Anil Pacaci, PhD candidate
David R. Cheriton School of Computer Science
Paolo Atzeni, Database Professor and Head of the Department of Engineering
Università Roma Tre
NoSQL systems have gained their popularity for many reasons, including the flexibility they provide with modeling, which tries to relax the rigidity provided by the relational model and by the other structured models.
Speaker: Stratos Idreos, Harvard University
Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science
Speaker: Mohammad Sadoghi, UC Davis
Mina Farid, PhD candidate
David R. Cheriton School of Computer Science
RDF has become a prevalent format to represent disparate data that is ingested from heterogeneous sources. However, data often contains errors due to extraction, transformation, and integration problems, leading to missing or contradicting information that propagate to downstream applications.
Mustafa Korkmaz, 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.
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.