Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior
复制标题
时间序列分类：研究鸡行为时在（字面）领域学到的经验教训
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Eamonn J. Keogh
中科院分区：
文献类型：
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作者：
Alireza Abdoli;A. Murillo;A. Gerry;Eamonn J. Keogh
家禽养殖场是人类食物链的主要贡献者。然而，在世界范围内，人们越来越关注家禽养殖场中牲畜的生活质量；要求提高动物福利标准的呼声也越来越高。传感技术和机器学习的最新进展使监测鸟类成为可能，并利用从中吸取的经验教训来改善所有鸟类的福利。这项任务表面上看起来很简单，然而，研究行为模式涉及收集大量数据，证明了“大数据”一词的合理性。在将大数据用于分析目的，以梳理出有意义的、保存良好的行为模式之前，收集到的数据需要进行预处理。预处理是指对数据进行清理和准备的过程，使其具有可供下游算法（如分类和聚类算法）分析的格式。然而，正如我们将展示的那样，鸡肉大数据的有效预处理对于进一步分析的成功既重要又至关重要。
Poultry farms are a major contributor to the human food chain. However, around the world, there have been growing concerns about the quality of life for the livestock in poultry farms; and increasingly vocal demands for improved standards of animal welfare. Recent advances in sensing technologies and machine learning allow the possibility of monitoring birds, and employing the lessons learned to improve the welfare for all birds. This task superficially appears to be easy, yet, studying behavioral patterns involves collecting enormous amounts of data, justifying the term Big Data. Before the big data can be used for analytical purposes to tease out meaningful, well-conserved behavioral patterns, the collected data needs to be preprocessed. The pre-processing refers to processes for cleansing and preparing data so that it is in the format ready to be analyzed by downstream algorithms, such as classification and clustering algorithms. However, as we shall demonstrate, efficient preprocessing of chicken big data is both non-trivial and crucial towards success of further analytics.