Kashif Rabbani

PhD Fellow
Aalborg University, Denmark

Kashif Rabbani is a PhD Research fellow in the Department of Computer Science at Aalborg University, Denmark. He received a Joint Masters Degree in Big Data Management and Analytics (BDMA) from ULB, Belgium; UPC, Spain; and TU-Berlin, Germany in 2019. He has also served as a Data Engineer in everis Barcelona in Semantic Business Unit (SEMBU). His research focuses on graph databases, knowledge graphs, query optimization, data modelling, databases, big data management and analytics.

Highlights

Kashif is currently working on a project called A Reliable Web of Data.
His PhD is about “Efficient Query Processing on Web”, supervised by Prof. Katja Hose and Dr. Matteo Lissandrini.
He is exploring various approaches to optimize query processing on Web, specifically in Knowledge Graphs.

Publications

Optimizing SPARQL Queries using Shape Statistics
Kashif Rabbani, Matteo Lissandrini, and Katja Hose
In Proceedings of the 24th International Conference on Extending Database Technology, EDBT 2021.
Abstract Cite Website PDF Publisher Version

With the growing popularity of storing data in native RDF, we witness more and more diverse use cases with complex SPARQL queries. As a consequence, query optimization - and in particular cardinality estimation and join ordering - becomes even more crucial. Classical methods exploit global statistics covering the entire RDF graph as a whole, which naturally fails to correctly capture correlations that are very common in RDF datasets, which then leads to erroneous cardinality estimations and suboptimal query execution plans. The alternative of trying to capture correlations in a fine-granular manner, on the other hand, results in very costly preprocessing steps to create these statistics. Hence, in this paper we propose shapes statistics, which extend the recent SHACL standard with statistic information to capture the correlation between classes and properties. Our extensive experiments on synthetic and real data show that shapes statistics can be generated and managed with only little overhead without disadvantages in query runtime while leading to noticeable improvements in cardinality estimation.

Rabbani, Kashif; Lissandrini, Matteo; and Hose, Katja. Optimizing SPARQL Queries using Shape Statistics. Proceedings of the 24th International Conference on Extending Database Technology, EDBT 2021
ODIN: A dataspace management system
Nadal Francesch, Sergi, Kashif Rabbani, Óscar Romero Moral, and Shumet Tadesse Nigatu
In Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas): co-located with 18th International Semantic Web Conference, ISWC 2019.
Abstract Cite Website PDF Publisher Version

ODIN (On-demand Data Integration) is a system that supports the incremental pay-as-you-go integration of data sources into dataspaces and provides user-friendly querying mechanisms of the resulting dataspaces. This website is a companion of a demonstration paper submitted to ISWC 2019, where we describe some of its characteristics and underlying assumptions, including the user interactions required. ODIN's novelty lies in a largely automated bottom-up approach (i.e., driven by the sources at hand) that includes the user in the loop for disambiguation purposes. ODIN relies on the concept of traceability graph, which are generic metadata abstraction (i.e., not tailored for an specific task) about the integration of a particular set of data sources. From this graphs, ODIN is capable of generating target-oriented metadata constructs. In this demonstration we focus on those for query answering over dataspaces.

Nadal Francesch, Sergi, Kashif Rabbani, Óscar Romero Moral, and Shumet Tadesse Nigatu. "ODIN: A dataspace management system." In Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas): co-located with 18th International Semantic Web Conference (ISWC 2019): Auckland, New Zealand, October 26-30, 2019, pp. 185-188. CEUR-WS. org, 2019
ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources.
Shumet Tadesse, Cristina Gómez, Oscar Romero, Katja Hose, Kashif Rabbani
In IEEE 23rd International Enterprise Distributed Object Computing Conference, EDOC 2019.
Abstract Cite PDF Publisher Version

The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.

Tadesse, Shumet, Cristina Gómez, Oscar Romero, Katja Hose, and Kashif Rabbani. "ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources." In 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), pp. 190-196. IEEE, 2019

Educational activities

Kashif's educational activities focus on supervising and assisting multiple groups of computer science and software engineering students at bachelors level in Aalborg University.

Teaching Assistant for Database Management System Course
Fall 2020, BSc Software Engineering Course, Computer Science Department, Aalborg University.
Group Supervisor for 5th semster students working on Knox (Knowledge Engineering Toolbox) project
Fall 2020, BSc Software Engineering - Semester Project, Computer Science Department, Aalborg University.
Group Supervisor for final semester students working on analyzing large scale ships AIS data
Fall 2020, BSc Software Engineering - Bachelors Project, Computer Science Department, Aalborg University.

Blog Posts

Distributed GraphLab
- Read it on medium.com, Published in 2020.
Data Streams Mining
- Read it on medium.com, Published in 2020.
Metadata (Data about data)
- Read it on medium.com, Published in 2020.
Dark Silicon (Toward Dark Silicon in Servers)
- Read it on medium.com, Published in 2020.

Side Projects

IslamOne IOS Application
- He had been developing and maintaining an IOS application http://islamoneapp.github.io/

Updated: March 2021