Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data.

Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives. AKD is a closed optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and is designed to deliver operable business rules that can be seamlessly associated or integrated with business processes and systems. To support such processes, we correspondingly propose, formalize, and illustrate four types of generic AKD frameworks: Postanalysis-based AKD, Unified-Interestingness-based AKD, Combined-Mining-based AKD, and Multisource Combined-Mining-based AKD (MSCM-AKD). A real-life case study of MSCM-based AKD is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making.

The pairwise mining problem is to discover pairwise objects having measures greater than the user-specified minimum threshold from a collection of objects. It is essential in a large variety of database and data-mining applications. Of late, there has been increasing interest in applying a Locality-Sensitive Hashing (LSH) scheme for pairwise mining. LSH-type methods have shown themselves to be simply implementable and capable of achieving significant performance gain in running time over most exact methods. However, the present LSH-type methods still suffer from some bottlenecks, such as ¿the curse of threshold¿. In this paper, we proposed a novel LSH-based method, namely Cost-effective LSH filter (Ce-LSH for short), for pairwise mining. Compared with previous LSH-type methods, it uses a lower fixed number of LSH functions and is thus more cost-effective. Substantial experiments evidence that our method gives significant improvement in running time over existing LSH-type methods and some recently reported method based on upper-bound. Experimental results also indicate that it scales well even for a relatively low minimum threshold and for a fairly small miss ratio.

Early requirements analysis (ERA) is quite significant for building agent-based systems. Goal-oriented requirements analysis is promising for the agent-oriented early requirements analysis. In general, either visual modeling or formal specifications is used for the ERA. This way cannot capture requirements precisely and completely. We present an integrative modeling framework for agent-oriented early requirements analysis; this framework implements goal-oriented requirement analysis. The integrative modeling combines visual modeling and formal modeling together. Extended i* framework is used for building visual models; formal specifications complement the visual modeling to define and refine requirements. Both visual and formal models are outlined through a practical agent-based system F-TRADE. The integrative modelling seems to model early requirements comprehensively and concretely, and benefit refinement and conflict management in building agent systems.

Usually, integration of business intelligence (BI) from realistic telecom enterprise is by packing data warehouse (DW), OLAP, data mining and reporting from different vendors together. As a result, BI system users are transferred to a reporting system with reports, data models, dimensions and measures predefined by system designers. As a result of survey, 85% of DW projects failed to meet their intended objectives. In this paper, we investigate how to integrate BI packages into an adaptive and flexible knowledge portal by constructing an internal link and communication channel from top-level business concepts to underlying enterprise information systems (EIS). An approach of three-level ontology services is developed, which implements unified naming, directory and transport of ontology services, and ontology mapping and query parsing among conceptual view, analytical view and physical view from user interfaces through DW to EIS. Experiments on top of real telecom EIS shows that our solution for integrating BI presents much stronger power to support operational decision making more user-friendly and adaptively compared with those simply combining BI products presently available together.