Temporal Data Mining Techniques for Incremental Updates in Time-Variant Data Warehouses Supporting Strategic Business Intelligence
Keywords:
Temporal data mining, incremental update, time-variant data warehouse, business intelligence, trend analysis, temporal patternsAbstract
Temporal data warehouses play a critical role in enabling strategic business intelligence (BI) through historical data analysis and trend forecasting. However, managing time-variant data efficiently in such warehouses remains challenging due to the evolving nature of business processes and data sources. This paper explores recent advances in temporal data mining (TDM) techniques tailored for incremental update strategies. We analyze how these methods maintain consistency and performance in time-variant data warehouses while supporting real-time or near-real-time decision-making. Through a structured literature review and analytical modeling, we identify key temporal mining algorithms and update strategies. We then evaluate their integration within time-variant architectures that support evolving BI requirements. This study contributes a conceptual framework for improving scalability, accuracy, and responsiveness in enterprise data environments, laying the foundation for future intelligent data management systems
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Copyright (c) 2021 Michael Sørensen (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
