S
Seyed-Mehdi-Reza Beheshti
Researcher at University of New South Wales
Publications - Â 18
Citations - Â 769
Seyed-Mehdi-Reza Beheshti is an academic researcher from University of New South Wales. The author has contributed to research in topics: Big data & SPARQL. The author has an hindex of 13, co-authored 18 publications receiving 694 citations. Previous affiliations of Seyed-Mehdi-Reza Beheshti include Curtin University.
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Journal ArticleDOI
DREAM: distributed RDF engine with adaptive query planner and minimal communication
TL;DR: DREAM is presented, a distributed and adaptive RDF system that combines the advantages of the state-of-the-art centralized and distributed RDF systems, whereby data communication is avoided and cluster resources are aggregated.
Journal ArticleDOI
Large scale graph processing systems: survey and an experimental evaluation
Omar Batarfi,Radwa El Shawi,Ayman G. Fayoumi,Reza Nouri,Seyed-Mehdi-Reza Beheshti,Ahmed Barnawi,Sherif Sakr +6 more
TL;DR: A comprehensive survey over the state-of-the-art of large scale graph processing platforms, namely, GraphChi, Apache Giraph, GPS, GraphLab and GraphX, and an extensive experimental study of five popular systems in this domain.
Book ChapterDOI
A query language for analyzing business processes execution
TL;DR: A query language for analyzing event logs of process-related systems based on the two concepts of folders and paths, which enable an analyst to group related events in the logs or find paths among events, is proposed.
Proceedings ArticleDOI
Reputation management in crowdsourcing systems
Mohammad Allahbakhsh,Aleksandar Ignjatovic,Boualem Benatallah,Seyed-Mehdi-Reza Beheshti,Elisa Bertino,Norman Foo +5 more
TL;DR: A reputation management model is proposed which leverages the values of the tasks completed, the credibility of the evaluators of the results of the task and the time of evaluation of theresults of these tasks in order to calculate more dependable quality metrics for workers andevaluators.
Journal ArticleDOI
Scalable graph-based OLAP analytics over process execution data
TL;DR: A model for process OLAP (P-OLAP) is presented and OLAP specific abstractions in process context such as process cubes, dimensions, and cells are defined and a MapReduce-based graph processing engine is presented, to support big data analytics over process graphs.