Energy-Efficient Design of Distributed Data Warehousing Systems for High-Performance Data Mining Applications in Smart Cities
Keywords:
Smart Cities, Distributed Data Warehouse, Energy Efficiency, Data Mining, Edge Computing, Query OptimizationAbstract
The integration of data mining applications in smart cities relies heavily on large-scale, distributed data warehousing systems. However, these infrastructures often suffer from excessive energy consumption due to redundant data replication, inefficient query processing, and poorly optimized storage. This paper proposes an energy-efficient architectural design for distributed data warehousing tailored to smart cities, aiming to reduce carbon footprints while ensuring high performance in data mining operations. We review existing approaches, identify research gaps, and present a framework integrating edge computing, energy-aware scheduling, and adaptive query optimization. Simulation results demonstrate a 22.4% reduction in energy use without compromising data throughput.
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Copyright (c) 2022 Satoshi Yamamoto (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
