TU Darmstadt / GRIS, Fraunhoferstr. 5, Room 073 Who:
Rehman Ahmed Abdul (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt
Master Thesis – “Contrasted Data from Science and Web for Advanced Visual Trend Analytics” Abstract:
With 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.
The 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.
The 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.