提高基于大数据和人工智能决策的房地产管理能力
1School of Management Engineering, Guangxi Construction Vocational and Technical College, Nanning, China.
PloS one
|August 14, 2024
概括
本研究引入了知识依赖数据处理方案 (KDPS) 用于房地产管理. 该KDPS改进了数据分析有利可图的房地产利用和减少处理时间.
科学领域:
- 房地产管理 房地产管理
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 有效的房地产管理需要分析历史购买和使用数据.
- 识别有价值的数据以增加价值和利利用仍然是一个挑战.
研究的目的:
- 为精确的房地产数据分析提出一个依赖于知识的数据处理方案 (KDPS).
- 加强数据的选择和处理,以提高房地产利用效率.
主要方法:
- 为了数据的选择和处理,开发了一个两级的KDPS.
- 来自销售,设施和市场价值的知识被整合在一起.
- 停滞和实际价值被用于训练数据处理系统.
主要成果:
- 该KDPS提高了8.2%的处理率,10.25%的停滞检测,10.28%的成功率和7%的培训比率.
- 与现有方法相比,处理时间减少了8.56%.
- 该计划确保了有利可图的特征和减少了停滞时间.
结论:
- KDPS为房地产数据分析提供了更精确,更有效的方法.
- 它有效地缓解了停滞,并确定了房地产中有利可图的特征.
- 这种方法支持知情决策,以改善房地产资产管理.
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