Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior

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.

[1]  Eamonn J. Keogh,et al.  Time Series Classification to Improve Poultry Welfare , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[2]  Fattaneh Taghiyareh,et al.  A semantic ontology-based document organizer to cluster elearning documents , 2016, 2016 Second International Conference on Web Research (ICWR).

[3]  A. Murillo,et al.  A review of the biology, ecology, and control of the northern fowl mite, Ornithonyssus sylviarum (Acari: Macronyssidae). , 2017, Veterinary parasitology.

[4]  Eamonn Keogh,et al.  Putting the Human in the Time Series Analytics Loop , 2019, WWW.

[5]  Ali Jahanian,et al.  A general-purpose field-programmable pin-constrained digital microfluidic biochip , 2015, 2015 18th CSI International Symposium on Computer Architecture and Digital Systems (CADS).

[6]  E. P. Lewis.  Information overload. , 1976, Nursing outlook.

[7]  Eamonn J. Keogh,et al.  Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[8]  Eamonn J. Keogh,et al.  Matrix Profile XIII: Time Series Snippets: A New Primitive for Time Series Data Mining , 2018, 2018 IEEE International Conference on Big Knowledge (ICBK).

[9]  Agenor Mafra-Neto,et al.  SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining , 2011, KDD.

[10]  Wade H. Shaw,et al.  Information overload , 2010 .

[11]  Vahid Shah-Mansouri,et al.  Power allocation for statistically delay constrained video streaming in femtocell networks based on Nash Bargaining game , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[12]  Ali Jahanian,et al.  Field-programmable cell array pin-constrained digital microfluidic biochip , 2015, 2015 22nd Iranian Conference on Biomedical Engineering (ICBME).