A Case Study on Periodic Spatio- Temporal Hotspot Detection in Azure Traffic Data

Venkata M. V. Gunturi, Rakesh Rajeev, Vipul Bondre, Aaditya Barnwal, Samir Jain, Ashank Anshuman, Manish Gupta

Abstract: Given a spatio-temporal event framework E and a collection of time-stamped events A (over E), the goal of the periodic spatio-temporal hotspot detection (PST-Hotspot) problem is to determine spatial regions which show high “intensity” of events at certain periodic intervals. The output of the PST-Hotspot detection problem consists of the following: (a) a col-lection of spatial regions (which show high intensity of events) and, (b) their respective time intervals of high activity and periodicity values (e.g., daily, weekday-only, etc). PST-Hotspot detection poses significant challenge for designing a suitable interest measure. The aim over here is to design a mathematical representation of a PST-Hotspot such that it can differentiate interesting periodic patterns from trivial persistent patterns in the dataset. The current state of the art in the area of spatial and spatio-temporal hotspot detection focus on non-periodic patterns. In contrast, our proposed approach is able to determine periodic hotspots. We experimentally evaluated our proposed algorithm using real Azure traffic dataset from the Indian region.

External IDs:dblp:conf/icdm/GunturiRBBJAG22