4th Graph-TA program

Modelling the Clustering Coefficient of a Random Graph

Speaker: Ariel Duarte (DAMA-UPC & MACDA)

Abstract extract: Acquiring graph-like datasets with realistic degree distributions or other structural properties such as clustering coefficient is not always feasible, either due to privacy preserving concerns or technical issues. However, for many research or benchmarking applications, having graph datasets with such characteristics is of high importance, as they highly aect the performance of applications or the outcome of the research. In this work in progress, we present a graph generation algorithm that is able to generate graphs following a given degree distribution and a target clustering coefficient.

Computing on Event-sourced Graphs

Speaker: Benjamin Erb (Institute of Distributed Systems - University of Ulm)

Abstract extract: While traditional graph computing usually employs batch processing, near-realtime computations on streaming data are often accomplished using event processing technologies. However, an increasing number of applications requires both computing capabilities for scenarios with highly dynamic and highly interconnected data fed by a stream of events. We suggest a novel platform architecture for graph computing that enables event-driven graph dynamicity while also supporting complex, long-running graph computations within the same system.

Use of graphs for cloud service selection in Multi-Cloud Environments

Speaker: Jaume Ferrarons (CA Technologies)

Abstract extract: The need for providing online resilient services is driving companies to adopt multi-cloud architectures in order to sustain their applications online. Multi-cloud architectures are characterized by the use of different cloud services from several cloud providers at the same time. The selection process to choose the right cloud services for each application turns out to be a complex problem. The outcome from this selection process is conditioned by several dimensions as the costs, flexibility, adaptability and quality of the final setup. We explore the use of weighted multigraphs to depict the information involved in this kind of decisions and then discuss the applicability of graph algorithms on top of it in order to find the best deployment solution.

Benchmarking Versioning systems for Big Linked Data

Speaker: Irini Fundulaki (FORTH)

Abstract extract: The evolution of datasets and the management of their links would often require storing different versions of the same dataset, so that interlinked datasets can refer to older versions of an evolving dataset and upgrade at their own pace, if at all. Supporting the functionality of accessing and querying past and multiple versions of an evolving dataset is the main challenge for versioning systems. In this presentation we are going to provide some ideas on how in the HOBBIT H2020 project we intend to develop benchmarks for versioning systems that will challenge the capability of versioning systems to address multiple versions of Linked Data and complex cross snapshot queries that span across multiple versions.

Identifiability in Dynamic Causal Networks

Speaker: Gilles Blondel (LARCA & MACDA)

Abstract extract: Causal networks are a variant of the well-known Bayesian networks that intend to model causality instead of simple association between a fixed, finite set of variables. Pearl (1995) introduced the do-calculus, a set of rules for reasoning about Causal Networks, and the ID algorithm which uses do-calculus rules for deducing whether the effect of an experiment can be calculated from observational (instead of interventional) data only. On the other hand, Dynamic Bayesian Networks model the case in which a set of variables evolve. In this work we put together the Dynamic and Causal aspects for the first time: We present Dynamic Causal Networks (DCN), we show how to apply do-calculus in this setting, and we present a dynamic variant of Pearl's ID algorithm for DCN.

Improving Mobility in Smart Cities with Intelligent Tourist Trip Planning

Speaker: Petar Mrazovic (KTH)

Abstract extract: Selecting the most interesting attractions and planning optimal sightseeing tours can be a difficult task for individuals visiting unfamiliar tourist destinations. On the other hand, the massive amounts of tourists in big cities can collapse certain areas causing transport inefficiency, unbalanced economic growth and nuisance among tourists and citizens. Therefore, the tourist trip planning problem can also arise as a means for the city government to manage the urban environment and achieve a balanced and sustainable growth. In this work we introduce the tourist trip planning problem which serves both individual (tourist) and global (city) needs. The planning problem is modelled as an extension of the mixed orienteering problem and can be controlled by deployment of mobility policies which put restrictions on points of interest and routes between them.