Peter Bailis

According to our database, Peter Bailis authored at least 113 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Towards Optimal Transaction Scheduling.

Audrey Cheng, Aaron N. Kabcenell, Jason Chan, Xiao Shi, Peter D. Bailis, Natacha Crooks, Ion Stoica

Proc. VLDB Endow., July, 2024

Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design.

Jared Quincy Davis, Boris Hanin, Lingjiao Chen, Peter Bailis, Ion Stoica, Matei Zaharia

CoRR, 2024

Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems.

Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei Zaharia, James Zou

CoRR, 2024

Online Speculative Decoding.

Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Break the Sequential Dependency of LLM Inference Using Lookahead Decoding.

Yichao Fu, Peter Bailis, Ion Stoica, Hao Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Epoxy: ACID Transactions Across Diverse Data Stores.

Peter Kraft, Qian Li, Xinjing Zhou, Peter Bailis, Michael Stonebraker, Xiangyao Yu, Matei Zaharia

Proc. VLDB Endow., 2023

2022

TAOBench: An End-to-End Benchmark for Social Networking Workloads.

Audrey Cheng, Xiao Shi, Aaron N. Kabcenell, Shilpa Lawande, Hamza Qadeer, Jason Chan, Harrison Tin, Ryan Zhao, Peter Bailis, Mahesh Balakrishnan, Nathan Bronson, Natacha Crooks, Ion Stoica

Proc. VLDB Endow., 2022

Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations.

Nirvik Baruah, Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2022

Apiary: A DBMS-Backed Transactional Function-as-a-Service Framework.

Peter Kraft, Qian Li, Kostis Kaffes, Athinagoras Skiadopoulos, Deeptaanshu Kumar, Danny Cho, Jason Li, Robert Redmond, Nathan W. Weckwerth, Brian S. Xia, Peter Bailis, Michael J. Cafarella, Goetz Graefe, Jeremy Kepner, Christos Kozyrakis, Michael Stonebraker, Lalith Suresh, Xiangyao Yu, Matei Zaharia

CoRR, 2022

TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data.

Daniel Kang, John Guibas, Peter D. Bailis, Tatsunori Hashimoto, Matei Zaharia

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Finding Label and Model Errors in Perception Data With Learned Observation Assertions.

Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter D. Bailis, Matei Zaharia

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Data-Parallel Actors: A Programming Model for Scalable Query Serving Systems.

Peter Kraft, Fiodar Kazhamiaka, Peter Bailis, Matei Zaharia

Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation, 2022

VIVA: An End-to-End System for Interactive Video Analytics.

Daniel Kang, Francisco Romero, Peter D. Bailis, Christos Kozyrakis, Matei Zaharia

Proceedings of the 12th Conference on Innovative Data Systems Research, 2022

Similarity Search for Efficient Active Learning and Search of Rare Concepts.

Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

DIFF: a relational interface for large-scale data explanation.

Firas Abuzaid, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Ananthanarayan, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia

VLDB J., 2021

Accelerating Approximate Aggregation Queries with Expensive Predicates.

Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia

Proc. VLDB Endow., 2021

RAMP-TAO: Layering Atomic Transactions on Facebook's Online TAO Data Store.

Audrey Cheng, Xiao Shi, Lu Pan, Anthony Simpson, Neil Wheaton, Shilpa Lawande, Nathan Bronson, Peter Bailis, Natacha Crooks, Ion Stoica

Proc. VLDB Endow., 2021

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates.

Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia

CoRR, 2021

Contracting Wide-area Network Topologies to Solve Flow Problems Quickly.

Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, Peter Bailis

Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, 2021

Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training.

Kai Sheng Tai, Peter Bailis, Gregory Valiant

Proceedings of the 38th International Conference on Machine Learning, 2021

Challenges and Opportunities for Autonomous Vehicle Query Systems.

Fiodar Kazhamiaka, Matei Zaharia, Peter Bailis

Proceedings of the 11th Conference on Innovative Data Systems Research, 2021

2020

Leveraging Organizational Resources to Adapt Models to New Data Modalities.

