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This week, Kohala, Hawaii hosts the 41st International Conference of Very Large Databases (VLDB 2015), a premier annual international forum for data management and database researchers, vendors, practitioners, application developers and users. As a leader in Database research, Google will have a strong presence at VLDB 2015 with many Googlers publishing work, organizing workshops and presenting demos.

The research Google is presenting at VLDB involves the work of Structured Data teams who are building intelligent and efficient systems to discover, annotate and explore structured data from the Web, surfacing them creatively through Google products (such as structured snippets and table search), as well as engineering efforts that create scalable, reliable, fast and general-purpose infrastructure for large-scale data processing (such as F1, Mesa, and Google Cloud's BigQuery).

If you are attending VLDB 2015, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at VLDB 2015 in the list below (Googlers highlighted in blue).

Google is a Gold Sponsor of VLDB 2015.

Papers:
Keys for Graphs
Wenfei Fan, Zhe Fan, Chao Tian, Xin Luna Dong

In-Memory Performance for Big Data
Goetz Graefe, Haris Volos, Hideaki Kimura, Harumi Kuno, Joseph Tucek, Mark Lillibridge, Alistair Veitch

The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing
Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael Fernández-Moctezuma, Reuven Lax, Sam McVeety, Daniel Mills, Frances Perry, Eric Schmidt, Sam Whittle

Resource Bricolage for Parallel Database Systems
Jiexing Li, Jeffrey Naughton, Rimma Nehme

AsterixDB: A Scalable, Open Source BDMS
Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alex Behm, Vinayak Borkar, Yingyi Bu, Michael Carey, Inci Cetindil, Madhusudan Cheelangi, Khurram Faraaz, Eugenia Gabrielova, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Guangqiang Li, Ji Mahn Ok, Nicola Onose, Pouria Pirzadeh, Vassilis Tsotras, Rares Vernica, Jian Wen, Till Westmann

Knowledge-Based Trust: A Method to Estimate the Trustworthiness of Web Sources
Xin Luna Dong, Evgeniy Gabrilovich, Kevin Murphy, Van Dang, Wilko Horn, Camillo Lugaresi, Shaohua Sun, Wei Zhang

Efficient Evaluation of Object-Centric Exploration Queries for Visualization
You Wu, Boulos Harb, Jun Yang, Cong Yu

Interpretable and Informative Explanations of Outcomes
Kareem El Gebaly, Parag Agrawal, Lukasz Golab, Flip Korn, Divesh Srivastava

Take me to your leader! Online Optimization of Distributed Storage Configurations
Artyom Sharov, Alexander Shraer, Arif Merchant, Murray Stokely

TreeScope: Finding Structural Anomalies In Semi-Structured Data
Shanshan Ying, Flip Korn, Barna Saha, Divesh Srivastava

Workshops:
Workshop on Big-Graphs Online Querying - Big-O(Q) 2015
Workshop co-chair: Cong Yu

3rd International Workshop on In-Memory Data Management and Analytics
Program committee includes: Sandeep Tata

High-Availability at Massive Scale: Building Google's Data Infrastructure for Ads
Invited talk at BIRTE by: Ashish Gupta, Jeff Shute

Demonstrations:
KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing
Xu Chu, John Morcos, Ihab Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang, Yin Ye

Error Diagnosis and Data Profiling with Data X-Ray
Xiaolan Wang, Mary Feng, Yue Wang, Xin Luna Dong, Alexandra Meliou

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In 2009, Google created the PhD Fellowship program to recognize and support outstanding graduate students doing exceptional research in Computer Science and related disciplines. Now in its seventh year, our fellowship programs have collectively supported over 200 graduate students in Australia, China and East Asia, India, North America, Europe and the Middle East who seek to shape and influence the future of technology.

Reflecting our continuing commitment to building mutually beneficial relationships with the academic community, we are excited to announce the 44 students from around the globe who are recipients of the award. We offer our sincere congratulations to Google’s 2015 Class of PhD Fellows!

Australia

  • Bahar Salehi, Natural Language Processing (University of Melbourne)
  • Siqi Liu, Computational Neuroscience (University of Sydney)
  • Qian Ge, Systems (University of New South Wales)

China and East Asia

  • Bo Xin, Artificial Intelligence (Peking University)
  • Xingyu Zeng, Computer Vision (The Chinese University of Hong Kong)
  • Suining He, Mobile Computing (The Hong Kong University of Science and Technology)
  • Zhenzhe Zheng, Mobile Networking (Shanghai Jiao Tong University)
  • Jinpeng Wang, Natural Language Processing (Peking University)
  • Zijia Lin, Search and Information Retrieval (Tsinghua University)
  • Shinae Woo, Networking and Distributed Systems (Korea Advanced Institute of Science and Technology)
  • Jungdam Won, Robotics (Seoul National University)

