Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?


  • Samta Shukla Rensselaer Polytechnic Institute
  • Aditya Telang IBM Reasearch, India
  • Salil Joshi IBM Reasearch, India
  • L. Subramaniam IBM Reasearch, India



Much work has been done on understanding and predicting human mobility in time. In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user. We propose an efficient way of user data representation called Spatio-Temporal Signatures to keep track of complete record of user movement. We define a measure called Spatio-Temporal similarity for comparing a given pair of users. Although computing exact pairwise Spatio-Temporal similarities between query user with all users is inefficient, we show that with our hybrid pruning scheme the most similar users can be obtained in logarithmic time with in a (1+\epsilon) factor approximation of the optimal. We are developing a framework to test our models against a real dataset of urban users.




How to Cite

Shukla, S., Telang, A., Joshi, S., & Subramaniam, L. (2015). Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).