Organizers:<\/p>\n
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- Artur Czumaj, University of Warwick<\/li>\n
- Milan Vojnovic (opens in new tab)<\/span><\/a>, Microsoft Research<\/li>\n
- Jingren Zhou, Microsoft<\/li>\n<\/ul>\n
Local Arrangements:<\/p>\n
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- Cara Freeman, Microsoft Research<\/li>\n<\/ul>\n
Sponsors:<\/p>\n
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- Microsoft Research Ltd<\/li>\n
- Centre for Discrete Mathematics and its Application (opens in new tab)<\/span><\/a>s, University of Warwick<\/li>\n<\/ul>\n
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![\"banner\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/banner.jpg\")
<\/p>\n\n\n\n\n\n\n\nWe witness a rapid development of the research and technology for efficient processing of big data. There is a surge of commercial and open source platforms for big data analytics, including platforms for querying of massive datasets, batch processing, real-time analytics, streaming computations, iterative computations, graph data processing, and distributed machine learning. There have been some remarkable achievements on the side of designing scalable and efficient algorithms for processing of massive amounts of data, as well as on the side of architecture of systems and infrastructure. The goal of this workshop is to bring together researchers and system architects to discuss and identify the most important and challenging directions to push forward the area of algorithms and systems for big data. The topics of the workshop include but are not limited to computation and storage platforms, querying of massive datasets, sketches, streaming, scaling up distributed machine learning, iterative computations, and large-scale graph processing.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Opens in a new tab<\/span><\/p>\n
Day 1<\/strong><\/p>\n
8:00 – 8:45
\nContinental breakfast<\/p>\n8:45 – 9:00
\nWelcome note<\/p>\n9:00 – 10:00 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nKeynote talk, Gerhard Weikum, Max Planck Institute for Informatics
\nFrom Text to Entities and from Entities to Insight: a Perspective on Unstructured Big Data<\/em><\/p>\n10:00 – 10:30 | Abstract<\/a>
\nAnastasia Ailamaki, EPFL
\nQuerying and Exploring Big Brain Data<\/em><\/p>\n10:30 – 11:00
\nRefreshments<\/p>\nProgramming and Computation Models<\/em><\/p>\n
11:00 – 11:30 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nVolker Markl, TU-Berlin
\nBig Data with Stratosphere<\/em><\/p>\n11:30 – 12:00 | Abstract<\/a>
\nJingren Zhou, Microsoft Corporation
\nSCOPE: Parallel Databases Meet MapReduce<\/em><\/p>\n12:00 – 13:00
\nLunch<\/p>\nAlgorithms, Infrastructure, and Platforms<\/em><\/p>\n
13:00 – 13:30 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nFlavio P. Junqueira, Microsoft Research
\nOnline Data Processing with S4 and Omid<\/em><\/p>\n13:30 – 14:00 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nThomas Karagiannis<\/a>, Microsoft Research
\nPredictable Data Centers<\/em><\/p>\n14:00 – 14:30 | Abstract<\/a>
\nSergei Vassilvitskii, Google
\nFrom Terabytes to Megabytes: Finding the Needle by Shrinking the Haystack<\/em><\/p>\n14:30 – 15:00 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nFrank McSherry, Microsoft Research
\nIncremental, Iterative, and Interactive Computation using Differential Dataflow<\/em><\/p>\n15:00 – 16:00 | See Demos and Posters Tab<\/span><\/span>
\nRefreshments + Demo and Poster Session<\/p>\nLarge-scale Graph Analytics<\/em><\/p>\n
16:00 – 16:30 | Abstracts<\/a> | Slides<\/a>
\nGuy Blelloch, Carnegie Mellon University
\nBig Data on Small Machines<\/em><\/p>\n16:30 – 17:00 | Abstracts<\/a> | Slides<\/a>
\nSudipto Guha, University of Pennsylvania
\nGraphs and Linear Measurements<\/em><\/p>\n17:00 – 17:30 | Abstract<\/a> | Slides<\/a>
\nGeorge Karypis, University of Minnesota
\nPartitioning & Clustering Big Graphs<\/em><\/p>\n17:30 – 18:00 | Abstract<\/a> | Slides<\/a>
\nStefano Leonardi, Sapienza University of Rome
\nOnline Team Formation in Social Networks<\/em><\/p>\n19:00 – 21:00
\nWorkshop reception and dinner at Emmanuel College<\/p>\nDay 2<\/strong><\/p>\n
8:30 – 9:00
\nCoffee and pastries<\/p>\n9:00 – 10:00 | Abstract<\/a>
\nKeynote talk
\nSurajit Chaudhuri<\/a>, Microsoft Research
\nBig Data and Enterprise Analytics<\/em><\/p>\n10:00 – 10:30 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nGraham Cormode, University of Warwick
\nStreaming Verification of Outsourced Computation<\/em><\/p>\n10:30 – 11:00
\nRefreshments + Group photo<\/p>\n11:00 – 12:00 | Panel
\nModerator:
\nGraham Cormode, University of Warwick