India

  • Palash Dey, Algorithms (Indian Institute of Science)
  • Avisek Lahiri, Machine Perception (Indian Institute of Technology Kharagpur)
  • Malavika Samak, Programming Languages and Software Engineering (Indian Institute of Science)

Europe and the Middle East

  • Heike Adel, Natural Language Processing (University of Munich)
  • Thang Bui, Speech Technology (University of Cambridge)
  • Victoria Caparrós Cabezas, Distributed Systems (ETH Zurich)
  • Nadav Cohen, Machine Learning (The Hebrew University of Jerusalem)
  • Josip Djolonga, Probabilistic Inference (ETH Zurich)
  • Jakob Julian Engel, Computer Vision (Technische Universität München)
  • Nikola Gvozdiev, Computer Networking (University College London)
  • Felix Hill, Language Understanding (University of Cambridge)
  • Durk Kingma, Deep Learning (University of Amsterdam)
  • Massimo Nicosia, Statistical Natural Language Processing (University of Trento)
  • George Prekas, Operating Systems (École Polytechnique Fédérale de Lausanne)
  • Roman Prutkin, Graph Algorithms (Karlsruhe Institute of Technology)
  • Siva Reddy, Multilingual Semantic Parsing (The University of Edinburgh)
  • Immanuel Trummer, Structured Data Analysis (École Polytechnique Fédérale de Lausanne)
  • Margarita Vald, Security (Tel Aviv University)

North America

  • Waleed Ammar, Natural Language Processing (Carnegie Mellon University)
  • Justin Meza, Systems Reliability (Carnegie Mellon University)
  • Nick Arnosti, Market Algorithms (Stanford University)
  • Osbert Bastani, Programming Languages (Stanford University)
  • Saurabh Gupta, Computer Vision (University of California, Berkeley)
  • Masoud Moshref Javadi, Computer Networking (University of Southern California)
  • Muhammad Naveed, Security (University of Illinois at Urbana-Champaign)
  • Aaron Parks, Mobile Networking (University of Washington)
  • Kyle Rector, Human Computer Interaction (University of Washington)
  • Riley Spahn, Privacy (Columbia University)
  • Yun Teng, Computer Graphics (University of California, Santa Barbara)
  • Carl Vondrick, Machine Perception, (Massachusetts Institute of Technology)
  • Xiaolan Wang, Structured Data (University of Massachusetts Amherst)
  • Tan Zhang, Mobile Systems (University of Wisconsin-Madison)
  • Wojciech Zaremba, Machine Learning (New York University)

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We have just completed another round of the Google Faculty Research Awards, our annual open call for research proposals on Computer Science and related topics, including systems, machine learning, software engineering, security and mobile. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 805 proposals, about the same as last round, covering 48 countries on 6 continents. After expert reviews and committee discussions, we decided to fund 113 projects, with 27% of the funding awarded to universities outside the U.S. The subject areas that received the highest level of support were systems, machine perception, software engineering, and machine learning.

The Faculty Research Awards program plays a critical role in building and maintaining strong collaborations with top research faculty globally. These relationships allow us to keep a pulse on what’s happening in academia in strategic areas, and they help to extend our research capabilities and programs. Faculty also report, through our annual survey, that they and their students benefit from a direct connection to Google as a source of ideas and perspective.

Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is October 15), please visit our website for more information.

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When a small team of software engineers first started working on Flu Trends in 2008, we wanted to explore how real-world phenomena could be modeled using patterns in search queries. Since its launch, Google Flu Trends has provided useful insights and served as one of the early examples for “nowcasting” based on search trends, which is increasingly used in health, economics, and other fields. Over time, we’ve used search signals to create prediction models, updating and improving those models over time as we compared our prediction to real-world cases of flu.

Instead of maintaining our own website going forward, we’re now going to empower institutions who specialize in infectious disease research to use the data to build their own models. Starting this season, we’ll provide Flu and Dengue signal data directly to partners including Columbia University’s Mailman School of Public Health (to update their dashboard), Boston Children’s Hospital/Harvard, and Centers for Disease Control and Prevention (CDC) Influenza Division. We will also continue to make historical Flu and Dengue estimate data available for anyone to see and analyze.

Flu continues to affect millions of people every year, and while it’s still early days for nowcasting and similar tools for understanding the spread of diseases like flu and dengue fever—we’re excited to see what comes next. To download the historical data or learn more about becoming a research partner, please visit the Flu Trends web page.

