Bushra Aloraini, PhD candidate
David R. Cheriton School of Computer Science
Amine Mhedhbi, PhD candidate
David R. Cheriton School of Computer Science
We study the problem of optimizing subgraph queries (SQs) using the new worst-case optimal (WCO) join plans in Selinger-style cost-based optimizers. WCO plans evaluate SQs by matching one query vertex at a time using multiway intersections. The core problem in optimizing WCO plans is to pick an ordering of the query vertices to match.
We make two contributions:
Jeff Avery, PhD candidate
David R. Cheriton School of Computer Science
Professor Brian Forrest
Department of Pure Mathematics, University of Waterloo
There are many challenges to teaching mathematics in a fully online environment. In this talk I will show the important role that assigned work plays in mitigating many of these challenges. I will also speak about how my experience in teaching online has impacted the way in which I approach my on campus courses.
Hemant Saxena, PhD candidate
David R. Cheriton School of Computer Science
We address the problem of discovering dependencies from distributed big data. Existing (non-distributed) algorithms focus on minimizing computation by pruning the search space of possible dependencies. However, distributed algorithms must also optimize data communication costs, especially in current shared-nothing settings.
Abel Molina, PhD candidate
David R. Cheriton School of Computer Science
Yao (1993) proved that quantum Turing machines and uniformly generated quantum circuits are polynomially equivalent computational models: t >= n steps of a quantum Turing machine running on an input of length n can be simulated by a uniformly generated family of quantum circuits with size quadratic in t, and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time.
Xinan Yan, PhD candidate
David R. Cheriton School of Computer Science
N. Asokan, Department of Computer Science
Aalto University, Finland
All kinds of previously local services are being moved to cloud settings. While this is justified by the scalability and efficiency benefits of cloud-based services, it also raises new security and privacy challenges. Solving them by naive application of standard security/privacy techniques can conflict with other functional requirements. In this talk, I will outline some cloud-assisted services and the conflicts that arise while trying to secure these services.
Li Liu, PhD candidate
David R. Cheriton School of Computer Science
Entanglement is a type of resource used in quantum information theory that gives correlations that cannot be simulated using classical probability theory. It is known that entanglement cannot be created locally.