Graph Data: Storage, Processing, and Mining
by Matteo Lissandrini, Aalborg University, Denmark
November 8th 2018 @ 11:30, in Room B104 - Povo 2
Graphs have proven to be expressive and powerful models for heterogeneous data where connections among data items are as important as the items they connect. From protein interactions to social networks, from communication systems to power grids, and from retail experiences to supply chains most big data analytics today require to store, process, and reason about information that cannot be adequately represented only in terms of attributes and values. As a result, different scientific communities and research areas have devolved their attention to graph data. In this presentation, I will cover different formalisations of the graph data model, in order to understand how graphs are applied to different scenarios. Then, I will survey a few typical algorithms to analyse graph data, these will give a general idea of the common challenges we face when dealing with this data model and the approaches to overcome them. Finally, I will present an overview of different graph storage and querying system. These are more general-purpose storage and querying system employed for the management of graph data.
Matteo Lissandrini is a researcher in the Department of Computer Science, at Aalborg University (Denmark) where he works on exploratory analytics techniques for Graph Data and the Semantic Web.
Matteo received his PhD from the University of Trento (Italy) with a thesis on exploratory search for information graphs. His works focuses on exploratory methods for data analytics, and in particular on example drive query paradigms (a.k.a., "exemplar queries").
Contact Info: Yannis Velegrakis (email@example.com)