TY - GEN
T1 - A Distributed and Parallel Processing Framework for Knowledge Graph OLAP
AU - Ahmad, Bashar
PY - 2023/5
Y1 - 2023/5
N2 - Business intelligence and analytics refers to the ensemble of tools and techniques that allow organizations to obtain insights from big data for better decision making. Knowledge graphs are increasingly being established as a central data hub and prime source for BI and analytics. In the context of BI and analytics, KGs may be used for various analytical tasks; the integration of data and metadata in a KG potentially facilitates interpretation of analysis results. Knowledge Graph OLAP (KG-OLAP) adapts the concept of online analytical processing (OLAP) from multidimensional data analysis for the processing of KGs for analytical purposes. The current KG-OLAP implementation is a monolithic system, which greatly inhibits scalability. We propose a research plan for the development of a framework for distributed and parallel data processing for KG-OLAP over big data. In particular, we propose a framework for KG-OLAP over big data based on the data lakehouse architecture, which leverages existing frameworks for parallel and distributed data processing. We are currently at an early stage of our research.
AB - Business intelligence and analytics refers to the ensemble of tools and techniques that allow organizations to obtain insights from big data for better decision making. Knowledge graphs are increasingly being established as a central data hub and prime source for BI and analytics. In the context of BI and analytics, KGs may be used for various analytical tasks; the integration of data and metadata in a KG potentially facilitates interpretation of analysis results. Knowledge Graph OLAP (KG-OLAP) adapts the concept of online analytical processing (OLAP) from multidimensional data analysis for the processing of KGs for analytical purposes. The current KG-OLAP implementation is a monolithic system, which greatly inhibits scalability. We propose a research plan for the development of a framework for distributed and parallel data processing for KG-OLAP over big data. In particular, we propose a framework for KG-OLAP over big data based on the data lakehouse architecture, which leverages existing frameworks for parallel and distributed data processing. We are currently at an early stage of our research.
UR - http://www.dke.jku.at/research/publications/index.xq
UR - https://www.scopus.com/pages/publications/85175955435
U2 - 10.1007/978-3-031-43458-7_47
DO - 10.1007/978-3-031-43458-7_47
M3 - Conference proceedings
SN - 9783031434570
VL - 13998
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 288
EP - 297
BT - Proceedings of the 20th European Semantic Web Conference (ESWC 2023), Heronissos, Greece, May 28 to June 1, 2023, PhD Symposium
A2 - Pesquita, Catia
A2 - Skaf-Molli, Hala
A2 - Efthymiou, Vasilis
A2 - Kirrane, Sabrina
A2 - Ngonga, Axel
A2 - Collarana, Diego
A2 - Cerqueira, Renato
A2 - Alam, Mehwish
A2 - Trojahn, Cassia
A2 - Hertling, Sven
PB - Springer Verlag
ER -