Sahaana Suri, Abishek Sethi, Girija Narlikar, Neslihan Bulut, Raghuveer Chanda, Sugato Basu, Pradyumna Narayana, Peter Bailis, Christopher Ré, Yemao Zeng

Proc. VLDB Endow., 2020

Approximate Partition Selection for Big-Data Workloads using Summary Statistics.

Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Alexander Levis

Proc. VLDB Endow., 2020

A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference.

Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2020

Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics.

Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2020

Approximate Selection with Guarantees using Proxies.

Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia

Proc. VLDB Endow., 2020

CoopStore: Optimizing Precomputed Summaries for Aggregation.

Edward Gan, Peter Bailis, Moses Charikar

Proc. VLDB Endow., 2020

Winds from Seattle: Database Research Directions.

Peter Bailis, Magda Balazinska, Xin Luna Dong, Juliana Freire, Raghu Ramakrishnan, Michael Stonebraker, Joseph M. Hellerstein

Proc. VLDB Endow., 2020

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data.

Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia

CoRR, 2020

Similarity Search for Efficient Active Learning and Search of Rare Concepts.

Cody Coleman, Edward Chou, Sean Culatana, Peter Bailis, Alexander C. Berg, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz

CoRR, 2020

Chromatic Learning for Sparse Datasets.

Vladimir Feinberg, Peter Bailis

CoRR, 2020

Storyboard: Optimizing Precomputed Summaries for Aggregation.

Edward Gan, Peter Bailis, Moses Charikar

CoRR, 2020

MLPerf Training Benchmark.

Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia

Proceedings of the Third Conference on Machine Learning and Systems, 2020

Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference.

Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia

Proceedings of the Third Conference on Machine Learning and Systems, 2020

Model Assertions for Monitoring and Improving ML Models.

Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia

Proceedings of the Third Conference on Machine Learning and Systems, 2020

Selection via Proxy: Efficient Data Selection for Deep Learning.

Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark.

Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia

ACM SIGOPS Oper. Syst. Rev., 2019

The Seattle Report on Database Research.

Daniel Abadi, Anastasia Ailamaki, David G. Andersen, Peter Bailis, Magdalena Balazinska, Philip A. Bernstein, Peter A. Boncz, Surajit Chaudhuri, Alvin Cheung, AnHai Doan, Luna Dong, Michael J. Franklin, Juliana Freire, Alon Y. Halevy, Joseph M. Hellerstein, Stratos Idreos, Donald Kossmann, Tim Kraska, Sailesh Krishnamurthy, Volker Markl, Sergey Melnik, Tova Milo, C. Mohan, Thomas Neumann, Beng Chin Ooi, Fatma Ozcan, Jignesh M. Patel, Andrew Pavlo, Raluca A. Popa, Raghu Ramakrishnan, Christopher Ré, Michael Stonebraker, Dan Suciu

SIGMOD Rec., 2019

BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics.

Daniel Kang, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2019

MLPerf Training Benchmark.

Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

CoRR, 2019

DROP: A Workload-Aware Optimizer for Dimensionality Reduction.

Sahaana Suri, Peter Bailis

Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, 2019

CrossTrainer: Practical Domain Adaptation with Loss Reweighting.

Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis

Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, 2019

Equivariant Transformer Networks.

Kai Sheng Tai, Peter Bailis, Gregory Valiant

Proceedings of the 36th International Conference on Machine Learning, 2019

Rehashing Kernel Evaluation in High Dimensions.

Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Alexander Levis

Proceedings of the 36th International Conference on Machine Learning, 2019

Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data.

Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant

Proceedings of the 36th International Conference on Machine Learning, 2019

LIT: Learned Intermediate Representation Training for Model Compression.

Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia

Proceedings of the 36th International Conference on Machine Learning, 2019

To Index or Not to Index: Optimizing Exact Maximum Inner Product Search.

Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia

Proceedings of the 35th IEEE International Conference on Data Engineering, 2019

Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine.

Daniel Kang, Peter Bailis, Matei Zaharia

Proceedings of the 9th Biennial Conference on Innovative Data Systems Research, 2019

2018

Multi-datacenter Consistency Properties.

