hajiraSenior Researcher

Computer Science Institute
University of Bonn

Profiles: LinkedIn, Google Scholar, DBLP

Room A108
Römerstr. 164, 53117 Bonn, Germany
University of Bonn, Computer Science
jabeen@iai.uni-bonn.de

 

Short CV


Dr. Hajira Jabeen is a Senior Researcher at the University of Bonn. She received her PhD degree in computer Science from National University of Computing and Emerging Sciences, Islamabad, Pakistan.
Her research interests includes Big Data Artificial Intelligence, Evolutionary Computation, Semantic Web, Data Mining and Machine Learning.

Research Interests


  • Big Data
  • Data Mining and Data Analysis
  • Semantic Web
  • Machine Learning

Publications


2017

  • I. Ermilov, A. N. Ngomo, A. Versteden, H. Jabeen, G. Sejdiu, G. Argyriou, L. Selmi, J. Jakobitsch, and J. Lehmann, “Managing Lifecycle of Big Data Applications,” in KESW, 2017.
    [BibTeX] [Download PDF]
    @InProceedings{KESW_2017_BDE,
    Title = {Managing Lifecycle of Big Data Applications},
    Author = {Ermilov, Ivan and Ngomo, Axel-Cyrille Ngonga and Versteden, Aad and Jabeen, Hajira and Sejdiu, Gezim and Argyriou, Giorgos and Selmi, Luigi and Jakobitsch, J{\"u}rgen and Lehmann, Jens},
    Booktitle = {KESW},
    Year = {2017},
    Added-at = {2017-08-31T16:24:46.000+0200},
    Biburl = {https://www.bibsonomy.org/bibtex/2f5ee59fb595ade7ece4c840ad4a95741/aksw},
    Interhash = {8ac92f717e75f88d59f2811ecf7b816e},
    Intrahash = {f5ee59fb595ade7ece4c840ad4a95741},
    Keywords = {bde group_aksw iermilov lehmann ngonga simba},
    Timestamp = {2017-08-31T16:24:46.000+0200},
    Url = {https://svn.aksw.org/papers/2017/KESW_BDE_Workflow/public.pdf}
    }

  • J. Lehmann, G. Sejdiu, L. Bühmann, P. Westphal, C. Stadler, I. Ermilov, S. Bin, N. Chakraborty, M. Saleem, A. N. Ngonga, and H. Jabeen, “Distributed Semantic Analytics using the SANSA Stack,” in Proceedings of 16th International Semantic Web Conference – Resources Track (ISWC’2017), 2017.
    [BibTeX] [Abstract] [Download PDF]
    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. A major research challenge today is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and question answering. Most analytics approaches, which scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input rather than more expressive knowledge structures. On the other hand, analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases. This software framework paper describes the ongoing project Semantic Analytics Stack (SANSA) which supports expressive and scalable semantic analytics by providing functionality for distributed in-memory computing for RDF data. The library provides APIs for RDF storage, querying using SPARQL and forward chaining inference. It includes several machine learning algorithms for RDF knowledge graphs. The article describes the vision, architecture and use cases of SANSA.

