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ORCIDTim Kraska
Person information
- affiliation: MIT Cambridge, MA, USA
- affiliation: Brown University, Providence, RI, USA
- affiliation: ETH Zurich, Switzerland
- award: VLDB Early Career Research Contribution Award 2018
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2020 – today
- 2024
[j67]Geoffrey X. Yu, Ziniu Wu, Ferdinand Kossmann, Tianyu Li, Markos Markakis, Amadou Latyr Ngom, Samuel Madden, Tim Kraska:
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD. Proc. VLDB Endow. 17(11): 3629-3643 (2024)- [j66]Alexander van Renen, Dominik Horn, Pascal Pfeil, Kapil Vaidya, Wenjian Dong, Murali Narayanaswamy, Zhengchun Liu, Gaurav Saxena, Andreas Kipf, Tim Kraska:
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet. Proc. VLDB Endow. 17(11): 3694-3706 (2024) - [j65]Bradley Barnhart, Marc Brooker, Daniil Chinenkov, Tony Hooper, Jihoun Im, Prakash Chandra Jha, Tim Kraska, Ashok Kurakula, Alexey Kuznetsov, Grant Mcalister, Arjun Muthukrishnan, Aravinthan Narayanan, Douglas Terry, Bhuvan Urgaonkar, Jiaming Yan:
Resource Management in Aurora Serverless. Proc. VLDB Endow. 17(12): 4038-4050 (2024) - [j64]Samuel Madden, Michael J. Cafarella, Michael J. Franklin, Tim Kraska:
Databases Unbound: Querying All of the World's Bytes with AI. Proc. VLDB Endow. 17(12): 4546-4554 (2024) - [c103]Amadou Latyr Ngom
, Tim Kraska:
Mallet: SQL Dialect Translation with LLM Rule Generation. aiDM@SIGMOD 2024: 3:1-3:5 - [c102]Vikramank Y. Singh, Kapil Vaidya, Vinayshekhar Bannihatti Kumar, Sopan Khosla, Balakrishnan Narayanaswamy, Rashmi Gangadharaiah, Tim Kraska:
Panda: Performance Debugging for Databases using LLM Agents. CIDR 2024 - [c101]Vikramank Y. Singh, Zhao Song, Balakrishnan (Murali) Narayanaswamy, Kapil Eknath Vaidya, Tim Kraska:
Vista: Machine Learning based Database Performance Troubleshooting Framework in Amazon RDS. SoCC 2024: 83-98 - [c100]Yanlei Diao, Dominik Horn, Andreas Kipf, Oleksandr Shchur, Ines Benito, Wenjian Dong, Davide Pagano, Pascal Pfeil, Vikram Nathan, Balakrishnan Narayanaswamy, Tim Kraska:
Forecasting Algorithms for Intelligent Resource Scaling: An Experimental Analysis. SoCC 2024: 126-143 - [c99]Jialin Ding, Matt Abrams, Sanghita Bandyopadhyay, Luciano Di Palma, Yanzhu Ji, Davide Pagano, Gopal Paliwal, Panos Parchas, Pascal Pfeil, Orestis Polychroniou, Gaurav Saxena, Aamer Shah, Amina Voloder, Sherry Xiao, Davis Zhang, Tim Kraska:
Automated Multidimensional Data Layouts in Amazon Redshift. SIGMOD Conference Companion 2024: 55-67 - [c98]Vikram Nathan, Vikramank Y. Singh, Zhengchun Liu, Mohammad Rahman, Andreas Kipf, Dominik Horn, Davide Pagano, Gaurav Saxena, Balakrishnan Narayanaswamy, Tim Kraska:
Intelligent Scaling in Amazon Redshift. SIGMOD Conference Companion 2024: 269-279 - [c97]Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, Tim Kraska:
Stage: Query Execution Time Prediction in Amazon Redshift. SIGMOD Conference Companion 2024: 280-294 - [c96]Tobias Schmidt, Andreas Kipf, Dominik Horn, Gaurav Saxena, Tim Kraska:
Predicate Caching: Query-Driven Secondary Indexing for Cloud Data Warehouses. SIGMOD Conference Companion 2024: 347-359 - [i69]Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, Tim Kraska:
