The graph-based storage and processing of large amounts of data is becoming increasingly important. In our work we encounter large networks of interactions between genes, proteins and processes in the life sciences, chemical compounds and their reactions in chemistry or information graphs in the business domain. A particularly prominent example Facebook offers its users access to information of the social network through a graph search.

In a current project at the University of Leipzig, a novel graph-processing platfom (GRADOOP) is developed, which simplifies the entire process of creating a graph, its processing and analysis with the help of standardized operators and workflows. These workflows are then efficiently executed and distributed by using Apache Flink.

In this Master thesis we would like to investigate how we could integrate gradoop operators into the workflowbased data analytics tool KNIME. KNIME provides a visual programming paradigm to develop analytics workflows that are executed on Big Data Infrastructures such as Hive or Spark.

• The work includes the following subtasks:
• Overview to related work of analytics workflows for graphs
• Concept and architecture of an integration of gradoop into KNIME
• Protoypical implementation of selected operators from Gradoop into KNIME and execution on Apache Flink
• Evaluation of the developed solution on a real world analytics use case

Contact: 

• Dr. Eric Peukert(peukert@informatik.uni-leipzig.de)
• Lars-Peter Meyer (lars-peter.meyer@uni-leipzig.de)