Paper Accepted At ICEIS 2022

We are very pleased to announce that our group got a paper accepted for presentation at ICEIS 2022. The purpose of the International Conference on Enterprise Information Systems (ICEIS) is to bring together researchers, engineers and practitioners interested in the advances and business applications of information systems. Six simultaneous tracks will be held, covering different aspects of Enterprise Information Systems Applications, including Enterprise Database Technology, Systems Integration, Artificial Intelligence, Decision Support Systems, Information Systems Analysis and Specification, Internet Computing, Electronic Commerce, Human Factors and Enterprise Architecture.

Here is the abstract and the link to the paper:

Efficient Computation of Comprehensive Statistical Information of Large-scale OWL Dataset: A Scalable Approach
By Heba Mohamed, Said Fathalla,, Jens Lehmann, and Hajira Jabeen.

Abstract

Computing dataset statistics is crucial for exploring their structure, however, it becomes challenging for large-scale datasets. This has several key benefits, such as link target identification, vocabulary reuse, quality analysis, big data analytics, and coverage analysis. In this paper, we present the first attempt of developing a distributed approach (OWLStats) for collecting comprehensive statistics over large-scale OWL datasets. OWLStats is a distributed in-memory approach for computing 50 statistical criteria for OWL datasets utilizing Apache Spark. We have successfully integrated OWLStats into the SANSA framework. Experiments results prove that OWLStats is linearly scalable in terms of both node and data scalability.