Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining approximate top-k subspace anomalies in multi-dimensional time-series data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
The TS-tree: efficient time series search and retrieval
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Key performance indicators: developing, implementing, and using winning kpis
Key performance indicators: developing, implementing, and using winning kpis
iSAX: disk-aware mining and indexing of massive time series datasets
Data Mining and Knowledge Discovery
iSAX 2.0: Indexing and Mining One Billion Time Series
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Awareness requirements for adaptive systems
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Business intelligence modeling in action: a hospital case study
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
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Modeling the strategic objectives has been shown to be useful both for understanding a business as well as planning and guiding the overall activities within an enterprise. Business strategy is modeled according to human expertise, setting up the goals as well as the indicators that monitor activities and goals. However, usually indicators provide high-level aggregated views of data, making it difficult to pinpoint problems within specific sub-areas until they have a significant impact into the aggregated value. By the time these problems become evident, they have already hindered the performance of the organization. However, performing a detailed analysis manually can be a daunting task, due to the size of the data space. In order to solve this problem, we propose a user-driven method to analyze the data related to each business indicator by means of data mining. We illustrate our approach with a real world example based on the Europe 2020 framework. Our approach allows us not only to identify latent problems, but also to highlight deviations from anticipated trends that may represent opportunities and exceptional situations, thereby enabling an organization to take advantage of them.