Dr. Mohamed Nadjib Mami – Smart Data Analytics

Dr. Mohamed Nadjib Mami left SDA. The profile below reflects the status at the point of his departure and is no longer updated.

Researcher
Enterprise Information Systems
Fraunhofer IAIS

Profiles: LinkedIn, Google Scholar, DBLP, Website

Room B3-236
Schloss Birlinghoven, 53757 Sankt Augustin, Germany

Short CV

Mohamed Nadjib Mami is a Researcher at the Enterprise Information Systems of the Fraunhofer IAIS.

Research Interests

Big Data
Data Management
Heterogeneous Databases
Linked Data

Projects

Publications

2021

Mami, Mohamed Nadjib

Strategies for a Semantified Uniform Access to Large and Heterogeneous Data Sources PhD Thesis

University of Bonn, Germany, 2021.

Abstract | Links | BibTeX

@phdthesis{DBLP:phd/dnb/Mami21,

title = {Strategies for a Semantified Uniform Access to Large and Heterogeneous

Data Sources},

author = {Mohamed Nadjib Mami},

url = {https://hdl.handle.net/20.500.11811/8925},

year = {2021},

date = {2021-01-01},

school = {University of Bonn, Germany},

abstract = {The remarkable advances achieved in both research and development of Data Management as well as the prevalence of high-speed Internet and technology in the last few decades have caused unprecedented data avalanche. Large volumes of data manifested in a multitude of types and formats are being generated and becoming the new norm. In this context, it is crucial to both leverage existing approaches and propose novel ones to overcome this data size and complexity, and thus facilitate data exploitation. In this thesis, we investigate two major approaches to addressing this challenge: Physical Data Integration and Logical Data Integration. The specific problem tackled is to enable querying large and heterogeneous data sources in an ad hoc manner.

In the Physical Data Integration, data is physically and wholly transformed into a canonical unique format, which can then be directly and uniformly queried. In the Logical Data Integration, data remains in its original format and form and a middleware is posed above the data allowing to map various schemata elements to a high-level unifying formal model. The latter enables the querying of the underlying original data in an ad hoc and uniform way, a framework which we call Semantic Data Lake, SDL. Both approaches have their advantages and disadvantages. For example, in the former, a significant effort and cost are devoted to pre-processing and transforming the data to the unified canonical format. In the latter, the cost is shifted to the query processing phases, e.g., query analysis, relevant source detection and results reconciliation.

In this thesis we investigate both directions and study their strengths and weaknesses. For each direction, we propose a set of approaches and demonstrate their feasibility via a proposed implementation. In both directions, we appeal to Semantic Web technologies, which provide a set of time-proven techniques and standards that are dedicated to Data Integration. In the Physical Integration, we suggest an end-to-end blueprint for the semantification of large and heterogeneous data sources, i.e., physically transforming the data to the Semantic Web data standard RDF (Resource Description Framework). A unified data representation, storage and query interface over the data are suggested. In the Logical Integration, we provide a description of the SDL architecture, which allows querying data sources right on their original form and format without requiring a prior transformation and centralization. For a number of reasons that we detail, we put more emphasis on the virtual approach. We present the effort behind an extensible implementation of the SDL, called Squerall, which leverages state-of-the-art Semantic and Big Data technologies, e.g., RML (RDF Mapping Language) mappings, FnO (Function Ontology) ontology, and Apache Spark. A series of evaluation is conducted to evaluate the implementation along with various metrics and input data scales. In particular, we describe an industrial real-world use case using our SDL implementation. In a preparation phase, we conduct a survey for the Query Translation methods in order to back some of our design choices.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

