analysis before.\nThe aim of this thesis is to enable entity recognition on both static tables and dynamic community updated data sources like Wikipedia & DBpedia for trend analysis. To accomplish this goal\, a model is suggested which enabled entity extraction on DBpedia and translated the extracted entities into interactive visualizations. The analysts can use these visualizations to gain trend insights\, evaluate research trends or to analyze prevailing market moods and industry trends.
URL:https://vis.h-da.de/events/thesis-presentation-named-entity-recognition-on-publications-and-raw-text-for-meticulous-insight-at-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
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190826T153000
DTEND;TZID=Europe/Berlin:20190826T160000
DTSTAMP:20260411T160840
CREATED:20190816T013124Z
LAST-MODIFIED:20211215T033312Z
UID:5463-1566833400-1566835200@vis.h-da.de
SUMMARY:Thesis Presentation: Contrasted Data from Science and Web for Advanced Visual Trend Analytics
DESCRIPTION:Where: TU Darmstadt / GRIS\, Fraunhoferstr. 5\, Room 073\nWho: Rehman Ahmed Abdul (Author)\, Prof. Dr. Arjan Kuijper (Supervisor)\, Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)\nWhat: Master Thesis – “Contrasted Data from Science and Web for Advanced Visual Trend Analytics” \nAbstract: \nWith more publicly accessible digital libraries accessible\, a plethora of digital research data is now available for gaining insights into actual and upcoming technology trends. These trends are essential to researchers\, business analysts\, and decision-makers for making strategic decisions and setting strategic goals. Appropriate processing and graphical analysis methods are required in order to extract meaningful information from the data. In particular\, the combination of data mining approaches together with visual analytics leads to real beneficial applications to support decision making in e.g. innovation or technology management.\nThe data from digital libraries is only limited to research and overlooks the market aspects e.g if the trend is not important for key business players\, it is irrelevant for the market. This importance of market aspects creates a demand for validation approaches based on market data. Most of the current market data can be found publically on websites and social networks\, e.g. as news from enterprises or on tech review sites or on tech blogs. Therefore\, it makes sense to consider this public and social media data as contrasting data to the research digital library data that can be used to validate technology trends.\nThe goal of this thesis is to enable trend analysis on public and social web data and compare it with retrieved trends based on research library data to enable validation of trends. To achieve this goal a model is proposed that acquires public/social web and digital library data based on user-defined scope called a “campaign”\, which is then visually transformed from raw data into interactive visualizations passing through different stages of data management\, enrichment\, transformation\, and visual mapping. These interactive visualizations can either be used in insight analysis to gain trend insights for an individual data source or they can be used in comparative analysis with the goal of validating trends from two contrasting data sources.
URL:https://vis.h-da.de/events/thesis-presentation-contrasted-data-from-science-and-web-for-advanced-visual-trend-analytics
LOCATION:TU Darmstadt / GRIS\, Fraunhoferstraße 5\, Darmstadt\, Hessian\, 64283\, Germany
CATEGORIES:Scitics,Thesis
ATTACH;FMTTYPE=image/jpeg:https://vis.h-da.de/wp-content/uploads/2019/08/Teaching.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190826T150000
DTEND;TZID=Europe/Berlin:20190826T153000
DTSTAMP:20260411T160840
CREATED:20190816T011146Z
LAST-MODIFIED:20211215T033334Z
UID:5454-1566831600-1566833400@vis.h-da.de
SUMMARY:Thesis Presentation: Visual Trend Analysis on Condensed Expert Data beside Research Library Data for Enhanced Insights
DESCRIPTION:Where: TU Darmstadt / GRIS\, Fraunhoferstr. 5\, Room 073\nWho: Muhammad Ali Riaz (Author)\, Prof. Dr. Arjan Kuijper (Supervisor)\, Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)\nWhat: Master Thesis – “Visual Trend Analysis on Condensed Expert Data beside Research Library Data for Enhanced Insights” \nAbstract: \nIn the present age of information\, we live amidst seas of digital text documents including academic publications\, white papers\, news articles\, patents\, newspapers. To tackle the issue of the ever-increasing amount of text documents\, researchers from the field of text mining and information visualization have developed tools and techniques to facilitate text analysis. In the context of visual trend analysis on text data\, the use of well-structured patent data and public digital libraries are quite established. However\, both sources of information have their limitations. For instance\, the registration process for patents takes at least one year\, which makes the extracted insights not suitable to research on present scenarios. In contrast to patent data\, the digital libraries are up-to-date but provide high-level insights\, only limited to broader research domains\, and the data usage is almost restricted on meta information\, such as title\, author names and abstract\, and they do not provide full text.\nFor a certain type of detailed analysis such as competitor analysis or portfolio analysis\, data from digital libraries is not enough\, it would also make sense to analyze the full-text. Even more\, it can be beneficial to analyze only a limited dataset that is filtered by an expert towards a very specific field\, such as additive printing or smart wearables for medical observations. Sometimes also a mixture of both digital library data and manually collected documents is relevant to be able to validate a certain trend\, where one gives a big picture and other gives a very condensed overview of the present scenario.\nThe thesis aims\, therefore\, to focus on such manually collected documents by experts that can be defined as condensed data. So\, the major goal of this thesis is to conceptualize and implement a solution that enables the creation and analysis of such a condensed data set and compensate therewith the limitations of digital library data analysis. As a result\, a visual trend analysis system for analyzing text documents is presented\, it utilizes the best of both state-of-the-art text analytics and information visualization techniques. In a nutshell\, the presented trend analysis system does two things. Firstly\, it is capable of extracting raw data from text documents in the form of unstructured text and meta-data\, convert it into structured and analyzable formats\, extract trends from it and present it with appropriate visualizations. Secondly\, the system is also capable of performing gap-analysis tasks between two data sources\, which in this case is digital library data and data from manually collected text documents (Condensed Expert Data). The proposed visual trend analysis system can be used by researchers for analyzing the research trends\, organizations to identify current market buzz and industry trends\, and many other use-cases where text data is the primary source of valuable information.
URL:https://vis.h-da.de/events/thesis-presentation-visual-trend-analysis-on-condensed-expert-data-beside-research-library-data-for-enhanced-insights
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