Stage: Query Execution Time Prediction in Amazon Redshift. CoRR abs/2403.02286 (2024) - [i68]Wenqi Jiang, Shuai Zhang, Boran Han, Jie Wang, Bernie Wang, Tim Kraska:
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design. CoRR abs/2403.05676 (2024) - [i67]Chunwei Liu, Matthew Russo, Michael J. Cafarella, Lei Cao, Peter Baile Chen, Zui Chen, Michael J. Franklin, Tim Kraska, Samuel Madden, Gerardo Vitagliano:
A Declarative System for Optimizing AI Workloads. CoRR abs/2405.14696 (2024) - [i66]Geoffrey X. Yu, Ziniu Wu, Ferdi Kossmann, Tianyu Li, Markos Markakis, Amadou Ngom, Samuel Madden, Tim Kraska:
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD - Extended Version. CoRR abs/2407.15363 (2024) - 2023
- [j63]Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh, Tim Kraska, Samuel Madden:
FactorJoin: A New Cardinality Estimation Framework for Join Queries. Proc. ACM Manag. Data 1(1): 41:1-41:27 (2023) - [j62]Parimarjan Negi, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, Mohammad Alizadeh:
Robust Query Driven Cardinality Estimation under Changing Workloads. Proc. VLDB Endow. 16(6): 1520-1533 (2023) - [j61]Ibrahim Sabek, Tim Kraska:
The Case for Learned In-Memory Joins. Proc. VLDB Endow. 16(7): 1749-1762 (2023) - [j60]Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul, Lei Cao, Tim Kraska, Sam Madden:
Extract-Transform-Load for Video Streams. Proc. VLDB Endow. 16(9): 2302-2315 (2023) - [j59]Tim Kraska, Tianyu Li, Samuel Madden, Markos Markakis, Amadou Ngom, Ziniu Wu, Geoffrey X. Yu:
Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes. Proc. VLDB Endow. 16(11): 3293-3301 (2023) - [j58]Tim Kraska:
Technical Perspective for Sherman: A Write-Optimized Distributed B+Tree Index on Disaggregated Memory. SIGMOD Rec. 52(1): 44 (2023) - [c95]Parimarjan Negi, Laurent Bindschaedler, Mohammad Alizadeh, Tim Kraska, Jyoti Leeka, Anja Gruenheid, Matteo Interlandi:
Unshackling Database Benchmarking from Synthetic Workloads. ICDE 2023: 3659-3662 - [c94]Gaurav Saxena, Mohammad Rahman, Naresh Chainani, Chunbin Lin, George Caragea, Fahim Chowdhury, Ryan Marcus, Tim Kraska, Ippokratis Pandis, Balakrishnan (Murali) Narayanaswamy:
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift. SIGMOD Conference Companion 2023: 225-237 - [c93]Xi Lyu, Andreas Kipf, Pascal Pfeil, Dominik Horn, Jana Giceva, Tim Kraska:
CorBit: Leveraging Correlations for Compressing Bitmap Indexes. VLDB Workshops 2023 - [c92]Leonhard F. Spiegelberg, Tim Kraska, Malte Schwarzkopf:
Hyperspecialized Compilation for Serverless Data Analytics. VLDB Workshops 2023 - [i65]Ani Kristo, Tim Kraska:
Parallel External Sorting of ASCII Records Using Learned Models. CoRR abs/2305.05671 (2023) - [i64]Zui Chen, Lei Cao, Sam Madden, Ju Fan, Nan Tang, Zihui Gu, Zeyuan Shang, Chunwei Liu, Michael J. Cafarella, Tim Kraska:
SEED: Simple, Efficient, and Effective Data Management via Large Language Models. CoRR abs/2310.00749 (2023) - [i63]Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul, Lei Cao, Tim Kraska, Samuel Madden:
Extract-Transform-Load for Video Streams. CoRR abs/2310.04830 (2023) - 2022