The remarkable advances achieved in both research and development of Data Management as well as the prevalence of high-speed Internet and technology in the last few decades have caused unprecedented data avalanche. Large volumes of data manifested in a multitude of types and formats are being generated and becoming the new norm. In this context, it is crucial to both leverage existing approaches and propose novel ones to overcome this data size and complexity, and thus facilitate data exploitation. In this thesis, we investigate two major approaches to addressing this challenge: Physical Data Integration and Logical Data Integration. The specific problem tackled is to enable querying large and heterogeneous data sources in an ad hoc manner.
In the Physical Data Integration, data is physically and wholly transformed into a canonical unique format, which can then be directly and uniformly queried. In the Logical Data Integration, data remains in its original format and form and a middleware is posed above the data allowing to map various schemata elements to a high-level unifying formal model. The latter enables the querying of the underlying original data in an ad hoc and uniform way, a framework which we call Semantic Data Lake, SDL. Both approaches have their advantages and disadvantages. For example, in the former, a significant effort and cost are devoted to pre-processing and transforming the data to the unified canonical format. In the latter, the cost is shifted to the query processing phases, e.g., query analysis, relevant source detection and results reconciliation.
In this thesis we investigate both directions and study their strengths and weaknesses. For each direction, we propose a set of approaches and demonstrate their feasibility via a proposed implementation. In both directions, we appeal to Semantic Web technologies, which provide a set of time-proven techniques and standards that are dedicated to Data Integration. In the Physical Integration, we suggest an end-to-end blueprint for the semantification of large and heterogeneous data sources, i.e., physically transforming the data to the Semantic Web data standard RDF (Resource Description Framework). A unified data representation, storage and query interface over the data are suggested. In the Logical Integration, we provide a description of the SDL architecture, which allows querying data sources right on their original form and format without requiring a prior transformation and centralization. For a number of reasons that we detail, we put more emphasis on the virtual approach. We present the effort behind an extensible implementation of the SDL, called Squerall, which leverages state-of-the-art Semantic and Big Data technologies, e.g., RML (RDF Mapping Language) mappings, FnO (Function Ontology) ontology, and Apache Spark. A series of evaluation is conducted to evaluate the implementation along with various metrics and input data scales. In particular, we describe an industrial real-world use case using our SDL implementation. In a preparation phase, we conduct a survey for the Query Translation methods in order to back some of our design choices.

2020

Mami, Mohamed Nadjib; Grangel-González, Irlán; Graux, Damien; Elezi, Enkeleda; Lösch, Felix

Semantic Data Integration for the SMT Manufacturing Process Using SANSA Stack Inproceedings

In: The Semantic Web: ESWC 2020 Satellite Events - ESWC 2020 Satellite Events, Heraklion, Crete, Greece, May 31 - June 4, 2020, Revised Selected Papers, pp. 307–311, Springer, 2020.

Links | BibTeX

2019

Mami, Mohamed Nadjib; Graux, Damien; Scerri, Simon; Jabeen, Hajira; Auer, Sören; Lehmann, Jens

Uniform Access to Multiform Data Lakes using Semantic Technologies Inproceedings

In: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, iiWAS 2019, Munich, Germany, December 2-4, 2019, pp. 313–322, ACM, 2019.

Links | BibTeX

Mami, Mohamed Nadjib; Graux, Damien; Scerri, Simon; Jabeen, Hajira; Auer, Sören; Lehmann, Jens

How to Feed the Squerall with RDF and Other Data Nuts? Inproceedings

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. 293–296, CEUR-WS.org, 2019.

Links | BibTeX

Mami, Mohamed Nadjib; Graux, Damien; Scerri, Simon; Jabeen, Hajira; Auer, Sören; Lehmann, Jens

Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources Inproceedings

In: The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedings, Part II, pp. 229–245, Springer, 2019.

Links | BibTeX

Mami, Mohamed Nadjib; Graux, Damien; Scerri, Simon; Jabeen, Hajira; Auer, Sören

Querying Data Lakes using Spark and Presto Inproceedings

In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, pp. 3574–3578, ACM, 2019.

Links | BibTeX

2018

Sejdiu, Gezim; Ermilov, Ivan; Lehmann, Jens; Mami, Mohamed Nadjib

DistLODStats: Distributed Computation of RDF Dataset Statistics Inproceedings

In: The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part II, pp. 206–222, Springer, 2018.

Links | BibTeX

Sejdiu, Gezim; Ermilov, Ivan; Mami, Mohamed Nadjib; Lehmann, Jens

STATisfy Me: What Are My Stats? Inproceedings

In: Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th - to - 12th, 2018, CEUR-WS.org, 2018.

Links | BibTeX

Endris, Kemele M.; Galkin, Mikhail; Lytra, Ioanna; Mami, Mohamed Nadjib; Vidal, Maria-Esther; Auer, Sören

Querying Interlinked Data by Bridging RDF Molecule Templates Journal Article

In: Trans. Large Scale Data Knowl. Centered Syst., vol. 39, pp. 1–42, 2018.

Links | BibTeX

2017

Endris, Kemele M.; Galkin, Mikhail; Lytra, Ioanna; Mami, Mohamed Nadjib; Vidal, Maria-Esther; Auer, Sören

MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates Inproceedings

In: Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part I, pp. 3–18, Springer, 2017.

