Events by month

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. 

A. Erdem Sarıyüce, University at Buffalo

Abstract: Finding dense substructures in a network is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasi-clique, densest at-least-k subgraph) are NP-hard. Furthermore, the goal is rarely to find the “true optimum” but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine relationships among them. In this talk, I will talk about a framework that we designed to find dense regions of the graph with hierarchical relations.

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.  To do this, we define a set of primitives for dependency discovery, which corresponds to data processing steps separated by communication barriers, and we present efficient implementations that optimize both computation and communication costs.  Using real data, we show that algorithms built using our primitives are significantly faster and more communication-efficient than straightforward distributed implementations.

Jaemyung Kim, PhD candidate
David R. Cheriton School of Computer Science

Transaction durability guarantees the ability to recover committed transactions from failures. However, making every transaction durable impacts transaction processing performance. Some ad-hoc durability mechanisms (e.g., delayed durability) improve performance, but they risk transactions losing their effects due to failures. The current one-size-fits-all transaction durability model does not solve this problem. We propose a new generalized transaction durability model to trade-off performance and durability and argue that transactions should also provide flexible durability like they provide multiple isolation levels. We evaluate the performance of a modified PostgreSQL that supports the new durability model using a micro-benchmark to show the durability/performance trade-offs.