Theme: Information Systems in the Big Data Era

Over the last years, Big Data and Artificial Intelligence technologies have gradually found their way into mainstream information systems. As these technologies mature and demonstrate their business value, they go from providing isolated functionality to becoming integrated into large and complex information systems, which entails that they have to be maintained and evolved in a sustainable manner. This maintenance imperative raises new challenges for information systems engineers due to the level of sophistication and the demanding infrastructure requirements that characterize these technologies.

The CAiSE conference will continue its tradition as the premiere venue for innovative and rigorous research across the whole spectrum of Information Systems Engineering, while placing a special emphasis on the theme of Information Systems in The Big Data Era. This year’s theme acknowledges the disruptions brought about by the abundance of Big Data sources on government and business services, their users and customers, as well as the environments in which they are generated. This data abundance creates new opportunities to develop smart and personalized information systems, but also raises new challenges for information systems engineers, for example in the areas of scalable data cleaning, integration and processing, and real-time and predictive data analytics.

Besides offering an exciting scientific program, CAiSE ’18 will feature a best paper award, a special issue, and a PhD-thesis award:

  • CAiSE ’18 Best Paper Award: CAiSE ’18 will award the best paper (prize 1.000 € – sponsored by Springer Verlag).
  • Special Issue of CAiSE ’18 in the “Information Systems Journal”: Authors of the selected papers of CAiSE ’18 will be invited to submit an extended version of their paper to the CAiSE ’18 special issue of the „Information Systems Journal“.
  • CAiSE ’18 PhD-Thesis Award: CAiSE’18 will award the best PhD thesis of a past CAISE Doctoral Consortium author (co-sponsored by the CAiSE Steering Committee and Springer). Additional information will be provided soon for this award.

Papers should be submitted in PDF format. The results described must be unpublished and must not be under review elsewhere. Submissions must conform to Springer’s LNCS format and should not exceed 15 pages, including all text, figures, references and appendices. Submissions not conforming to the LNCS format, exceeding 15 pages, or being obviously out of the scope of the conference, will be rejected without review. Information about the Springer LNCS format can be found at Three to five keywords characterizing the paper should be listed at the end of the abstract.

Submission is done through CyberChair at the following page:

Each paper will be reviewed by at least two program committee members and, if positively evaluated, by one additional board committee member. The selected papers will be discussed among the paper reviewers on-line and additionally during the program board meeting in Amesterdam. Accepted papers will be presented at CAiSE ’18 and published in the conference proceedings (Springer Lecture Notes in Computer Science (LNCS)).

Main Conference
Abstract Submission: 24 November 2017
Paper Submission: 01 December 2017 (strict!)
Notification of Acceptance: 23 February 2018
Other deadlines
Workshop Proposals: 15 October 2017
CAiSE Forum: 4 March 2018
Notification of Acceptance: 1 April 2018

We invite four types of original and scientific papers:

  • Formal and/or technical papers describe original solutions (theoretical, methodological or conceptual) in the field of IS engineering. A technical paper should clearly describe the situation or problem tackled, the relevant state of the art, the position or solution suggested and the potential – or, even better, the evaluated – benefits of the contribution.
  • Empirical evaluation papers evaluate existing problem situations or validate proposed solutions with scientific means, i.e. by empirical studies, experiments, case studies, simulations, formal analyses, mathematical proofs, etc. Scientific reflection on problems and practices in industry also falls into this category. The topic of the evaluation presented in the paper as well as its causal or logical properties must be clearly stated. The research method must be sound and appropriate.
  • Experience papers present problems or challenges encountered in practice, relate success and failure stories, or report on industrial practice. The focus is on ‘what’ and on lessons learned, not on an in-depth analysis of ‘why’. The practice must be clearly described and its context must be given. Readers should be able to draw conclusions for their own practice.
  • Exploratory papers can describe completely new research positions or approaches, in order to face a generic situation arising because of new ICT tools, new kinds of activities or new IS challenges. They must describe precisely the situation and demonstrate why current methods, tools, ways of reasoning, or meta-models are inadequate. They must also rigorously present their approach and demonstrate its pertinence and correctness to addressing the identified situation.

For all the submissions and depending on their type, we invite the authors to be explicit about the research method used.

Contributions are welcome in terms of models, methods, techniques, architecture and technologies. Each contribution should explicitly address the engineering or the operation of information systems. Each contribution should clearly identify the information systems problem addressed as well as the expected positive impact of the contribution to information system engineering or operation. We strongly advise authors to clearly emphasize those aspects in their paper, including the abstract.

Contributions about methods, models, techniques, architectures and platforms for supporting the engineering and evolution of information systems and organizations in the digital connected world could include (but are not limited to):

Novel approaches to IS Engineering

  • Context-aware and adaptive systems
  • Agile enterprise models and architecture
  • Distributed, mobile and open architecture
  • IS for collaboration
  • Social computing
  • Customer analytics
  • Big data application in IS
  • Application of AI in IS
  • Data and business analytics
  • Use of new visualization-techniques in IS
  • Service science and innovation

Models, Methods and Techniques in IS Engineering

  • Conceptual modeling, languages and design
  • Requirements engineering
  • Business process modeling, analysis, and engineering
  • Process mining
  • Models and methods for evolution and reuse
  • Domain and method engineering
  • Variability and configuration management
  • Compliance and alignment handling
  • Active and interactive models
  • Quality of IS models for analysis and design

Architectures and Platforms in and for IS Engineering

  • Big Data architectures
  • Cloud-based IS engineering
  • Service oriented IS engineering
  • Multi-agent IS engineering
  • Robotic Process Automation
  • Multi-platform IS engineeering
  • Cyber-physical systems
  • Big data and the Internet of Things
  • Blockchains
  • Digital twins
  • Workflow and PAIS systems
  • Handling of real time data streams
  • Content management and semantic Web

Domain Specific and multi-aspect IS Engineering

  • IT governance
  • eGovernment
  • Smart City management
  • Industrial ecology management
  • IS for healthcare
  • Educational IS
  • Value and supply chain management
  • Industry 4.0
  • Sustainability and social responsibility management
  • Predictive information systems
  • Big Data and privacy
  • Security and safety management
  • Dark data processing