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Financial Big Data Visualization Architecture

The rising amount of big data, such as user-generated or social interaction data has led to a “Big Data Revolution”. YUKKA Lab AG develops solutions that enable the analysis of finance-related big data in real-time.  Since data and analysis results are far better understood when visualized, YUKKA Lab AG works on solutions that provide a meaningful visual representation of data and analysis results. In this context, there are different challenges involved: What subset of data is relevant to the user? How should it be chosen? What options are relevant to browse and display the subset? How can this be realized technically in a scalable and user-oriented manner? How are analysis results presented in an intuitive and insightful way? How can quick interactions with the GUI be realized?

The aim of the theses topics in this area is to help answer these questions by extending Graphr Visualizer, a visual big data analysis tool developed at CIT, and integrating it with Apache Spark. Methods that try to answer the above questions fall into the following topic areas:

  • Developing methods to meaningfully represent financial big data and analysis results. This includes e.g. selection of a meaningful subset or a visualization metaphor.
  • Developing methods to analyze financial big data visually and interactively using a web interface.
  • Developing methods that allow graphr to interact with Apache Spark, thereby enable the analysis of Internet-scale big data and provide the short response times of a modern application GUI.

Students can…

  • choose their thesis topic from one of the above feature groups,
  • decide whether they want to work more on the conceptual/scientific or implementation level (usually it will be a mixture of both). 

From YUKKA Lab AG’s side access to financial experts, real-world financial big data and the already existing analysis infrastructure is available to students. From TUB’s side for testing and evaluation proposes, our 200-node cluster (each node: Quadcore Xeon @3.3 GHz, 16 GB RAM, 3 TB RAID0) is available. Thesis language: German or English. 

Prerequisites: good programming skills in Java or JavaScript, general interest in distributed systems, data analysis and visualization. The feature groups can be adjusted to better match the individual interests and skill level. If desired, topics may be shared among multiple students. Students will be supervised by experts from YUKKA Lab AG and scientists from TUB/CIT.

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Dr. Peter Janacik
+49 (30) 314-25397
Room E-N 103