    @InProceedings{lehmann-2017-sansa-iswc,
    Title = {Distributed {S}emantic {A}nalytics using the {SANSA} {S}tack},
    Author = {Lehmann, Jens and Sejdiu, Gezim and B\"uhmann, Lorenz and Westphal, Patrick and Stadler, Claus and Ermilov, Ivan and Bin, Simon and Chakraborty, Nilesh and Saleem, Muhammad and Ngonga, Axel-Cyrille Ngomo and Jabeen, Hajira},
    Booktitle = {Proceedings of 16th International Semantic Web Conference - Resources Track (ISWC'2017)},
    Year = {2017},
    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. A major research challenge today is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and question answering. Most analytics approaches, which scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input rather than more expressive knowledge structures. On the other hand, analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases. This software framework paper describes the ongoing project Semantic Analytics Stack (SANSA) which supports expressive and scalable semantic analytics by providing functionality for distributed in-memory computing for RDF data. The library provides APIs for RDF storage, querying using SPARQL and forward chaining inference. It includes several machine learning algorithms for RDF knowledge graphs. The article describes the vision, architecture and use cases of SANSA.},
    Added-at = {2017-07-17T14:46:26.000+0200},
    Biburl = {https://www.bibsonomy.org/bibtex/21ae18ac13750f9cf74227fe0a7c50104/aksw},
    Interhash = {eb99dff0ce6a9cdbce2c4cbea115fbee},
    Intrahash = {1ae18ac13750f9cf74227fe0a7c50104},
    Keywords = {2017 bde buehmann chakraborty group_aksw iermilov lehmann ngonga saleem sbin sejdiu stadler westphal},
    Owner = {iermilov},
    Timestamp = {2017-07-17T14:46:26.000+0200},
    Url = {http://svn.aksw.org/papers/2017/ISWC_SANSA_SoftwareFramework/public.pdf}
    }

  • 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.
    [BibTeX] [Download PDF]
    @InProceedings{iermilov-2017-sansa-iswc-demo,
    Title = {The {T}ale of {S}ansa {S}park},
    Author = {Ermilov, Ivan and Lehmann, Jens and Sejdiu, Gezim and B\"uhmann, Lorenz and Westphal, Patrick and Stadler, Claus and Bin, Simon and Chakraborty, Nilesh and Petzka, Henning and Saleem, Muhammad and Ngonga, Axel-Cyrille Ngomo and Jabeen, Hajira},
    Booktitle = {Proceedings of 16th International Semantic Web Conference, Poster \& Demos},
    Year = {2017},
    Added-at = {2017-08-31T16:24:45.000+0200},
    Biburl = {https://www.bibsonomy.org/bibtex/2f9b5a69afa4755944984ae63f59ad146/aksw},
    Interhash = {ebabfe08f697304b399c9b6b89f2829e},
    Intrahash = {f9b5a69afa4755944984ae63f59ad146},
    Keywords = {2017 bde buehmann chakraborty group_aksw iermilov lehmann mole ngonga saleem sbin sejdiu stadler westphal},
    Owner = {iermilov},
    Timestamp = {2017-08-31T16:24:45.000+0200},
    Url = {http://jens-lehmann.org/files/2017/iswc_pd_sansa.pdf}
    }

  • S. Auer, S. Scerri, A. Versteden, E. Pauwels, A. Charalambidis, S. Konstantopoulos, J. Lehmann, H. Jabeen, I. Ermilov, G. Sejdiu, A. Ikonomopoulos, S. Andronopoulos, M. Vlachogiannis, C. Pappas, A. Davettas, I. A. Klampanos, E. Grigoropoulos, V. Karkaletsis, V. de Boer, R. Siebes, M. N. Mami, S. Albani, M. Lazzarini, P. Nunes, E. Angiuli, N. Pittaras, G. Giannakopoulos, G. Argyriou, G. Stamoulis, G. Papadakis, M. Koubarakis, P. Karampiperis, A. N. Ngomo, and M. Vidal, “The BigDataEurope Platform – Supporting the Variety Dimension of Big Data,” in 17th International Conference on Web Engineering (ICWE2017), 2017.
    [BibTeX] [Abstract] [Download PDF]
    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.

