CaiSE 2018 : International Conference on Advanced Information Systems Engineering

  in Conferences   Posted on August 21, 2017

Conference Information

Submission Deadline Friday 24 Nov 2017
Conference & Submission Link https://caise2018.ut.ee/
Conference Dates Jun 11, 2018 - Jun 15, 2018
Conference Address Tallinn, Estonia
Proceedings indexed by
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Google Scholar H5-index: 22
CORE 2017 Rating: A
Guide2Research Overall Ranking: 213
Category Rankings
Software Engineering & Programming 52
Databases & Information Systems 60

Conference Call for Papers

The 30th Edition of the CAiSE conference series will be hosted in Tallinn, Estonia – home to a groundswell of IT startups and to one of the most advanced digital societies. The conference will continue its tradition as the premiere venue for innovative and rigorous research across the whole spectrum of Information Systems Engineering with a special emphasis on the theme Information Systems in The Big Data Era. This year’s theme acknowledges the disruptions brought about by the abundance of Big Data sources about government and business services, their users and customers, and their environments. This 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.

The conference will be hosted by the Software Engineering and Information Systems Research Group, Institute of Computer Science, University of Tartu.
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.

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

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