Knowledge Discovery in Databases
The process of automatically searching large volumes of data for patterns that can be considered knowledge about the data
Mining Data Streams
Mining data streams has recently become an important and challenging task for a wide range applications. In these scenarios data do not typically take the form of persistent relations, but tend to arrive in continuous, and time-varying data streams. Conventional knowledge discovery tools cannot manage this overwhelming volume of streaming data. The complex nature of data streams requires the use of algorithms, which involve at most one pass over the data, and try to keep track of time-evolving features. The challenges of these methods are that they are allowed to use small space and time to process a single item, while they must provide an accurate representation of some relevant characteristics of data streams.
Ontology Driven Mining
Ontologies enable the definition of domain-specific constraints to enable a more accurate mining process. By using a domain-ontology, a user can define several rules that, pushed in the knowledge extraction process improve the quality of the mining models in terms of relevance and understandability.
Business Intelligence
IT-Operational Risk Management consists of identifying, assessing, monitoring and mitigating the adverse risks of loss resulting from hardware and software system failures.
Knowledge Discovery Systems
KDD Markup Language (KDDML) is a middleware language and system designed to support the development of final applications or higher level systems which deploy a mixture of data access, data preprocessing, data mining model extraction and deployment. KDDML - Knoweldge Discovery in Databases Markup Language –
http://kdd.di.unipi.it/kddml/