Systems and Applications for Graph Data, with Khaled Ammar of Thomson Reuters Innovation Labs
September 25, 2018 at 1:30 PM to 3:30 PM
|Location:||5345 Herzberg Laboratories|
|Key Contact:||Kathryn Elliott|
|Contact Phone:||613-520-2600 x3244|
In this talk…
Large volumes of data generated from human interaction - with software systems that support daily applications in areas such as commerce, law, and entertainment - have given rise to what is commonly referred to as the “Big Data Problem”. Graph data is of growing importance in this context. Applications that rely on graph data include the semantic web (i.e., RDF), bioinformatics, finance and trade, and social networks among others. Graphs naturally model complicated structures, such as protein interaction networks, product purchasing, business transactions, relationships and interactions in social or computer networks, and web page connections.
The size and complexity of these graphs raise significant data management and data analysis challenges. In my talk, I will start by an overview of existing parallel systems for graph analysis, then show how dynamic graphs could be supported, and conclude with relevant use cases for graph data at Thomson Reuters.
About the Speaker
Khaled Ammar, Data Scientist at Thomson Reuters Innovation Labs
Khaled creates innovative applications for legal, governance, regulation and tax use cases. Before joining Thomson Reuters Innovation Labs, he was a research engineer at the Cognitive Computing Center, the R&D technical team for Thomson Reuters. Khaled’s research interests are motivated by real-life data processing and understanding, specifically data analytics over large and dynamic graphs. His research focuses on three aspects of this problem. The first focus is on identifying potential graph structures in business data, extracting these graphs, and proposing innovative solutions for different use cases. The second is studying dynamic graphs, which are graphs that change over time. He is developing data structures, algorithms, and systems for efficient analytics over multiple releases of a graph. The third aspect is scalability to very large graphs, where he is investigating parallel and distributed systems.
Khaled is also working on his PhD at the David R. Cheriton School of Computer Science at the University of Waterloo. His thesis studies distributed systems for graph data processing with emphasis on large dynamic graphs. He has published multiple papers in VLDB, SIGMOD, and BigData, spoken at industrial conferences such as Strata, and won academic awards such as OGS and IBM CAS fellowship.
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