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 RelWeb (A Reliable Web of Data).
His PhD is about “Exploiting Schemas for Efficient Query Processing over Knowledge Graphs”, supervised by Prof. Katja Hose and Assist Prof. Matteo Lissandrini.
He is exploring various approaches to optimize query processing on Web, specifically in Knowledge Graphs, by exploiting various schematic constraints such as SHACL/ShEx etc. Moreover, he is also involved in educational activities like supervision of 5th/6th semester's Software Engineering groups in their projects and teaching assistant for the Database Course.
He is also serving as a VLDB external reviewer and continuously reviewing VLDB scientific papers.

Publications

SHACL and ShEx in the Wild: A Community Survey on Validating Shapes Generation and Adoption
Kashif Rabbani, Matteo Lissandrini, and Katja Hose
In Companion Proceedings of the Web Conference 2022 (WWW'22 Companion), April 25-29 2022. DOI: 10.1145/3487553.3524253
Abstract Cite Website PDF Publisher Version

Knowledge Graphs (KGs) are widely used to represent heterogeneous domain knowledge on the Web and within organizations. Various methods exist to manage KGs and ensure the quality of their data. Among these, the Shapes Constraint Language (SHACL) and the Shapes Expression Language (ShEx) are the two state-of-the-art languages to define validating shapes for KGs. Since the usage of these constraint languages has recently increased, new needs arose. One such need is to enable the efficient generation of these shapes. Yet, since these languages are relatively new, we witness a lack of understanding of how they are effectively employed for existing KGs. Therefore, in this work, we answer How validating shapes are being generated and adopted? Our contribution is threefold. First, we conducted a community survey to analyze the needs of users (both from industry and academia) generating validating shapes. Then, we cross-referenced our results with an extensive survey of the existing tools and their features. Finally, we investigated how existing automatic shape extraction approaches work in practice on real, large KGs. Our analysis shows the need for developing semi-automatic methods that can help users generate shapes from large KGs.

Rabbani, Kashif; Lissandrini, Matteo; and Hose, Katja. SHACL and ShEx in the Wild: A Community Survey on Validating Shapes Generation and Adoption. In Companion Proceedings of the Web Conference 2022 (WWW'22 Companion), April 25-29 2022. Lyon France.
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 and Fall 2021, BSc Software Engineering Course, Computer Science Department, Aalborg University.
Group Supervisor for 5th semster students working on Knox (Knowledge Engineering Toolbox) project
Fall 2020 and Fall 2021, BSc Software Engineering - Semester Project, Computer Science Department, Aalborg University.
Group Supervisor for final semester students working on analyzing large scale ships AIS data
Spring 2021, BSc Software Engineering - Bachelors Project, Computer Science Department, Aalborg University.

Achivements and Awards

Big Data Talent Awards 2019 (Runner-up)
- I was selected as a runner-up for Big Data Talent Awards 2019 at UPC Barcelona, Spain for my master thesis titled as "Dataspaces: Pay-As-you-go Data Integration".
Gold Medal - Bachelors in Computer Science (BSCS)
- I won a campus Gold Medal award at COMSATS University Islamabad, Pakistan for the highest CGPA: 3.84/4.00 (Batch Spring 2013-17) of my Bachelor's degree in Computer Science (BSCS).
Silver Medal - Bachelors in Computer Science (BSCS)
- I won an institute Silver Medal award at COMSATS University Islamabad, Pakistan for the highest CGPA: 3.84/4.00 among all the institutes of COMSATS University for my Bachelor's degree in Computer Science (BSCS).
Open House Final Year Project Award (2nd Position)
- I got 2nd position for my Final Year Project during my Bachelor's degree in Computer Science (BSCS) at COMSATS University, Islamabad, Pakistan.

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 2022