Links | BibTeX

Auer, Sören; Scerri, Simon; Versteden, Aad; Pauwels, Erika; Charalambidis, Angelos; Konstantopoulos, Stasinos; Lehmann, Jens; Jabeen, Hajira; Ermilov, Ivan; Sejdiu, Gezim; Ikonomopoulos, Andreas; Andronopoulos, Spyros; Vlachogiannis, Mandy; Pappas, Charalambos; Davettas, Athanasios; Klampanos, Iraklis A.; Grigoropoulos, Efstathios; Karkaletsis, Vangelis; de Boer, Victor; Siebes, Ronald; Mami, Mohamed Nadjib; Albani, Sergio; Lazzarini, Michele; Nunes, Paulo; Angiuli, Emanuele; Pittaras, Nikiforos; Giannakopoulos, George; Argyriou, Giorgos; Stamoulis, George; Papadakis, George; Koubarakis, Manolis; Karampiperis, Pythagoras; Ngomo, Axel-Cyrille Ngonga; Vidal, Maria-Esther

The BigDataEurope Platform - Supporting the Variety Dimension of Big Data Inproceedings

In: Web Engineering - 17th International Conference, ICWE 2017, Rome, Italy, June 5-8, 2017, Proceedings, pp. 41–59, Springer, 2017.

Abstract | Links | BibTeX

@inproceedings{DBLP:conf/icwe/AuerSVPCKLJESIA17,

title = {The BigDataEurope Platform - Supporting the Variety Dimension of Big 

Data},

author = {Sören Auer and 

Simon Scerri and 

Aad Versteden and 

Erika Pauwels and 

Angelos Charalambidis and 

Stasinos Konstantopoulos and 

Jens Lehmann and 

Hajira Jabeen and 

Ivan Ermilov and 

Gezim Sejdiu and 

Andreas Ikonomopoulos and 

Spyros Andronopoulos and 

Mandy Vlachogiannis and 

Charalambos Pappas and 

Athanasios Davettas and 

Iraklis A. Klampanos and 

Efstathios Grigoropoulos and 

Vangelis Karkaletsis and 

Victor de Boer and 

Ronald Siebes and 

Mohamed Nadjib Mami and 

Sergio Albani and 

Michele Lazzarini and 

Paulo Nunes and 

Emanuele Angiuli and 

Nikiforos Pittaras and 

George Giannakopoulos and 

Giorgos Argyriou and 

George Stamoulis and 

George Papadakis and 

Manolis Koubarakis and 

Pythagoras Karampiperis and 

Axel-Cyrille Ngonga Ngomo and 

Maria-Esther Vidal},

url = {https://doi.org/10.1007/978-3-319-60131-1_3},

doi = {10.1007/978-3-319-60131-1_3},

year  = {2017},

date = {2017-01-01},

booktitle = {Web Engineering - 17th International Conference, ICWE 2017, Rome, 

Italy, June 5-8, 2017, Proceedings},

volume = {10360},

pages = {41--59},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

abstract = {The management and analysis of large-scale datasets -- described with the term Big Data -- involves the three classic dimensions volume, velocity and variety. While the former two are well supported by a plethora of software components, the variety dimension is still rather neglected. We present the BDE platform -- an easy-to-deploy, easy-to-use and adaptable (cluster-based and standalone) platform for the execution of big data components and tools like Hadoop, Spark, Flink. The BDE platform was designed based upon the requirements gathered from the seven societal challenges put forward by the European Commission in the Horizon 2020 programme and targeted by the BigDataEurope pilots. As a result, the BDE platform allows to perform a variety of Big Data flow tasks like message passing (Kafka, Flume), storage (Hive, Cassandra) or publishing (GeoTriples). In order to facilitate the processing of heterogeneous data, a particular innovation of the platform is the semantic layer, which allows to directly process RDF data and to map and transform arbitrary data into RDF.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Karim, Farah; Mami, Mohamed Nadjib; Vidal, Maria-Esther; Auer, Sören

Large-scale storage and query processing for semantic sensor data Inproceedings

In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017, Amantea, Italy, June 19-22, 2017, pp. 8:1–8:12, ACM, 2017.

Links | BibTeX

2016

Mami, Mohamed Nadjib; Scerri, Simon; Auer, Sören; Vidal, Maria-Esther

Towards Semantification of Big Data Technology Inproceedings

In: Big Data Analytics and Knowledge Discovery - 18th International Conference, DaWaK 2016, Porto, Portugal, September 6-8, 2016, Proceedings, pp. 376–390, Springer, 2016.

Links | BibTeX