    @InProceedings{Auer+ICWE-2017,
    Title = {{T}he {B}ig{D}ata{E}urope {P}latform - {S}upporting the {V}ariety {D}imension of {B}ig {D}ata},
    Author = {S\"oren 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},
    Booktitle = {17th International Conference on Web Engineering (ICWE2017)},
    Year = {2017},
    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.},
    Bdsk-url-1 = {http://svn.aksw.org/lod2/Paper/ISWC2012-InUse_LOD2-Stack/public.pdf},
    Date-modified = {2012-12-02 12:25:29 +0000},
    Keywords = {group_aksw sys:relevantFor:infai sys:relevantFor:bis 2017 auer iermilov ngonga lehmann bde MOLE},
    Url = {http://jens-lehmann.org/files/2017/icwe_bde.pdf}
    }

  • Q. Abbas, J. Ahmad, and H. Jabeen, “Random Controlled Pool base Differential Evolution Algorithm (RCPDE),” Intelligent Automation & Soft Computing, pp. 1-14, 2017.
    [BibTeX]
    @Article{abbas2017random,
    Title = {Random Controlled Pool base Differential Evolution Algorithm (RCPDE)},
    Author = {Abbas, Qamar and Ahmad, Jamil and Jabeen, Hajira},
    Journal = {Intelligent Automation \& Soft Computing},
    Year = {2017},
    Pages = {1--14},
    Publisher = {Taylor \& Francis}
    }

  • H. Jabeen, P. Archer, S. Scerri, A. Versteden, I. Ermilov, G. Mouchakis, J. Lehmann, and S. Auer, “Big Data Europe,” in Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference, 2017.
    [BibTeX] [Download PDF]
    @InProceedings{JabeenEtAl:EDBT/ICDT2017,
    Title = {Big Data Europe},
    Author = {Hajira Jabeen and Phil Archer and Simon Scerri and Aad Versteden and Ivan Ermilov and Giannis Mouchakis and Jens Lehmann and Soeren Auer},
    Booktitle = {Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference},
    Year = {2017},
    Crossref = {EDBT/ICDT2017WS},
    Keywords = {2017 lehmann group_aksw MOLE hellmann sys:relevantFor:infai sys:relevantFor:bis bde},
    Url = {http://ceur-ws.org/Vol-1810/EuroPro_paper_05.pdf}
    }

2016

  • H. Jabeen and J. Lehmann, Distributed Big Data platform for Life Sciences, 2016.
    [BibTeX]
    @Misc{Jab_p,
    Title = {Distributed Big Data platform for Life Sciences},
    Author = {Hajira Jabeen and Jens Lehmann},
    Note = {KAUST Research Conference on Computational and experimental interfaces of Big Data and Biotechnology, 25 - 27 January, King Abdullah University of Science and Technology, KSA},
    Year = {2016}
    }

  • Q. Abbas, J. Ahmad, and H. Jabeen, “Fitness Proportionate Random Vector Selection based DE Algorithm (FPRVDE),” INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, vol. 7, iss. 9, pp. 324-340, 2016.
    [BibTeX]
    @Article{abbas2016fitness,
    Title = {Fitness Proportionate Random Vector Selection based DE Algorithm (FPRVDE)},
    Author = {Abbas, Qamar and Ahmad, Jamil and Jabeen, Hajira},
    Journal = {INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS},
    Year = {2016},
    Number = {9},
    Pages = {324--340},
    Volume = {7},
    Publisher = {SCIENCE \& INFORMATION SAI ORGANIZATION LTD 19 BOLLING RD, BRADFORD, WEST YORKSHIRE, 00000, ENGLAND}
    }

  • Q. Abbas, J. Ahmad, and H. Jabeen, “The analysis, identification and measure to remove inconsistencies from Differential Evolution Mutation Variants,” ScienceAsia Journal, 2016.
    [BibTeX]
    @Article{abbas2016analysis,
    Title = {The analysis, identification and measure to remove inconsistencies from Differential Evolution Mutation Variants},
    Author = {Abbas, Qamar and Ahmad, Jamil and Jabeen, Hajira},
    Journal = {ScienceAsia Journal},
    Year = {2016}
    }