\nPanelists:
\nSurajit Chaudhuri<\/a>, Microsoft Research
\nSudipto Guha, University of Pennsylvania
\nSergei Vassilvitskii, Google
\nJingren Zhou, Microsoft Corporation
\nBig Data Analytics: A Happy Marriage of Systems and Theory?<\/em><\/p>\n12:00 – 13:30
\nLunch<\/p>\n13:30 – 14:30 | Abstract<\/a>
\nKeynote talk, Sanjeev Khanna, University of Pennsylvania
\nFast Algorithms for Perfect Matchings in Regular Bipartite Graphs<\/em><\/p>\nLarge-Scale Graph Analytics Cont’d<\/em><\/p>\n
14:30 – 15:00 | Abstract<\/a> | Slides<\/a>
\nAleksandar Madry, EPFL
\nCuts, Trees, and Electrical Flows<\/em><\/p>\n15:00 – 15:30 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nSrikanta Tirthapura, Iowa State University
\nNeighborhood Sampling for Estimating Local Properties on a Graph Stream<\/em><\/p>\n15:30 – 16:00
\nRefreshments<\/p>\nStreaming and Massive Distributed Data<\/em><\/p>\n
16:00 – 16:30 | Abstract<\/a> |\u00a0 Video<\/a>
\nAmit Chakrabarti, Dartmouth College
\nWhat Can’t We Compute on Data Streams?<\/em><\/p>\n16:30 – 17:00 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nMinos Garofalakis, Technical University of Crete
\nQuerying Big, Dynamic, Distributed Data<\/em><\/p>\n17:00 – 17:30 | Abstract<\/a> | Slides<\/a> | Video<\/a>
\nKe Yi, Hong Kong University of Science and Technology
\nMergable Summaries<\/em><\/p>\n17:30 – 18:00
\nClosing sessionOpens in a new tab<\/span><\/p>\n\t
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![\"surajitc\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/surajitc-1.jpg\")
Distinguished Scientist and Managing Director, Microsoft Research<\/em><\/p>\n
- \n\t\t
- Jingren Zhou, Microsoft<\/li>\n<\/ul>\n
![\"sanjeev_pic\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/sanjeev_pic.jpg\")
Professor, University of Pennsylvania<\/em><\/p>\n![\"gerhardweikum\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/gerhardweikum.jpg\")
Scientific Director, Max Planck Institute for Informatics<\/em><\/p>\n![\"surajitc\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/surajitc.jpg\")
Surajit Chaudhuri, Distinguished Scientist and a Managing Director at Microsoft Research, will discuss the key secular trends that characterize the field of Big Data with respect to enterprise analytics. He will describe some of the open challenges for enterprise analytics in the context of Big Data.![\"guha\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/guha.png\")
Guha is an Associate Professor in the Department of Computer and Information Sciences at University of Pennsylvania since Fall 2001. He completed his Ph.D. in 2000 at Stanford University working on approximation algorithms. He is a recipient of the NSF CAREER award in 2007, and the Alfred P. Sloan Foundation fellowship.![\"vassilvitskii\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/vassilvitskii.png\")
Sergei Vassilvitskii is a Research Scientist at Google and an Adjunct Assistant Professor of Computer Science at Columbia University. He received his M.S. and Ph.D. in Computer Science from Stanford University and a B.S. from Cornell University. He works on problems at the intersection of economics, data mining and computer science.![\"jingren\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/jingren.png\")
Jingren Zhou is a Partner Development Manager in the Bing’s Infrastructure Team. Jingren manages a team to develop a cloud-scale distributed computation system, called SCOPE, targeted for massive data analysis over tens of thousands of machines at Microsoft Bing. The SCOPE system combines benefits from both traditional parallel databases and MapReduce execution engines to allow easy programmability and deliver massive scalability and high performance through advanced optimization. The system processes petabytes of data daily for a variety of data analysis and data mining applications, powering Bing and other online services at Microsoft. Before he joined Bing Search in 2008, Jingren was a researcher in the database research group at Microsoft Research. He has published many articles in premier database conferences and journals. Jingren received a Ph.D. in Computer Science from Columbia University.![\"cormode\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/cormode.png\")
Graham Cormode is a professor of Computer Science at the University of Warwick, UK. He works on topics around big data, streaming analytics, privacy and data mining. From 2006-2013, he was a researcher at AT&T Labs, and prior to that he was at Bell Labs![\"bda_2013\"](\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/08\/bda_2013.jpg\")
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