Gëzim Sejdiu – Smart Data Analytics

PhD Student
Computer Science Institute
University of Bonn

Profiles: Homepage, LinkedIn, Google Scholar, GitHub, Research Gate, Twitter

Room 1.068
Endenicher Allee 19a, 53115 Bonn
University of Bonn, Computer Science
sejdiu@cs.uni-bonn.de

Short CV

Gëzim Sejdiu is a PhD Student & Research Associate at the University of Bonn. Gëzim’s research interest are in the area of Semantic Web, Big Data and Machine Learning. He is also interested in the area of distributed computing systems (Apache Spark, Apache Flink).

Research Interests

Big Data
Data Mining and Data Analysis
Semantic Web and Semantic Search
Machine Learning
Distributed Computing

Projects

Big Data Europe – Integrating Big Data, Software & Communities for Addressing Europe’s Societal Challenges
SANSA – Open source platform for distributed data processing for RDF large-scale datasets
BigDataOcean – Exploiting Ocean’s of Data for Maritime Applications
Boost4.0 – Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0

Teaching

Courses
- Lab “Distributed Big Data Analytics” – (MA-INF 4223)
  The goal is to provide experience and technical skills related to Big data processing tools like Flink and Spark, in addition, to make them acquainted with the functional programming style prevalent in concurrent and parallel programming for Big data.
  (SoSe2019, WiSe2018/19, SoSe2018, WiSe2017/18, SoSe2017)
Supervision
- Emetis Niazmand, since 2019; Master Thesis _{(co-supervision with Prof. Dr. Jens Lehmann)}
- Gresa Halimi (University of Prishtina), since 2019; Master Thesis _{(co-supervision with Prof. Dr. Lule Ahmedi)}
- David Ibhaluobe, since 2019; Master Thesis _{(co-supervision with Dr. Damien Graux and Prof. Dr. Jens Lehmann)}
- Moumen Elteir, since 2018; Master Thesis _{(co-supervision with Prof. Dr. Jens Lehmann)}
- Pardeep Naik, 2018 – 2019; Master Thesis: “An efficient recommendation system for RDF partitioners over large-scale RDF datasets” _{(co-supervision with Dr. Ioanna Lytra and Prof. Dr. Jens Lehmann)}
- Mohammad Ghasemi, 2018 – 2019; Master Thesis: “An efficient semantic-based Entity-Resolution over Big RDF data with SANSA framework” _{(co-supervision with Prof. Dr. Jens Lehmann)}
- Abakar Bouba, 2018 – 2019; Master Thesis: “RDF Data Compression Techniques in a Highly Distributed Context” _{(co-supervision with Dr. Damien Graux and Prof. Dr. Jens Lehmann)}
- Gulnar Khalilova, 2018; Master Thesis _{(co-supervision with Dr. Anisa Rula and Prof. Dr. Jens Lehmann)}
- Wang Zhe, 2018; Master Thesis: “Efficient In-memory Graph Partitioning Algorithms and Query Engine for RDF Data” _{(co-supervision with Dr. Ioanna Lytra and Prof. Dr. Jens Lehmann)}
- Kunal Jha, 2018; Master Thesis: “Rule Mining on Distributed RDF Data” _{(co-supervision with Dr. Hajira Jabeen, Tommaso Soru, Michael Roeder and Prof. Dr. Jens Lehmann)}
- Mohamad Denno, 2017 – 2018; Master Thesis: “Scalable deep learning technique for sensitive data exposure detection” _{(co-supervision with Dr. Hajira Jabeen and Prof. Dr. Jens Lehmann)}
- Ali Denno, 2017 – 2018; Master Thesis: “Scalable Knowledge Graph Exploration for Sentiment Classification” _{(co-supervision with Dr. Hajira Jabeen and Prof. Dr. Jens Lehmann)}
- Imran Khan, 2017 – 2018; Master Thesis: “Efficient and Scalable in-memory Semantic Partitioning for RDF Data” _{(co-supervision with Dr. Ioanna Lytra and Prof. Dr. Jens Lehmann)}
- Nayef Roqaya, 2017 – 2018; Master Thesis: “Distributed Data Parsing and Vandalism Detection on Large Knowledge Graphs using Apache Spark and Hadoop Ecosystem” _{(co-supervision with Dr. Hajira Jabeen and Prof. Dr. Jens Lehmann)}
- Rohan Asmat, since 2017; Web Development.
- Julius Kaufmann, since 2017; DevOps.
- Adrian Bajraktari, June – September 2018; Web Development.

