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 Managing, Mining and Learning in the Legal Data Domain
Submission Date: 2020-10-15

Legal domain is a challenging focus of attention for scholars in computer science and engineering related fields as it lends itself to a unique blend of research opportunities at convergence not only with law and jurisprudence, but also humanities, linguistics, social sciences, economics, cognitive psychology, and other disciplines. This has been long witnessed by a number of venues for developing and publishing computer-science-related research studies applied to the legal domain, for which the volume of data of interest is rapidly growing, also thanks to the support of Internet and online media platforms. Moreover, recent breakthroughs in data science, machine learning, and cybersecurity, have unveiled a range of new opportunities and solutions for dealing with legal information sources and providing a deeper understanding of laws, legal systems, legal reasoning, and the role and impact of laws in our society.

This Special Issue of the Information Systems Journal invites researchers working in the field cross-cutting information and knowledge-based systems, data science and artificial intelligence, and legal informatics to submit original papers discussing and promoting ideas and practices about advanced data management and analytics technologies for the legal domain. This would help legal professionals handle a variety of critical cases, which may benefit from getting easier access to law data, gaining insights into knowledge patterns hidden in legal data, argumenting and supporting legal decision-making.

In this regard, we solicit theoretical as well as application-oriented research studies on relevant topics related to the processing, management and analysis of legal databases and text corpora, covering models, methodologies, algorithms, evaluation benchmarks and tools for the development and application of legal information systems and knowledge engineering.

Topics of interest include, but are not limited to:

Automated information extraction from legal databases and text corpora
Web-based systems engineering for searching, retrieving and managing legal data
Legal knowledge representation and reasoning models and methods
Computational models of argumentation for legal data
Natural language processing techniques and systems for legal documents
Machine learning, deep learning, and reinforcement learning for legal data
Semantic computing for legal data
Big data analytics for legal data
Computational models and systems handling ethical and fairness issues in the legal domain
Cybersecurity in the legal domain
Emerging applications in legal data & knowledge engineering