- [j57]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:
The Seattle report on database research. Commun. ACM 65(8): 72-79 (2022) - [j56]Kapil Vaidya, Tim Kraska, Subarna Chatterjee, Eric R. Knorr, Michael Mitzenmacher, Stratos Idreos:
SNARF: A Learning-Enhanced Range Filter. Proc. VLDB Endow. 15(8): 1632-1644 (2022) - [j55]Jialin Ding, Ryan Marcus, Andreas Kipf, Vikram Nathan, Aniruddha Nrusimha, Kapil Vaidya, Alexander van Renen, Tim Kraska:
SageDB: An Instance-Optimized Data Analytics System. Proc. VLDB Endow. 15(13): 4062-4078 (2022) - [j54]Geoffrey X. Yu, Markos Markakis, Andreas Kipf, Per-Åke Larson, Umar Farooq Minhas, Tim Kraska:
TreeLine: An Update-In-Place Key-Value Store for Modern Storage. Proc. VLDB Endow. 16(1): 99-112 (2022) - [j53]Ibrahim Sabek, Kapil Vaidya, Dominik Horn, Andreas Kipf, Michael Mitzenmacher, Tim Kraska:
Can Learned Models Replace Hash Functions? Proc. VLDB Endow. 16(3): 532-545 (2022) - [j52]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Making Learned Query Optimization Practical. SIGMOD Rec. 51(1): 6-13 (2022) - [c91]Samuel Madden, Jialin Ding, Tim Kraska, Sivaprasad Sudhir, David E. Cohen, Timothy G. Mattson, Nesime Tatbul:
Self-Organizing Data Containers. CIDR 2022 - [c90]Oscar R. Moll, Favyen Bastani, Sam Madden, Mike Stonebraker, Vijay Gadepally, Tim Kraska:
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling. ICDE 2022: 2956-2968 - [c89]Oscar R. Moll, Favyen Bastani, Sam Madden, Mike Stonebraker, Vijay Gadepally, Tim Kraska:
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling. ICDE 2022: 3065-3077 - [c88]Andreas Kipf, Dominik Horn, Pascal Pfeil, Ryan Marcus, Tim Kraska:
LSI: a learned secondary index structure. aiDM@SIGMOD 2022: 4:1-4:5 - [c87]Ibrahim Sabek, Tenzin Samten Ukyab, Tim Kraska:
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems. SIGMOD Conference 2022: 1228-1242 - [e5]El Kindi Rezig, Vijay Gadepally, Timothy G. Mattson, Michael Stonebraker, Tim Kraska, Jun Kong, Gang Luo, Dejun Teng, Fusheng Wang:
Heterogeneous Data Management, Polystores, and Analytics for Healthcare - VLDB Workshops, Poly 2022 and DMAH 2022, Virtual Event, September 9, 2022, Revised Selected Papers. Lecture Notes in Computer Science 13814, Springer 2022, ISBN 978-3-031-23904-5 [contents] - [i62]Andreas Kipf, Dominik Horn, Pascal Pfeil, Ryan Marcus, Tim Kraska:
LSI: A Learned Secondary Index Structure. CoRR abs/2205.05769 (2022) - [i61]Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh, Tim Kraska, Samuel Madden:
FactorJoin: A New Cardinality Estimation Framework for Join Queries. CoRR abs/2212.05526 (2022) - 2021
- [j51]Tim Kraska, Umar Farooq Minhas, Thomas Neumann, Olga Papaemmanouil, Jignesh M. Patel, Christopher Ré, Michael Stonebraker:
ML-In-Databases: Assessment and Prognosis. IEEE Data Eng. Bull. 44(1): 3-10 (2021) - [j50]Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Flow-Loss: Learning Cardinality Estimates That Matter. Proc. VLDB Endow. 14(11): 2019-2032 (2021) - [j49]Zeyuan Shang, Emanuel Zgraggen, Benedetto Buratti, Philipp Eichmann, Navid Karimeddiny, Charlie Meyer, Wesley Runnels, Tim Kraska:
