You are here

2018 Events Schedule | Data Systems Group | UW

[Please remove <h1>]

Please note that this event list is no longer being updated. For current events, please visit our new site

Public talks of interest to the Data Systems Group are posted here, and are also mailed to the dsg-faculty, dsg-grads, dsg-friends mailing lists. Subscribe to one of these mailing lists to receive e-mail notification of upcoming events. Everyone is welcome to attend.

Information about older events can be found in the event archive.

2018 Events

PhD Seminar: Wednesday January 17, 12:30 pm, MC 5417
Speaker: Brad Glasbergen
Title: Title: Apollo: Learning Database Query Correlations for Predictive Caching
Absract: The performance of modern database applications is increasingly dependent on remote access latencies. The worldwide distribution of users results in significant latency for clients that are distant from the application's data center. Systems that reduce latencies by caching query data closer to clients are becoming more popular but do not take advantage of application-specific behaviour patterns. In this talk, I will present Apollo, a system that learns database query patterns and exploits them to enhance query performance via predictive caching. Experiments demonstrate Apollo's efficacy as a predictive cache for geo-distributed database applications.

PhD Seminar: Wednesday March 14, 1:00 pm, MC 5417
Speaker: Michael Abebe
Title: Title: EC-Store: A Dynamic Distributed Erasure Coded Storage System
Absract: Cloud storage systems typically choose between replicating or erasure encoding data to provide fault tolerance. Replication ensures that data can be accessed from a single site but incurs a much higher storage overhead, which is a costly downside for large-scale storage systems. Erasure coding has a lower storage requirement but relies on encoding/decoding and distributed data retrieval that can result in increased response times. In this talk I will present EC-Store, a dynamic distributed erasure coded storage system that significantly reduces data retrieval times when compared to replicated and erasure coded storage systems. EC-Store achieves these reductions in latency by making intelligent data access decisions and dynamically moving data in response to system load and access patterns.

CS Seminar: Monday March 26, 10:30 am, DC 1304
Speaker: Xi He, Duke University
Title: Leave No Trace: Personal Data with Provable Privacy Guarantees

CS Distinguished Lecture Series: Monday March 26, 3:30 pm, DC 1302
Speaker: Jennifer Widom, Stanford University
Title: Magic Moments in Research and Teaching

DSG Seminar Series: Friday April 20, 10:30 am, DC 1304
Speaker: Lei Zou, Peking University
Title: Speedup Set Intersections in Graph Algorithms using SIMD Instructionsnotes

DSG Seminar Series: Monday April 23, 10:30 am, DC 1302
Speaker: Barzan Mozafari, University of Michigan
Title: Making Approximate Query Processing Mainstream: Progress and the Road Ahead

DSG Seminar Series: Thursday April 26, 10:30 am, DC 1304
Speaker: Rachel Pottinger, University of British Columbia
Title: Improving Understanding and Exploration of Data by Non-Database Experts

DSG Seminar Series: Thursday May 10, 2:00 pm, DC 1304
Speaker: Daniel Lemire, Université Télug
Title: Next Generation Indexes For Big Data Engineering

MMath Thesis Presentation: Friday May 11, 1:30 pm, DC 1304
Speaker: Babar Naveed Memon
Title: RAMP: RDMA Migration Platform

MMath Thesis Presentation: Wednesday May 16, 1:00 pm, DC 2585
Speaker: Dallas Fraser
Title: Math Information Retrieval using a Text Search Engine

DSG Seminar Series: Thursday July 12, 10:30 am, DC 1302
Speaker: Torben Bach Pedersen, Aalborg University
Title: Managing Big Multidimensional Data – A Journey from Data Acquisition to Prescriptive Analytics

PhD Seminar: Wednesday July 18, 1:00 pm, DC 2585
Speaker: Anil Pacaci
Title: Experimental Analysis of Streaming Algorithms for Graph Partitioning
Absract: Graph partitioning in streaming setting is the problem of splitting graph structured data over a cluster of machines in the context of distributed graph processing systems. Streaming algorithms for graph partitioning has recently gained attention due to its ability to scale very large graphs with limited resources. This study first characterizes streaming algorithms for graph partitioning based on the distribution model. We identify edge-cut and vertex-cut model and provide analysis of existing algorithms in literature. The main objective is to understand how the choice of graph partitioning algorithm affects the system performance, resource usage and scalability. To this end, we provide systematic empirical evaluation of existing algorithms on large, real-world graphs using two classes of workloads: offline graph analytics and online graph queries. In our study, we argue that "no one size fits all" and choice of graph partitioning algorithm depends on: (i) type and degree distribution of the graph, (ii) characteristics of the workload and (iii) requirements of the application.

DSG Seminar Series: Monday July 30, 2:00 pm, DC 1304
Speaker: Paolo Atzeni, Università Roma Tre
Title: Data Models from Traditional Databases to NoSQL Systems

This page is maintained by Ken Salem.