2015

  • Q. Abbas, J. Ahmad, and H. Jabeen, “A novel tournament selection based differential evolution variant for continuous optimization problems,” Mathematical Problems in Engineering, vol. 2015, 2015.
    [BibTeX]
    @Article{abbas2015novel,
    Title = {A novel tournament selection based differential evolution variant for continuous optimization problems},
    Author = {Abbas, Qamar and Ahmad, Jamil and Jabeen, Hajira},
    Journal = {Mathematical Problems in Engineering},
    Year = {2015},
    Volume = {2015},
    Publisher = {Hindawi Publishing Corporation}
    }

2013

  • H. Jabeen and A. R. Baig, “Two-stage learning for multi-class classification using genetic programming,” Neurocomputing, vol. 116, pp. 311-316, 2013.
    [BibTeX]
    @Article{jabeen2013two,
    Title = {Two-stage learning for multi-class classification using genetic programming},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Journal = {Neurocomputing},
    Year = {2013},
    Pages = {311--316},
    Volume = {116},
    Publisher = {Elsevier}
    }

2012

  • H. Jabeen and A. R. Baig, “Two layered Genetic Programming for mixed-attribute data classification,” Applied Soft Computing, vol. 12, iss. 1, pp. 416-422, 2012.
    [BibTeX]
    @Article{jabeen2012two,
    Title = {Two layered Genetic Programming for mixed-attribute data classification},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Journal = {Applied Soft Computing},
    Year = {2012},
    Number = {1},
    Pages = {416--422},
    Volume = {12},
    Publisher = {Elsevier}
    }

  • H. Jabeen and A. R. Baig, “GPSO: a framework for optimization of genetic programming classifier expressions for binary classification using particle swarm optimization,” International Journal of Innovative Computing, Information and Control, vol. 8, iss. 1, pp. 233-242, 2012.
    [BibTeX]
    @Article{jabeen2012gpso,
    Title = {GPSO: a framework for optimization of genetic programming classifier expressions for binary classification using particle swarm optimization},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Journal = {International Journal of Innovative Computing, Information and Control},
    Year = {2012},
    Number = {1},
    Pages = {233--242},
    Volume = {8}
    }

2011

  • H. Jabeen and A. R. Baig, “Lazy learning for multi-class classification using genetic programming,” in International Conference on Intelligent Computing, 2011, pp. 177-182.
    [BibTeX]
    @InProceedings{jabeen2011lazy,
    Title = {Lazy learning for multi-class classification using genetic programming},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Booktitle = {International Conference on Intelligent Computing},
    Year = {2011},
    Organization = {Springer},
    Pages = {177--182}
    }

2010

  • H. Jabeen and A. R. Baig, “Review of classification using genetic programming,” International journal of engineering science and technology, vol. 2, iss. 2, pp. 94-103, 2010.
    [BibTeX]
    @Article{jabeen2010review,
    Title = {Review of classification using genetic programming},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Journal = {International journal of engineering science and technology},
    Year = {2010},
    Number = {2},
    Pages = {94--103},
    Volume = {2}
    }

  • Z. Jalil, A. M. Mirza, and H. Jabeen, “Word length based zero-watermarking algorithm for tamper detection in text documents,” in Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, 2010, p. V6–378.
    [BibTeX]
    @InProceedings{jalil2010word,
    Title = {Word length based zero-watermarking algorithm for tamper detection in text documents},
    Author = {Jalil, Zunera and Mirza, Anwar M and Jabeen, Hajira},
    Booktitle = {Computer Engineering and Technology (ICCET), 2010 2nd International Conference on},
    Year = {2010},
    Organization = {IEEE},
    Pages = {V6--378},
    Volume = {6}
    }

  • H. Jabeen and A. R. Baig, “Particle swarm optimization based tuning of genetic programming evolved classifier expressions,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer, 2010, pp. 385-397.
    [BibTeX]
    @InCollection{jabeen2010particle,
    Title = {Particle swarm optimization based tuning of genetic programming evolved classifier expressions},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Booktitle = {Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)},
    Publisher = {Springer},
    Year = {2010},
    Pages = {385--397}
    }

