Chang Ge, PhD candidate
David R. Cheriton School of Computer Science
Speaker: David Doermann, University at Buffalo
Bradley Glasbergen, PhD candidate
David R. Cheriton School of Computer Science
Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science
Speaker: Umar Farooq Minhas, Microsoft Research
Michael Abebe, PhD candidate
David R. Cheriton School of Computer Science
Workload access patterns can limit the performance achievable with static data placement in distributed database systems. Dynamic physical database designs in which data item master locations, partitioning schemes and replica locations can vary with the workload help improve system performance.
Joan Bartlett, School of Information Studies
McGill University
Millennials have been found to rely heavily on information obtained from the web and social networks; but it is also seen that they may not be able to judge the authenticity, validity and reliability of the digital information, and may share misinformation among themselves.
Speaker: Saif M. Mohammad, National Research Council Canada
Speaker: Marco Serafini, University of Massachusetts Amherst
Richard Zanibbi, Director, Document and Pattern Recognition Lab
Rochester Institute of Technology
Speaker: Guoliang Li, Tsinghua University
Abstract:
Camilo Munoz, MMath candidate
David R. Cheriton School of Computer Science
Thanks to the advance in mobile and touch screen devices, handwritten input has gained more popularity among users. When considering mathematical input, however, handwritten math interfaces have to deal with new problems and issues not found in natural language. A popular area of interest that deals with math formulae recognition is math information retrieval (MIR).
Speaker: Spyros Blanas, The Ohio State University
PLEASE NOTE: THIS TALK IS CANCELLED
Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science
Alireza Heidari, PhD candidate
David R. Cheriton School of Computer Science
We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement.
Besat Kassaie, PhD candidate
David R. Cheriton School of Computer Science
Speaker: Ricardo Jimenez-Peris
Abstract: The talk will present the ultra-scalable distributed algorithm to process transactional management and how it has been implemented as part of the LeanXcale database. The talk will go into the details on how ACID properties have been scaled out independently in a composable manner.
Michael Farag, MMath candidate
David R. Cheriton School of Computer Science
Knowledge graphs are considered an important representation that lies between free text on one hand and fully-structured relational data on the other. Knowledge graphs are a backbone of many applications on the Web. With the rise of many large-scale open-domain knowledge graphs like Freebase, DBpedia, and Yago, various applications including document retrieval, question answering, and data integration have been relying on them.
Speaker: Oliver Kennedy, University at Buffalo
James She, Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology
Speaker: Dan Suciu, University of Washington
Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science
Ian Soboroff, Leader, Retrieval Group
National Institute of Standards and Technology
Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science
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.