Peter Bailis

Proceedings of the Encyclopedia of Database Systems, Second Edition, 2018

MacroBase: Prioritizing Attention in Fast Data.

Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri

ACM Trans. Database Syst., 2018

Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science.

Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi, Peter Bailis, Philip Alexander Levis, Gregory C. Beroza

Proc. VLDB Endow., 2018

Filter Before You Parse: Faster Analytics on Raw Data with Sparser.

Shoumik Palkar, Firas Abuzaid, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2018

Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries.

Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis

Proc. VLDB Endow., 2018

DIFF: A Relational Interface for Large-Scale Data Explanation.

Firas Abuzaid, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Anathanaraya, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2018

LIT: Block-wise Intermediate Representation Training for Model Compression.

Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia

CoRR, 2018

BlazeIt: Fast Exploratory Video Queries using Neural Networks.

Daniel Kang, Peter Bailis, Matei Zaharia

CoRR, 2018

Research for practice: toward a network of connected things.

Deepak Vasisht, Peter Bailis

Commun. ACM, 2018

Research for practice: cluster scheduling for datacenters.

Malte Schwarzkopf, Peter Bailis

Commun. ACM, 2018

Research for practice: knowledge base construction in the machine-learning era.

Alexander Ratner, Christopher Ré, Peter Bailis

Commun. ACM, 2018

Research for practice: private online communication; highlights in systems verification.

Albert Kwon, James R. Wilcox, Peter Bailis

Commun. ACM, 2018

Research for practice: prediction-serving systems.

Daniel Crankshaw, Joseph Gonzalez, Peter Bailis

Commun. ACM, 2018

Research for practice: FPGAs in datacenters.

Gustavo Alonso, Peter Bailis

Commun. ACM, 2018

Sketching Linear Classifiers over Data Streams.

Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant

Proceedings of the 2018 International Conference on Management of Data, 2018

2017

Research for Practice: Technology for UnderservedCommunities; Personal Fabrication.

Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch, Peter Bailis

ACM Queue, 2017

Research for Practice: Tracing and Debugging Distributed Systems; Programming by Examples.

Peter Alvaro, Sumit Gulwani, Peter Bailis

ACM Queue, 2017

ASAP: Prioritizing Attention via Time Series Smoothing.

Kexin Rong, Peter Bailis

Proc. VLDB Endow., 2017

NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale.

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

Proc. VLDB Endow., 2017

Finding Heavily-Weighted Features in Data Streams.

Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant

CoRR, 2017

DROP: Dimensionality Reduction Optimization for Time Series.

Sahaana Suri, Peter Bailis

CoRR, 2017

There and Back Again: A General Approach to Learning Sparse Models.

Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant

CoRR, 2017

ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization.

Kexin Rong, Peter Bailis

CoRR, 2017

Optimizing Deep CNN-Based Queries over Video Streams at Scale.

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

CoRR, 2017

Infrastructure for Usable Machine Learning: The Stanford DAWN Project.

Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia

CoRR, 2017

SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations.

Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia

CoRR, 2017

Research for practice: vigorous public debates in academic computer science.

John Regehr, Peter Bailis

Commun. ACM, 2017

Research for practice: technology for underserved communities; personal fabrication.

Peter Bailis, Tawanna Dillahunt, Stefanie Mueller, Patrick Baudisch

Commun. ACM, 2017

ACIDRain: Concurrency-Related Attacks on Database-Backed Web Applications.

Todd Warszawski, Peter Bailis

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Scalable Kernel Density Classification via Threshold-Based Pruning.

Edward Gan, Peter Bailis

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Demonstration: MacroBase, A Fast Data Analysis Engine.

Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

MacroBase: Prioritizing Attention in Fast Data.

Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Prioritizing Attention in Analytic Monitoring.

Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri

Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, 2017

2016

Scalable Atomic Visibility with RAMP Transactions.

Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

ACM Trans. Database Syst., 2016

Research for Practice: Web Security and Mobile Web Computing.

Jean Yang, Vijay Janapa Reddi, Yuhao Zhu, Peter Bailis

ACM Queue, 2016

Research for Practice: Distributed Transactions and Networks as Physical Sensors.

