Research Area
Year Published

598 Results

October 1, 2013

Virtual Network Diagnosis as a Service

ACM Symposium on Cloud Computing (SoCC)

Today‚Äôs cloud network platforms allow tenants to construct sophisticated virtual network topologies among their VMs on a shared physical network infrastructure. However, these platforms provide little…

By: Wenfei Wu, Guohui Wang, Aditya Akella, Anees Shaikh
August 27, 2013

Scuba: Diving into Data at Facebook

International Conference on Very Large Data Bases (VLDB)

Facebook takes performance monitoring seriously. Performance issues can impact over one billion users so we track thousands of servers, hundreds of PB of daily network traffic, hundreds of daily code…

By: Lior Abraham, John Allen, Oleksandr Barykin, Vinayak Borkar, Bhuwan Chopra, Ciprian Gerea, Dan Merl, Josh Metzler, David Reiss, Subbu Subramanian, Janet Wiener, Okay Zed
August 26, 2013

XORing Elephants: Novel Erasure Codes for Big Data

International Conference on Very Large Data Bases (VLDB)

Distributed storage systems for large clusters typically use replication to provide reliability. Recently, erasure codes have been used to reduce the large storage overhead of three-replicated systems. Reed-Solomon codes are the standard design choice and their high repair cost is often considered an unavoidable price to pay for high storage efficiency and high reliability.

By: Maheshwaran Sathiamoorthy, Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis, Ramkumar Vadali, Scott Chen, Dhruba Borthakur
August 26, 2013

Unicorn: A System for Searching the Social Graph

International Conference on Very Large Data Bases (VLDB)

Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes.

By: Mike Curtiss, Iain Becker, Tudor Bosman, Sergey Doroshenko, Lucian Adrian Grijincu, Tom Jackson, Sandhya Kunnatur, Soren Lassen, Philip Pronin, Sriram Sankar, Guanghao Shen, Gintaras Woss, Chao Yang, Ning Zhang
August 22, 2013

Reciprocal Hash Tables for Nearest Neighbor Search

AAAI Conference on Artificial Intelligence (AI)

Recent years have witnessed the success of hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually employed to retrieve more desired results from all hit buckets of each table. However, there are rare works studying the unified approach to constructing multiple informative hash tables except the widely used random way.

By: Xianglong Liu, Junfeng He, Bo Lang
August 22, 2013

Weighted Hashing for Fast Large Scale Similarity Search

ACM International Conference on Information and Knowledge Management (CIKM)

Similarity search, or finding approximate nearest neighbors, is an important technique for many applications. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency.

By: Qifan Wang, Dan Zhang, Luo Si
August 11, 2013

Graph Cluster Randomization: Network Exposure to Multiple Universes

ACM Conference on Knowledge Discovery and Data Mining (KDD)

A drawback with A/B testing is that it is poorly suited for experiments involving social interference, when the treatment of individuals spills over to neighboring individuals along an underlying social network. In this work, we propose a novel methodology using graph clustering to analyze average treatment effects under social interference.

By: Johan Ugander, Brian Karrer, Lars Backstrom, Jon Kleinberg
August 11, 2013

MI2LS: Multi-Instance Learning from Multiple Information Sources

ACM Conference on Knowledge Discovery and Data Mining (KDD)

In Multiple Instance Learning (MIL), each entity is normally expressed as a set of instances. Most of the current MIL methods only deal with the case when each instance is represented by one type of f…

By: Dan Zhang, Jingrui He, Richard Lawrence
August 11, 2013

Uncertainty in Online Experiments with Dependent Data: An Evaluation of Bootstrap Methods

ACM Conference on Knowledge Discovery and Data Mining (KDD)

Many online experiments exhibit dependence between users and items. For example, in online advertising, observations that have a user or an ad in common are likely to be associated. Because of this, even in experiments involving millions of subjects, the difference in mean outcomes between control and treatment conditions can have substantial variance. Previous theoretical and simulation results demonstrate that not accounting for this kind of dependence structure can result in confidence intervals that are too narrow, leading to inaccurate hypothesis tests.

By: Eytan Bakshy, Dean Eckles
August 11, 2013

Representing Documents Through Their Readers

ACM Conference on Knowledge Discovery and Data Mining (KDD)

From Twitter to Facebook to Reddit, users have become accustomed to sharing the articles they read with friends or followers on their social networks. While previous work has modeled what these shared stories say about the user who shares them, the converse question remains unexplored: what can we learn about an article from the identities of its likely readers?

By: Khalid El-Arini, Min Xu, Emily Fox, Carlos Guestrin