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Pursuing Google’s mission of organizing the world’s information to make it universally accessible and useful takes an enormous amount of computing and storage. In fact, it requires coordination across a warehouse-scale computer. Ten years ago, we realized that we could not purchase, at any price, a datacenter network that could meet the combination of our scale and speed requirements. So, we set out to build our own datacenter network hardware and software infrastructure. Today, at the ACM SIGCOMM conference, we are presenting a paper with the technical details on five generations of our in-house data center network architecture. This paper presents the technical details behind a talk we presented at Open Network Summit a few months ago.

From relatively humble beginnings, and after a misstep or two, we’ve built and deployed five generations of datacenter network infrastructure. Our latest-generation Jupiter network has improved capacity by more than 100x relative to our first generation network, delivering more than 1 petabit/sec of total bisection bandwidth. This means that each of 100,000 servers can communicate with one another in an arbitrary pattern at 10Gb/s.

Such network performance has been tremendously empowering for Google services. Engineers were liberated from optimizing their code for various levels of bandwidth hierarchy. For example, initially there were painful tradeoffs with careful data locality and placement of servers connected to the same top of rack switch versus correlated failures caused by a single switch failure. A high performance network supporting tens of thousands of servers with flat bandwidth also enabled individual applications to scale far beyond what was otherwise possible and enabled tight coupling among multiple federated services. Finally, we were able to substantially improve the efficiency of our compute and storage infrastructure. As quantified in this recent paper, scheduling a set of jobs over a single larger domain supports much higher utilization than scheduling the same jobs over multiple smaller domains.

Delivering such a network meant we had to solve some fundamental problems in networking. Ten years ago, networking was defined by the interaction of individual hardware elements, e.g., switches, speaking standardized protocols to dynamically learn what the network looks like. Based on this dynamically learned information, switches would set their forwarding behavior. While robust, these protocols targeted deployment environments with perhaps tens of switches communicating between between multiple organizations. Configuring and managing switches in such an environment was manual and error prone. Changes in network state would spread slowly through the network using a high-overhead broadcast protocol. Most challenging of all, the system could not scale to meet our needs.

We adopted a set of principles to organize our networks that is now the primary driver for networking research and industrial innovation, Software Defined Networking (SDN). We observed that we could arrange emerging merchant switch silicon around a Clos topology to scale to the bandwidth requirements of a data center building. The topology of all five generations of our data center networks follow the blueprint below. Unfortunately, this meant that we would potentially require 10,000+ individual switching elements. Even if we could overcome the scalability challenges of existing network protocols, managing and configuring such a vast number of switching elements would be impossible.
So, our next observation was that we knew the exact configuration of the network we were trying to build. Every switch would play a well-defined role in a larger-ensemble. That is, from the perspective of a datacenter building, we wished to build a logical building-scale network. For network management, we built a single configuration for the entire network and pushed the single global configuration to all switches. Each switch would simply pull out its role in the larger whole. Finally, we transformed routing from a pair-wise, fully distributed (but somewhat slow and high overhead) scheme to a logically-centralized scheme under the control of a single dynamically-elected master as illustrated in the figure below from our paper.
Our SIGCOMM paper on Jupiter is one of four Google-authored papers being presented at that conference. Taken together, these papers show the benefits of embedding research within production teams, reflecting both collaborations with PhD students carrying out extended research efforts with Google engineers during internships as well as key insights from deployed production systems:

  • Our work on Bandwidth Enforcer shows how we can allocate wide area bandwidth among tens of thousands of individual applications based on centrally configured policy, substantially improving network utilization while simultaneously isolating services from one another.
  • Condor addresses the challenges of designing data center network topologies. Network designers can specify constraints for data center networks; Condor efficiently generates candidate network designs that meet these constraints, and evaluates these candidates against a variety of target metrics.
  • Congestion control in datacenter networks is challenging because of tiny buffers and very small round trip times. TIMELY shows how to manage datacenter bandwidth allocation while maintaining highly responsive and low latency network roundtrips in the data center.

These efforts reflect the latest in a long series of substantial Google contributions in networking. We are excited about being increasingly open about results of our research work: to solicit feedback on our approach, to influence future research and development directions so that we can benefit from community-wide efforts to improve networking, and to attract the next-generation of great networking thinkers and builders to Google. Our focus on Google Cloud Platform further increases the importance of being open about our infrastructure. Since the same network powering Google infrastructure for a decade is also the underpinnings of our Cloud Platform, all developers can leverage the network to build highly robust, manageable, and globally scalable services.

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USENIX Enigma is a new conference focused on security, privacy and electronic crime through the lens of emerging threats and novel attacks. The goal of this conference is to help industry, academic, and public-sector practitioners better understand the threat landscape. Enigma will have a single track of 30-minute talks that are curated by a panel of experts, featuring strong technical content with practical applications to current and emerging threats.
Google is excited to both sponsor and help USENIX build Enigma, since we share many of its core principles: transparency, openness, and cutting-edge security research. Furthermore, we are proud to provide Enigma with with engineering and design support, as well as volunteer participation in program and steering committees.

