The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Goal identification and refinement in the specification of software-based information systems
Goal identification and refinement in the specification of software-based information systems
From object-oriented to goal-oriented requirements analysis
Communications of the ACM
Requirements engineering: a roadmap
Proceedings of the Conference on The Future of Software Engineering
Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
Goal-Based Requirements Analysis
ICRE '96 Proceedings of the 2nd International Conference on Requirements Engineering (ICRE '96)
Semantic Models for Knowledge Management
WISE '01 Proceedings of the Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1 - Volume 1
IEEE Transactions on Knowledge and Data Engineering
Engineering and Managing Software Requirements
Engineering and Managing Software Requirements
Goal-oriented requirement analysis for data warehouse design
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Ontology-Driven KDD Process Composition
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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Knowledge Discovery in Databases (KDD) is a highly complex, iterative and interactive process, with a goal-driven and domain dependent nature. The complexity of KDD is mainly due to the nature of the analyzed data (which are massive, distributed, incomplete, and heterogeneous) and the nature of the process itself (since the process is by definition interactive and iterative). Given this complexity, a KDD user faces two major challenges: on the one hand, he must manipulate prior domain knowledge to better understand data and business objectives. On the other hand, he must be able to choose, configure, compose and execute tools and methods from various fields (e.g., machine learning, statistics, artificial intelligence, databases) to achieve goals. Furthermore, in the business real world, a data mining project is usually held by several actors (domain experts, data analysts, KDD experts ...), each with a different viewpoint. In this paper we propose to tackle the complexity of KDD process, and to enhance coordination and knowledge sharing between actors of a multi-view KDD analysis through a goal driven modeling of interactions between viewpoints. After a brief review of our approach of viewpoint in KDD, we will first develop a semantic Model of Goals that allows identification and representation of business objectives during the business understanding step of KDD process. Then, based on this Goal Model, we define a set of semantic relations between viewpoints of a multi-view analysis; namely equivalence, inclusion, conflict and requirement.