QMMQ 2018 : 5th workshop Quality of Models and Models of Quality QMMQ
Call For Papers
5th workshop on Quality of Models and Models of Quality QMMQ'18
In conjunction with the
37th International Conference on Conceptual Modeling (ER2018)
To be held in
Xi'an, China. from Oct. 22-25, 2018
website : http://qmmq2018.cnam.fr/index.php
Information Systems practices are evolving rapidly in the Internet age. We are witnessing the emergence of collaborative designs, user generated contents, crowdsourcing information systems and other nontraditional methods based Information systems. Consequently, a new challenge facing the researchers as well as practitioners is How to ensure the quality of such systems? In a world in which we observe traditional methods that depends on heuristics in decision making on a one hand and new data driven approaches, we believe that models have a crucial role to play.
Quality assurance has been and is still a very challenging issue within the Information Systems (IS) and Conceptual Modeling (CM) disciplines. The research encompasses theoretical aspects including quality definition and quality models, and practical/empirical aspects such as the development of methods, approaches and tools for quality measurement and improvement. Nowadays, with the development of web technologies and the growth of collected and exploited data volumes (or to exploit), IS and CM communities are faced to new challenges: they have to envision new perspectives to the problem of evaluating quality in IS.
The QMMQ workshop intends to provide a space for fruitful exchanges involving both researchers and practitioners having a variety of interests such as: data quality, information quality, system quality as well as models, methods, processes and tools for managing quality. The aim of the workshop is twofold. Firstly, to provide an opportunity for researchers and industry developers working on various aspects of information systems quality to exchange research ideas and results and discuss them. Secondly, to promote research on information systems and conceptual model quality to the broader conceptual modeling research community attending ER 2018.
It is now more and more agreed that there is a need to develop the capacity to understand how the quality of data affects the quality of the insight we derive from it. However, this quality, to be ensured, requires reliable IS that can only be designed with a precise ontological commitment. Moreover, research on quality needs more contributions based on experimentation to provide empirical evidences of successful IS design. Empirical Software Engineering techniques and protocols should be followed in the CM modeling to provide reliable and useful results to assess IS quality.
For its 5th edition, the QMMQ workshop will have a special focus on data quality. Authors are encouraged to submit papers related to data quality frameworks, methods for assessing and improving data quality as well as empirical studies for the evaluation of data quality specially if they are connected with modeling approaches.
Topics of interest
The workshop is not restricted to particular research methods and we will consider both conceptual and empirical research, as well as novel applications. Particular topics of interest related to IS and CM quality include, but are not limited to:
-- Quality constructs, models and ontologies
-- Quality measures and instruments
-- Experiments for validating quality models, measures and instruments
-- Methodological issues of research on IS quality
-- Data quality
-- Big data quality
-- Method and tool support for improving and monitoring quality
-- Quality of requirements engineering artifacts and processes
-- Quality of models and meta-models
-- Quality of ontologies and reference models
-- Quality of decision-making processes and models
-- Quality modeling languages
-- Ontological analysis of conceptual modeling grammars
-- Cost/benefit analysis of quality assurance processes
-- Quality assurance practices : case studies and experiences
-- Experiments and case studies on quality evaluation.
-- Quality of end-users interfaces
-- Quality of experimental processes