May 15-17, 2020 | The University of Manchester, UK


2020 the 5th International Conference on Information Systems Engineering (ICISE2020) will be held at The University of Manchester, Manchester, UK during May 15-17, 2020. Big data abundance creates new opportunities to develop smart and personalized information systems, and concomitantly raises new challenges for information systems engineers, for example in the areas of scalable data cleaning, integration and processing, real-time and predictive data analytics, and cognification of information systems engineering.

ICISE2019 is a forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences, and work-in-process. This conference will also provide an excellent opportunity for the young researchers to expose their work to international scrutiny, receive feedback from peers from different parts of world, gain from the vast experience and expertise of the leaders in this important field of research and to open up the scope for new research collaborations among the international community of participants and invited delegates.

Previous ICISE conferences were held in Los Angeles, USA (2016), College of Charleston, USA (2017) and Shanghai, China (2018 & 2019). 

All submissions will be peer reviewed, and all the accepted papers will be published in the ICISE2020 conference Proceedings. Conference content will be submitted for inclusion into IEEE Xplore, Ei Compendex and Scopus .


ICISE2019 Keynote & Plenary Speakers

  • Prof. Houssain Kettani,
    Dakota State University, USA

    Speech Title: Advances in High Performance Computing and Impact on Cybersecurity

  • Prof. Ying Tan
    Peking University, China

    Speech Title: Swarm Intelligence Algorithms-Fireworks Algorithm and Its Applications

  • Chen-Huei Chou,
    College of Charleston, SC, USA

    Speech Title: State-of-the-art Internet abuse detection

  • Heng Chen
    Shanghai Information Center for Life Sciences,Chinese Academy of Sciences, China

    Speech Title: Application of Linked Data in HIV Literature Database and HIV Protein Ontology

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