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 Conformance Checking in Process Mining
Submission Date: 2020-02-15

Process mining combines process model-driven approaches and data mining techniques to create methods and tools providing fact-based insights into processes and supporting process improvements (van der Aalst, 2011). Over the past two decades, the field has matured significantly from an initial focus on discovering control-flow models from event data to a myriad of new research challenges. The growth of the field of process mining is reflected by the growing community of researchers which form a recognizable and constant presence at conferences such as CAiSE and BPM - and more recently by the birth of its own International Conference on Process Mining. It is also telling that industry has witnessed an uptake of process mining techniques resulting in a growing market approaching $160 million[1].

One of the research challenges within process mining that has gained significant traction over the past decade is that of conformance checking. Conformance Checking relates modelled and recorded behaviour of a process and provides techniques and methods to compare and analyse observed process behaviour in the presence of a process model.

This Special Issue invites researchers active in the field of conformance checking to submit original research papers that explore the current boundaries of the research domain. Submissions should introduce new paradigms, address promising application domains or tackle interesting challenges which has the potential to uncover many new research opportunities for the future. Some application domains are, among others:

Process diagnostics -Process diagnostics is the broad area of exploratory data analysis which provides value to the user by adding structure and context to the original process data. Deviation detection, root cause analysis, deviation categorisation and performance analysis are examples of specific application domains that provide promising research opportunities for conformance checking techniques.

Compliance -Compliance refers to the procedures and internal control systems that organisations have in place to comply with regulations and laws, such as e.g. GDPR. Within the area of compliance, conformance checking can play an important role in checking process rules.

Process model improvement -The comparison of recorded and modelled process behaviour generates various opportunities towards process redesign and improvement. Conformance checking create opportunities for process model improvement challenges, such as model enrichment which tries to visually enrich an existing process model, as well as model repair which alters process models to better reflect reality.

Process Discovery Quality Assessment -Process Discovery Quality Assessment is the area concerned with the evaluation of process discovery algorithms. One of the main challenges in this application domain relates to the availability of suitable quality metrics to assess the quality of models with respect to the data.

Predictive business process monitoring -Predictive Business Process Monitoring refers to the prediction of the future of incomplete process instances. Conformance checking can play an important role in this application field by providing both new input features as well as the actual target variable for such prediction problems.

Potential challenges with respect to conformance checking include, but are not limited to:

Representing uncertainty and preventing bias -When conformance checking is used to make claims to what extent the process model conforms the underlying process, or the other way around, rather than the observed data, it becomes important to realize that the observed data is only an incomplete sample of the possible behaviour. Consequently, the conformance measures has the potential to be imprecise or even biased. Research is needed to address these issues.

Computational feasibility and online conformance checking -Computational feasibility is an important challenge in the field of conformance checking. As the size of event logs persistently become larger, the conformance metrics need to become increasingly computational efficient. As event logs can even become so large that it is no longer possible to store it all, conformance checking needs to operate in an online setting, which presents its own set of challenges.

Desired properties of conformance measures:A substantial part of conformance checking deals with the development of measures to express process conformance in a quantitative way. Despite the various measures that have been presented over the past decade, many challenges remain open with respect to desired properties of such measures and to what extent current measures meet these requirements. Possible requirements, among others, are logical consistency, robustness, confidence and comparability of the measures.

Multi-perspective and multi-paradigm conformance checking:Where in the early days of process mining the control-flow perspective and a procedural paradigm expressed in Petri net notation dominated the field of process mining, different perspectives, paradigms and modelling notations have been explored over time. Currently, various techniques even exist which mix these perspectives, paradigms and notations, introducing a whole new set of challenges to conformance checking.