Understanding Urban Spatio-Temporal Usage Patterns Using Matrix Tensor Factorization
Automated understanding of spatio-temporal usage patterns of real-world applications are significant in urban planning. With the capability of smartphones collecting various information using inbuilt sensors, the smart city data is enriched with multiple contexts. Whilst tensor factorization has been successfully used to capture latent factors (patterns) exhibited by the real-world datasets, the multifaceted nature of smart city data needs an improved modeling to utilize multiple contexts in sparse condition. Thus, in our ongoing research, we aim to model this data with a novel Context-Aware Nonnegative Coupled Sparse Matrix Tensor (CAN-CSMT) framework which imposes sparsity constraint to learn the true factors in sparse data. We also aim to develop a fast and efficient factorization algorithm to deal with the scalability problem persistent in the state-of-the-art factorization algorithms.
The specification of non-functional requirements, e. g., on timing forms an essential part of modern system design. Modeling languages such as MARTE/CCSL provide dedicated description means enabling engineers to formally define the ticking of the clocks to be implemented in terms of clock constraints and the actually intended timing behavior in terms of instant relations. But thus far, instant relations have only been utilized in order to monitor the correct execution of the clock constraints. In this work, we propose a methodology which, for the first time, verifies clock constraints against the given instant relations. To this end, the timing behavior is represented in terms of an automaton followed by its verification through satisfiability solvers. A case study illustrates the application of the proposed methodology.