A computationally efficient approach for temporal pattern mining in IoT

Data generated in IOT is implicitly temporal. This research essentially focuses on design of novel similarity measure to find similarity between temporal patterns subjected to subset specifications which include specifying reference, a distance value permissible. At present, existing approaches do not reveal seasonal patterns, emerging or diminishing patterns. Determining similar temporal patterns and unearthing eccentric patterns require an efficient measure. This research makes attempt to come up with an efficient similarity measure for revealing similar temporal patterns in time series data generated by IoT.