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Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from data, often based on underlying large data sets. A major aspect of Knowledge Discovery is data mining, i.e. applying data analysis and discovery algorithms that produce a particular enumeration of patterns (or models) over the data. Knowledge Discovery also includes the evaluation of patterns and identification of which add to knowledge. This has proven to be a promising approach for enhancing the intelligence of software systems and services. The ongoing rapid growth of online data due to the Internet and the widespread use of large databases have created an important need for knowledge discovery methodologies. The challenge of extracting knowledge from data draws upon research in a large number of disciplines including statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions. Information retrieval (IR) is concerned with gathering relevant information from unstructured and semantically fuzzy data in texts and other media, searching for information within documents and for metadata about documents, as well as searching relational databases and the Web. Automation of information retrieval enables the reduction of what has been called "information overload". Information retrieval can be combined with knowledge discovery to create software tools that empower users of decision support systems to better understand and use the knowledge underlying large data sets. The primary focus of KDIR is to provide a major forum for the scientific and technical advancement of knowledge discovery and information retrieval.
Authors should submit a paper in English, carefully checked for correct grammar and spelling, addressing one or several of the conference areas or topics.
Each paper should clearly indicate the nature of its technical/scientific contribution, and the problems, domains or environments to which it is applicable. To facilitate the double-blind paper evaluation method, authors are kindly requested to produce and provide the paper WITHOUT any reference to any of the authors, including the authors’ personal details, the acknowledgements section of the paper and any other reference that may disclose the authors’ identity.
Only original papers should be submitted. Authors are advised to read INSTICC's ethical norms regarding plagiarism and self-plagiarism thoroughly before submitting and must make sure that their submissions do not substantially overlap work which has been published elsewhere or simultaneously submitted to a journal or another conference with proceedings. Papers that contain any form of plagiarism will be rejected without reviews.
All papers must be submitted through the online submission platform PRIMORIS and should follow the instructions and templates provided in the documents here, which are also the templates for the camera-ready submission. After the paper submission has been successfully completed, authors will receive an automatic confirmation e-mail.
These prices apply to both paper authors, i.e. speakers, and non-speakers.535€ - Member of INSTICC, with lunches included