Davos: A System for Interactive Data-Driven Decision Making. Proc. VLDB Endow. 14(12): 2893-2905 (2021) - [j48]Tim Kraska:
Towards instance-optimized data systems. Proc. VLDB Endow. 14(12): 3222-3232 (2021) - [j47]Athinagoras Skiadopoulos, Qian Li, Peter Kraft, Kostis Kaffes, Daniel Hong, Shana Mathew, David Bestor, Michael J. Cafarella, Vijay Gadepally, Goetz Graefe, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Lalith Suresh, Matei Zaharia:
DBOS: A DBMS-oriented Operating System. Proc. VLDB Endow. 15(1): 21-30 (2021) - [j46]Erfan Zamanian, Julian Shun, Carsten Binnig, Tim Kraska:
Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks. SIGMOD Rec. 50(1): 15-22 (2021) - [c86]Laurent Bindschaedler, Andreas Kipf, Tim Kraska, Ryan Marcus, Umar Farooq Minhas:
Towards a Benchmark for Learned Systems. ICDE Workshops 2021: 127-133 - [c85]Kapil Vaidya, Eric Knorr, Michael Mitzenmacher, Tim Kraska:
Partitioned Learned Bloom Filters. ICLR 2021 - [c84]Lujing Cen, Andreas Kipf, Ryan Marcus, Tim Kraska:
LEA: A Learned Encoding Advisor for Column Stores. aiDM@SIGMOD 2021: 32-35 - [c83]Jialin Ding, Umar Farooq Minhas, Badrish Chandramouli, Chi Wang, Yinan Li, Ying Li, Donald Kossmann, Johannes Gehrke, Tim Kraska:
Instance-Optimized Data Layouts for Cloud Analytics Workloads. SIGMOD Conference 2021: 418-431 - [c82]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Making Learned Query Optimization Practical. SIGMOD Conference 2021: 1275-1288 - [c81]Leonhard F. Spiegelberg, Rahul Yesantharao, Malte Schwarzkopf, Tim Kraska:
Tuplex: Data Science in Python at Native Code Speed. SIGMOD Conference 2021: 1718-1731 - [c80]Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc T. Friedman, Alekh Jindal:
Steering Query Optimizers: A Practical Take on Big Data Workloads. SIGMOD Conference 2021: 2557-2569 - [c79]Tim Kraska:
Living in a Candy Store - from being a PhD Student to Working as a Faculty Member on ML for Systems. PhD@VLDB 2021 - [e4]Vijay Gadepally, Timothy G. Mattson, Michael Stonebraker, Tim Kraska, Fusheng Wang, Gang Luo, Jun Kong, Alevtina Dubovitskaya:
Heterogeneous Data Management, Polystores, and Analytics for Healthcare - VLDB Workshops, Poly 2020 and DMAH 2020, Virtual Event, August 31 and September 4, 2020, Revised Selected Papers. Lecture Notes in Computer Science 12633, Springer 2021, ISBN 978-3-030-71054-5 [contents] - [e3]El Kindi Rezig, Vijay Gadepally, Timothy G. Mattson, Michael Stonebraker, Tim Kraska, Fusheng Wang, Gang Luo, Jun Kong, Alevtina Dubovitskaya:
Heterogeneous Data Management, Polystores, and Analytics for Healthcare - VLDB Workshops, Poly 2021 and DMAH 2021, Virtual Event, August 20, 2021, Revised Selected Papers. Lecture Notes in Computer Science 12921, Springer 2021, ISBN 978-3-030-93662-4 [contents] - [i60]Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Flow-Loss: Learning Cardinality Estimates That Matter. CoRR abs/2101.04964 (2021) - [i59]Songtao He, Favyen Bastani, Mohammad Alizadeh, Hari Balakrishnan, Michael J. Cafarella, Tim Kraska, Sam Madden:
TagMe: GPS-Assisted Automatic Object Annotation in Videos. CoRR abs/2103.13428 (2021) - [i58]Favyen Bastani, Songtao He, Ziwen Jiang, Osbert Bastani, Michael J. Cafarella, Tim Kraska, