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Publications
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Signal/Collect Processing Large Graphs in Seconds
Semantic Web Journal
Both researchers and industry are confronted with the need to process increasingly large amounts of data, much of which has a natural graph representation. Some use MapReduce for scalable processing, but this abstraction is not designed for graphs and has shortcomings when it comes to both iterative and asynchronous processing, which are particularly important for graph algorithms. This paper presents the Signal/Collect programming model for scalable synchronous and asynchronous graph…
Both researchers and industry are confronted with the need to process increasingly large amounts of data, much of which has a natural graph representation. Some use MapReduce for scalable processing, but this abstraction is not designed for graphs and has shortcomings when it comes to both iterative and asynchronous processing, which are particularly important for graph algorithms. This paper presents the Signal/Collect programming model for scalable synchronous and asynchronous graph processing. We show that this abstraction can capture the essence of many algorithms on graphs in a concise and elegant way by giving Signal/Collect adaptations of algorithms that solve tasks as varied as clustering, inferencing, ranking, classification, constraint optimisation, and even query processing. Furthermore, we built and evaluated a parallel and distributed framework that executes algorithms in our programming model. We empirically show that our framework efficiently and scalably parallelises and distributes algorithms that are expressed in the programming model. We also show that asynchronicity can speed up execution times. Our framework can compute a PageRank on a large (>1.4 billion vertices, >6.6 billion edges) real-world graph in 112 seconds on eight machines, which is competitive with other graph processing approaches.
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Scalable forensic transaction matching and its application for detecting patterns of fraudulent financial transactions
See publicationThe detection of fraudulent patterns in large sets of financial transaction data is a crucial
task in forensic investigations of money laundering, employee fraud and various other
illegal activities. Scalable and flexible tools are needed to be able to analyze these large
amounts of data and express the complex structures of the patterns that should be detected.
This thesis presents a novel approach of locally identifying associations between incoming
and outgoing transactions…The detection of fraudulent patterns in large sets of financial transaction data is a crucial
task in forensic investigations of money laundering, employee fraud and various other
illegal activities. Scalable and flexible tools are needed to be able to analyze these large
amounts of data and express the complex structures of the patterns that should be detected.
This thesis presents a novel approach of locally identifying associations between incoming
and outgoing transactions for each participant of the transaction network and then
aggregating these associations to larger patterns. The identified patters can be pruned
and visualized in a graphical user interface to conduct further investigations.
The evaluation of our approach shows that it allows a stream-processing of real-world financial transactions with a throughput of more than one million transactions per minute.
Furthermore we demonstrate the capability of our approach to express six sophisticated
money laundering patterns, as reported by the Egmont group, and successfully retrieve
components that correspond to these patterns.
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