  • H. Jabeen and A. R. Baig, “A framework for optimization of genetic programming evolved classifier expressions using particle swarm optimization,” in International Conference on Hybrid Artificial Intelligence Systems, 2010, pp. 56-63.
    [BibTeX]
    @InProceedings{jabeen2010framework,
    Title = {A framework for optimization of genetic programming evolved classifier expressions using particle swarm optimization},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Booktitle = {International Conference on Hybrid Artificial Intelligence Systems},
    Year = {2010},
    Organization = {Springer},
    Pages = {56--63}
    }

  • H. Jabeen, “Advancements in Genetic Programming for Data Classification,” PhD Thesis, 2010.
    [BibTeX]
    @PhdThesis{jabeen2010advancements,
    Title = {Advancements in Genetic Programming for Data Classification},
    Author = {Jabeen, Hajira},
    School = {National University of Computer and Emerging Sciences Islamabad},
    Year = {2010}
    }

  • H. Jabeen and A. R. Baig, “CLONAL-GP framework for artificial immune system inspired genetic programming for classification,” in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2010, pp. 61-68.
    [BibTeX]
    @InProceedings{jabeen2010clonal,
    Title = {CLONAL-GP framework for artificial immune system inspired genetic programming for classification},
    Author = {Jabeen, Hajira and Baig, Abdul Rauf},
    Booktitle = {International Conference on Knowledge-Based and Intelligent Information and Engineering Systems},
    Year = {2010},
    Organization = {Springer},
    Pages = {61--68}
    }

  • M. Imran, H. Jabeen, M. Ahmad, Q. Abbas, and W. Bangyal, “Opposition based PSO and mutation operators,” in Education Technology and Computer (ICETC), 2010 2nd International Conference on, 2010, p. V4–506.
    [BibTeX]
    @InProceedings{imran2010opposition,
    Title = {Opposition based PSO and mutation operators},
    Author = {Imran, Muhammad and Jabeen, Hajira and Ahmad, Mubashir and Abbas, Qamar and Bangyal, Waqas},
    Booktitle = {Education Technology and Computer (ICETC), 2010 2nd International Conference on},
    Year = {2010},
    Organization = {IEEE},
    Pages = {V4--506},
    Volume = {4}
    }

2009

  • H. Jabeen, Z. Jalil, and A. R. Baig, “Opposition based initialization in particle swarm optimization (O-PSO),” in Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, 2009, pp. 2047-2052.
    [BibTeX]
    @InProceedings{jabeen2009opposition,
    Title = {Opposition based initialization in particle swarm optimization (O-PSO)},
    Author = {Jabeen, Hajira and Jalil, Zunera and Baig, Abdul Rauf},
    Booktitle = {Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers},
    Year = {2009},
    Organization = {ACM},
    Pages = {2047--2052}
    }

2007

  • Z. Jalil and H. Jabeen, “Sponsor-based-architecture for resource management in multi-agent systems,” in IADIS Multi Conference on Computer Science and Information Systems, Lisbon, 2007, pp. 217-221.
    [BibTeX]
    @InProceedings{jalil2007sponsor,
    Title = {Sponsor-based-architecture for resource management in multi-agent systems},
    Author = {Jalil, Zunera and Jabeen, Hajira},
    Booktitle = {IADIS Multi Conference on Computer Science and Information Systems, Lisbon},
    Year = {2007},
    Pages = {217--221}
    }

  • Q. Abbas, J. Ahmad, and H. Jabeen, “Fitness proportionate and Tournament Selection based variation of DE algorithm (FPTDE),” Intelligent automation & Soft Computing.
    [BibTeX]
    @Article{abbasfitness,
    Title = {Fitness proportionate and Tournament Selection based variation of DE algorithm (FPTDE)},
    Author = {Abbas, Qamar and Ahmad, Jamil and Jabeen, Hajira},
    Journal = {Intelligent automation \& Soft Computing}
    }