Awards and Nominations

Best demonstration award at International Semantic Web Conference 2017.
I. Ermilov, J. Lehmann, G. Sejdiu, L. Bühmann, P. Westphal, C. Stadler, S. Bin, N. Chakraborty, H. Petzka, M. Saleem, A. N. Ngonga, and H. Jabeen, “The Tale of Sansa Spark” in Proceedings of 16th International Semantic Web Conference, Poster & Demos, 2017. (Project Website, GitHub, Slides, Screencasts)

Presentations

Towards A Scalable Semantic-based Distributed Approach for SPARQL query evaluation @SEMANTiCS 2019, 9-12.09.2019 (slides)
DistLODStats: Distributed Computation of RDF Dataset Statistics @ISWC 2018, 8-12.10.2018 (slides)
The Tale of SANSA Spark :: SANSA-Notebooks: Developer friendly access to SANSA @ISWC 2017, 21-25.10.2017 (slides, demo)
Distributed Knowledge Graph Processing in SANSA @HPI Future SOC – Lab Day (Spring 2017), 25.04.2017 (video).
A demo of Apache Flink with Docker on the BDE platform @2nd BDE Technical Webinar, 20.10.2016 (slides, video)

Workshops & Tutorials

Workshops
- 1st Workshop on Large Scale RDF Analytics (LASCAR-19)
  _{Half-Day Workshop at 16th European Semantic Web Conference 2019 (ESWC2019).
  
  2nd – 6th June 2019, Portorož, Slovenia}
Tutorials
- SANSA’s Leap of Faith: Scalable RDF and Heterogeneous Data Lakes
  _{Half-Day Tutorial at 16th European Semantic Web Conference 2019 (ESWC2019).
  
  2nd – 6th June 2019, Portorož, Slovenia}

Publications

2020

Sejdiu, Gezim

Efficient Distributed In-Memory Processing of RDF Datasets PhD Thesis

Rheinische Friedrich-Wilhelms-Universität Bonn, 2020.

Abstract | Links | BibTeX

@phdthesis{gezim-sejdiu-2020-phd-thesis,
title = {Efficient Distributed In-Memory Processing of RDF Datasets},
author = {Gezim Sejdiu},
url = {http://hdl.handle.net/20.500.11811/8735},
year = {2020},
date = {2020-01-01},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
abstract = {Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. Today, we count more than 10,000 datasets made available online following Semantic Web standards. A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. Thus, we introduce a distributed approach of quality assessment of large RDF datasets. It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. As a result, it has become difficult to efficiently process these large RDF datasets. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. All the proposed approaches during this thesis are integrated into the larger SANSA framework.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. Today, we count more than 10,000 datasets made available online following Semantic Web standards. A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. Thus, we introduce a distributed approach of quality assessment of large RDF datasets. It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. As a result, it has become difficult to efficiently process these large RDF datasets. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. All the proposed approaches during this thesis are integrated into the larger SANSA framework.

Jabeen, Hajira; Haziiev, Eskender; Sejdiu, Gezim; Lehmann, Jens

DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data Inproceedings

IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 400–407, IEEE, 2020.

Short CV

Research Interests

Projects

Teaching

Awards and Nominations

Presentations

Workshops & Tutorials

Publications

2020

2019

2018

2017

2016

2014