Homepage Inria website
  • Inria login
  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

  • Legal notice
  • Cookie management
  • Personal data
  • Cookies

Section: Overall Objectives


Knowledge discovery in databases (KDD) consists in processing large volumes of data in order to discover knowledge units that are significant and reusable. Assimilating knowledge units to gold nuggets, and databases to lands or rivers to be explored, the KDD process can be likened to the process of searching for gold. This explains the name of the research team: in French “orpailleur” denotes a person who is searching for gold in rivers or mountains. The KDD process is based on three main operations: data preparation, data mining and interpretation of the extracted units as knowledge units. Moreover, the KDD process is iterative, interactive, and generally controlled by an expert of the data domain, called the analyst. The analyst selects and interprets a subset of the extracted units for obtaining knowledge units having a certain plausibility. In this view, KDD is an exploratory process similar to “exploratory data analysis”.

As a person searching for gold may have a certain experience about the task and the location, the analyst may use general and domain knowledge for improving the whole KDD process. Accordingly, the KDD process may be associated with knowledge bases –or domain ontologies– related to the domain of data for implementing knowledge discovery guided by domain knowledge (KDDK). In KDDK, extracted units may have “a life” after the interpretation step for becoming “actionable”: they are represented as knowledge units using a knowledge representation formalism and integrated within an ontology to be reused for problem-solving needs. In this way, knowledge discovery extends and updates existing knowledge bases, materializing a complementarity between knowledge discovery and knowledge engineering.