Graphical abstractProcess flow: from original datasets to final meta-association rules. The process starts from a set of databases {D1, , Dk} which share some of their content, i.e. they have attributes in common. After applying a rule extraction procedure, we obtain k sets of association rules represented by Ri. We are interested in searching associations between the already extracted rules in the sets Ri. For achieving this, we create a meta-database D collecting the information. We propose two different ways, by considering only presence-absence of rules (crisp meta-database, D) or taking into account their reliability represented by a degree in the unit interval (fuzzy meta-database, D). We can also introduce additional information into the process by adding new attributes about the original datasets Di. After this the so-called meta-association rules are mined. Examples of meta-association rules are depicted in the right part of the figure relating the primary rules and the added attributes. The paper explains and compares both proposals (crisp and fuzzy), proposes a level-based mining algorithm using the RL-theory for the representation of fuzziness and makes some experimentation with synthetic and real data. Display Omitted HighlightsMeta-association rules are extracted from regular rules and contextual information.Meta-rules discover which associations are more frequent in multiple datasets.Fuzzy and non-fuzzy approaches are described.The algorithm proposed avoids information loss and supports data imprecision.Fuzzy algorithm allows parameters fine-tuning with acceptable execution time. Association rules have been widely used in many application areas to extract new and useful information expressed in a comprehensive way for decision makers from raw data. However, raw data may not always be available, it can be distributed in multiple datasets and therefore there resulting number of association rules to be inspected is overwhelming. In the light of these observations, we propose meta-association rules, a new framework for mining association rules over previously discovered rules in multiple databases. Meta-association rules are a new tool that convey new information from the patterns extracted from multiple datasets and give a summarized representation about most frequent patterns. We propose and compare two different algorithms based respectively on crisp rules and fuzzy rules, concluding that fuzzy meta-association rules are suitable to incorporate to the meta-mining procedure the obtained quality assessment provided by the rules in the first step of the process, although it consumes more time than the crisp approach. In addition, fuzzy meta-rules give a more manageable set of rules for its posterior analysis and they allow the use of fuzzy items to express additional knowledge about the original databases. The proposed framework is illustrated with real-life data about crime incidents in the city of Chicago. Issues such as the difference with traditional approaches are discussed using synthetic data.

This work deals with the need for managing large amounts of fuzzy data in the context of the Semantic Web. A schema to store ontologies with fuzzy datatypes into a database is presented as part of a framework designed to perform tasks of fuzzy information extraction and publishing. The database schema allows the storage of an ontology along with its instances preserving all information. Ontology and instances are stored in different schemas in order to improve the access to instances while retaining the capacity of reasoning over the ontology. This sets the foundations of a research opportunity on the definition of a ontology reasoner over these structures. The paper also presents a brief description of the framework on which the database is included, and the structures conforming the storage schema proposed.