Dissimilarity and retrieval of time-varying data towards big data analysis

In analyzing big data, data are tried to be categorized into several classes based on their similarity or dissimilarity. As the success of the analysis depends on dissimilarity, dissimilarity plays a very important role in analyzing data. Dissimilarity is also used in the similarity retrieval. This talk focuses on time-varying data; time-series data and video data. Several similarity retrieval methods of timevarying data are surveyed with consideration for big data analysis. These are categorized and explained according to the domain where dissimilarity is calculated: the time domain and the frequency domain. Characteristics of the methods are described. Dissimilarity based on the visual aspect of time-varying data is also referred. This could improve precision of the retrieval.