Events

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

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.

Panos K. Chrysanthis, University of Pittsburgh

Abstract: Online analytics, in most advanced scientific, business, and defense applications, rely heavily on the efficient execution of large numbers of Aggregate Continuous Queries (ACQs). ACQs continuously aggregate streaming data and periodically produce results such as max or average over a given window of the latest data.  It was shown that in processing ACQs it is beneficial to use incremental evaluation, which involves storing and reusing calculations performed over the unchanged parts of the window, rather than performing the re-evaluation of the entire window after each update.

Zeynep Korkmaz, PhD candidate
David R. Cheriton School of Computer Science

Analysis on graphs have powerful impact on solving many social and scientific problems, and applications often perform expensive traversals on large scale graphs. Caching approaches on top  of persistent storage are among the classical solutions to handle high request throughput. However, graph processing applications have poor access locality, and caching algorithms do not improve disk I/O sufficiently. We present GAL, a graph-aware layout for disk-resident graph databases that generates a storage layout for large-scale graphs on disk with the objective of increasing locality of disk blocks and reducing the number of I/O operations for transactional workloads.

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science

Dynamic sampling (DS) is applied to create a sampled set of relevance judgments in our participation of TREC Common Core Track 2018. One goal was to test the effectiveness and efficiency of this technique with a set of non-expert, secondary relevance assessors.  We consider NIST assessors to be the experts and the primary assessors. Another goal was to make available to other researchers a sampled set of relevance judgments (prels) and thus allow the estimation of retrieval metrics that have the potential to be more robust than the standard NIST provided relevance judgments (qrels). In addition to creating the prels, we also submitted several runs based on our manual judging and the models produced by our HiCAL system. 

Speaker: Verena Kantere, University of Ottawa

Abstract: Big Data analytics in science and industry are performed on a range of heterogeneous data stores, both traditional and modern, and on a diversity of query engines. Workflows are difficult to design and implement since they span a variety of systems. To reduce development time and processing costs, some automation is needed. In this talk we will present a new platform to manage analytics workflows.

Chang Ge, PhD candidate
David R. Cheriton School of Computer Science

Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current systems for differentially private query answering place an inordinate burden on the data analysts to understand differential privacy, manage their privacy budget and even implement new algorithms for noisy query answering. Moreover, current systems do not provide any guarantees to the data analyst on the quantity they care about, namely accuracy of query answers.

Alexey Karyakin, PhD candidate
David R. Cheriton School of Computer Science

Energy consumed by the main memory in existing database systems does not effectively scale down with lower system utilization, both in terms of actual memory usage and load conditions. At the same time, main memory represents a sizable portion of the total server energy footprint, which makes it an outlier as the rest of the system moves towards energy proportionality. 

We introduce DimmStore, a prototype main-memory database system that addresses the problem of memory energy consumption.

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science