Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.

Subject areas include data management issues as presented in the principal international database conferences (e.g. ACM SIGMOD, ACM PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining, information retrieval, internet and cloud data management, web semantics, visual and audio information systems, scientific computing, and organisational behaviour. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome.

All papers should motivate the problems they address with compelling examples from real or potential applications. Systems papers must be serious about experimentation either on real systems or simulations based on traces from real systems. Papers from industrial organisations are welcome.

Theoretical papers should have a clear motivation from applications. They should either break significant new ground or unify and extend existing algorithms. Such papers should clearly state which ideas have potentially wide applicability.

In addition to publishing submitted articles, the Editors-in-Chief will invite retrospective articles that describe significant projects by the principal architects of those projects. Authors of such articles should write in the first person, tracing the social as well as technical history of their projects, describing the evolution of ideas, mistakes made, and reality tests.
Technical results should be explained in a uniform notation with the emphasis on clarity and on ideas that may have applications outside of the environment of that research. Particularly complex details may be summarised with references to previously published papers.

We will make every effort to allow authors the right to republish papers appearing in Information Systems in their own books and monographs. 

Special Issue on Process mining meets visual analytics
Día de Entrega: 2024-06-05

Process mining (PM) is a body of techniques blending data science concepts with business process management (BPM). It utilizes event data recorded by IT systems that support process execution for a variety of tasks. These include the automated discovery of graphical process models, conformance checking between data and models, enhancement of process models with additional analytic information, run-time monitoring of processes and operational support. Thus far, research efforts in the PM area have primarily focused on algorithms and methods from a technical perspective. Less attention has been paid to supporting PM practitioners throughout the entire PM process. An important means for supporting human sense-making is through visualization of the analysis processes, the input (event data), and results. Appropriate visualizations can trigger hypotheses, drive additional analysis, reveal patterns, and raise insights.

Visual Analytics (VA) is a multidisciplinary approach, integrating aspects of data mining and knowledge discovery, information visualization, human-computer interaction, and cognitive science, with the aim to make complex phenomena more comprehensible, facilitate new insights, and enable knowledge discovery from data. VA leverages the specific strengths of computers and humans for the best possible outcome: on the one hand, computers are better at managing and processing large amounts of data by exploiting their computational power; on the other hand, humans have better perceptual and cognitive means, which enable them to visually perceive unexpected patterns and to interpret data. 
VA and PM exhibit complementary traits that would greatly and mutually benefit from joining forces. However, the scientific body of literature reports on few endeavours towards this direction.

This special issue aims to contribute to the cross-fertilization of VA and PM, and welcomes works centred around methods and techniques stemming from, and contributing to, these disciplines.

Guest editors:

Dr. Claudio Di Ciccio (Executive Guest Editor)Utrecht University, Utrecht, NetherlandsEmail: c.diciccio@uu.nl Areas of Expertise: Process mining, automated reasoning in AI, blockchain technologies

Prof. Pnina SofferUniversity of Haifa, Haifa, IsraelEmail: spnina@is.haifa.ac.il Areas of Expertise: Business process management, process mining, conceptual modeling

Prof. Barbara WeberUniversity of St. Gallen, St. Gallen, SwitzerlandEmail: barbara.weber@unisg.ch Areas of Expertise: Process modeling, process mining, adaptive software systems

Prof. Silvia MikschTU Wien, Vienna, AustriaEmail: silvia.miksch@tuwien.ac.at Areas of Expertise: Visualization and visual analytics over time and space, guidance, plan and process modeling