Journal Guide

Database Systems and Knowledgebase Systems share many common principles. Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems. DKE achieves this aim by publishing original research results, technical advances and news items concerning data engineering, knowledge engineering, and the interface of these two fields.DKE covers the following topics:1. Representation and Manipulation of Data & Knowledge: Conceptual data models. Knowledge representation techniques. Data/knowledge manipulation languages and techniques.2. Architectures of database, expert, or knowledge-based systems: New architectures for database / knowledge base / expert systems, design and implementation techniques, languages and user interfaces, distributed architectures.3. Construction of data/knowledge bases: Data / knowledge base design methodologies and tools, data/knowledge acquisition methods, integrity/security/maintenance issues.4. Applications, case studies, and management issues: Data administration issues, knowledge engineering practice, office and engineering applications.5. Tools for specifying and developing Data and Knowledge Bases using tools based on Linguistics or Human Machine Interface principles.6. Communication aspects involved in implementing, designing and using KBSs in Cyberspace.Plus... conference reports, calendar of events, book reviews etc.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

The premier technical publication in the field, Data Mining and Knowledge Discovery is a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities. The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications. Coverage includes: - Theory and Foundational Issues - Data Mining Methods - Algorithms for Data Mining - Knowledge Discovery Process - Application Issues.

The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.

All data is in scope, whether born digital or converted from other sources.

Data Technologies & Applications focusses on the management of digital information, mostly covering Information Science and Information System aspects. Covers all aspects of the data revolution brought about by the Internet and the World-Wide-Web.

• Thoroughly describe your data, facilitating reproducibility.
• Make your data, which is often buried in supplementary material, easier to find.
• Increase traffic towards associated research articles and data, leading to more citations.
• Open up doors for new collaborations.

Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.

Data Science for Transportation publishes high-quality original research and reviews in a wide range of topics related to Data Science for Transportation. This includes classical approaches when data sources are used to unravel underlying physical mechanisms leading to general laws and new modelling frameworks. It also includes new data-driven approaches when AI plays a central role.

The goal of the journal is to showcase the latest methodological advances and applications of data science methods in transportation and appropriate implications for policy making. The journal is also interested in the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where big data intelligence can augment other methods in transportation systems planning, operations, freight, safety analysis, transit, safe and sustainable cities and emergency management. There are many emerging questions of relevance on ethical, social and privacy, that are also relevant in this domain. The focus is primarily on analytical data driven methods. High quality application based studies will also be considered.

Journal closed. Available from 1978 volume: 1 until 1999 volume: 22 issue: 3