Knowledge Discovery and Data Mining is a very dynamic research and development area that is reaching maturity. As such, it requires stable and well-defined foundations, which are well understood and popularized throughout the community. This survey presents a historical overview, description and future directions concerning a standard for a Knowledge Discovery and Data Mining process model. It presents a motivation for use and a comprehensive comparison of several leading process models, and discusses their applications to both academic and industrial problems. The main goal of this review is the consolidation of the research in this area. The survey also proposes to enhance existing models by embedding other current standards to enable automation and interoperability of the entire process.

In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) and ‘vertical’ (candidate distribution) partitioning strategies are considered, including a vertical partitioning algorithm (DATA-VP) which we have developed to exploit the structure of the T-tree. We present experimental results examining the performance of the methods in implementations using JavaSpaces. We conclude that in a JavaSpaces environment, candidate distribution strategies offer better performance than those that distribute the original dataset, because of the lower messaging overhead, and the DATA-VP algorithm produced results that are especially encouraging.

In contrast to standard approaches based on agent communication languages (ACLs), environment-based coordination is emerging as an interesting alternative for structuring interactions in multiagent systems (MASs). In particular, the notion of coordination artifacts has been proposed as an engineering methodology to build runtime abstractions effectively providing collaborating agents with specifically designed coordination tasks.

In this paper, we study the semantics for the interaction of agents with coordination artifacts playing the same role of ACL semantics, that is, supporting semantic interoperability between agents developed by different parties through the connection between rationality and interaction. Our approach is rooted on the notion of operating instructions of coordination artifacts, which—as with a manual for a human exploiting a device—describe the interaction protocols the agent can follow as well as the mentalistic semantics of each single interaction. By tackling some of the most relevant issues raised in the context of ACL semantics, our framework allows intelligent, BDI-like agents to carry on complex interactions through coordination artifacts in a rational way.