The first instantiation of Enigma will be held January 25-27 in San Francisco. You can sign up for more information about the conference or propose a talk through the official conference site at http://enigma.usenix.org

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The 21st ACM conference on Knowledge Discovery and Data Mining (KDD’15), a main venue for academic and industry research in data management, information retrieval, data mining and machine learning, was held last week in Sydney, Australia. In the past several years, Google has been actively participating in KDD, with several Googlers presenting work at the conference in the research and industrial tracks. This year Googlers presented 12 papers at KDD (listed below, with Googlers in blue), all of which are freely available at the ACM Digital Library.

One of these papers, Efficient Algorithms for Public-Private Social Networks, co-authored by Googlers Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni, former Googler intern Alessandro Epasto and research visitor Flavio Chierichetti, was awarded Best Research Paper. The inspiration for this paper comes from studying social networks and the importance of addressing privacy issues in analyzing such networks.

Privacy issues dictate the way information is shared among the members of the social network. In the simplest case, a user can mark some of her friends as private; this would make the connections (edges) between this user and these friends visible only to the user. In a different instantiation of privacy, a user can be a member of a private group; in this case, all the edges among the group members are to be considered private. Thus, each user in the social network has her own view of the link structure of the network. These privacy issues also influence the way in which the network itself can be viewed and processed by algorithms. For example, one cannot use the list of private friends of user X for suggesting potential friends or public news items to another user on the network, but one can use this list for the purpose of suggesting friends for user X.

As a result, enforcing these privacy guarantees translates to solving a different algorithmic problem for each user in the network, and for this reason, developing algorithms that process these social graphs and respect these privacy guarantees can become computationally expensive. In a recent study, Dey et al. crawled a snapshot of 1.4 million New York City Facebook users and reported that 52.6% of them hid their friends list. As more users make a larger portion of their social neighborhoods private, these computational issues become more important.

Motivated by the above, this paper introduces the public-private model of graphs, where each user (node) in the public graph has an associated private graph. In this model, the public graph is visible to everyone, and the private graph at each node is visible only to each specific user. Thus, any given user sees their graph as a union of their private graph and the public graph.

From algorithmic point of view, the paper explores two powerful computational paradigms for efficiently studying large graphs, namely, sketching and sampling, and focuses on some key problems in social networks such as similarity ranking, and clustering. In the sketching model, the paper shows how to efficiently approximate the neighborhood function, which in turn can be used to approximate various notions of centrality scores for each node - such centrality scores like the PageRank score have important applications in ranking and recommender systems. In the sampling model, the paper focuses on all-pair shortest path distances, node similarities, and correlation clustering, and develop algorithms that computes these notions on a given public-private graph and at the same time. The paper also illustrates the effectiveness of this model and the computational efficiency of the algorithms by performing experiments on real-world social networks.

The public-private model is an abstraction that can be used to develop efficient social network algorithms. This work leaves a number of open interesting research directions such as: obtaining efficient algorithms for the densest subgraph/community detection problems, influence maximization, computing other pairwise similarity scores, and most importantly, recommendation systems.

KDD’15 Papers, co-authored by Googlers:

Efficient Algorithms for Public-Private Social Networks (Best Paper Award)
Flavio Chierichetti, Alessandro Epasto, Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling

TimeMachine: Timeline Generation for Knowledge-Base Entities
Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, Wei Zhang

Algorithmic Cartography: Placing Points of Interest and Ads on Maps
Mohammad Mahdian, Okke Schrijvers, Sergei Vassilvitskii

Stream Sampling for Frequency Cap Statistics
Edith Cohen

Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams
Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J.Smola, Le Song

Adaptation Algorithm and Theory Based on Generalized Discrepancy
Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina (now at Google)

Estimating Local Intrinsic Dimensionality
Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle Ken-ichi Kawarabayashi, Michael Nett

Unified and Contrasting Cuts in Multiple Graphs: Application to Medical Imaging Segmentation
Chia-Tung Kuo, Xiang Wang, Peter Walker, Owen Carmichael, Jieping Ye, Ian Davidson

Going In-depth: Finding Longform on the Web
Virginia Smith, Miriam Connor, Isabelle Stanton

Annotating needles in the haystack without looking: Product information extraction from emails
Weinan Zhang, Amr Ahmed, Jie Yang, Vanja Josifovski, Alexander Smola

Focusing on the Long-term: It's Good for Users and Business
Diane Tang, Henning Hohnhold, Deirdre O'Brien