Peter Bailis, Irene Zhang, Fadel Adib

ACM Queue, 2016

Introducing Research for Practice.

Peter Bailis, Justine Sherry, Simon Peter

ACM Queue, 2016

Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning.

Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han

ACM Queue, 2016

Research for Practice: Distributed Consensus and Implications of NVM on Database Management Systems.

Peter Bailis, Camille Fournier, Joy Arulraj, Andy Pavlo

ACM Queue, 2016

MacroBase: Analytic Monitoring for the Internet of Things.

Peter Bailis, Deepak Narayanan, Samuel Madden

CoRR, 2016

Research for practice: distributed consensus and implications of NVM on database management systems.

Peter Bailis, Joy Arulraj, Andrew Pavlo

Commun. ACM, 2016

2015

Coordination Avoidance in Distributed Databases.

Peter Bailis

PhD thesis, 2015

Asynchronous Complex Analytics in a Distributed Dataflow Architecture.

Joseph E. Gonzalez, Peter Bailis, Michael I. Jordan, Michael J. Franklin, Joseph M. Hellerstein, Ali Ghodsi, Ion Stoica

CoRR, 2015

Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity.

Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31, 2015

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox.

Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan

Proceedings of the Seventh Biennial Conference on Innovative Data Systems Research, 2015

The Case for Invariant-Based Concurrency Control.

Peter Bailis

Proceedings of the Seventh Biennial Conference on Innovative Data Systems Research, 2015

2014

Coordination Avoidance in Database Systems.

Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

Proc. VLDB Endow., 2014

Coordination-Avoiding Database Systems.

Peter Bailis, Alan D. Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

CoRR, 2014

Quantifying eventual consistency with PBS.

Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica

Commun. ACM, 2014

The network is reliable.

Peter Bailis, Kyle Kingsbury

Commun. ACM, 2014

2013

Highly Available Transactions: Virtues and Limitations.

Peter Bailis, Aaron Davidson, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

Proc. VLDB Endow., 2013

HAT, not CAP: Highly Available Transactions

Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

CoRR, 2013

Eventual consistency today: limitations, extensions, and beyond.

Peter Bailis, Ali Ghodsi

Commun. ACM, 2013

PBS at work: advancing data management with consistency metrics.

Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2013

Bolt-on causal consistency.

Peter Bailis, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2013

HAT, Not CAP: Towards Highly Available Transactions.

Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

Proceedings of the 14th Workshop on Hot Topics in Operating Systems, 2013

Consistency without borders.

Peter Alvaro, Peter Bailis, Neil Conway, Joseph M. Hellerstein

Proceedings of the ACM Symposium on Cloud Computing, SOCC '13, 2013

HAT, not CAP: Highly Available Transactions for Everybody.

Peter Bailis

Proceedings of the Sixth Biennial Conference on Innovative Data Systems Research, 2013

2012

TinyToCS Volume 1 Chairs' Note.

Peter Bailis, Justine Sherry

Tiny Trans. Comput. Sci., 2012

Probabilistically Bounded Staleness for Practical Partial Quorums.

Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica

Proc. VLDB Endow., 2012

The potential dangers of causal consistency and an explicit solution.

Peter Bailis, Alan D. Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

Proceedings of the ACM Symposium on Cloud Computing, SOCC '12, 2012

2011

Programming micro-aerial vehicle swarms with karma.

Karthik Dantu, Bryan Kate, Jason Waterman, Peter Bailis, Matt Welsh

Proceedings of the 9th International Conference on Embedded Networked Sensor Systems, 2011

Dimetrodon: processor-level preventive thermal management via idle cycle injection.

Peter Bailis, Vijay Janapa Reddi, Sanjay Gandhi, David M. Brooks, Margo I. Seltzer

Proceedings of the 48th Design Automation Conference, 2011

2010

Positional Communication and Private Information in Honeybee Foraging Models.

Peter Bailis, Radhika Nagpal, Justin Werfel

Proceedings of the Swarm Intelligence - 7th International Conference, 2010