Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior
This work addresses the problem of improving animal welfare in poultry farms by enabling better monitoring through data analysis, but it appears incremental as it focuses on pre-processing steps rather than novel methods or broad impacts.
The paper tackles the challenge of efficiently pre-processing large-scale time series data collected from chickens to study their behavior, highlighting that this step is non-trivial and crucial for enabling downstream analytics like classification and clustering.
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 pre-processed. 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 pre-processing of chicken big data is both non-trivial and crucial towards success of further analytics.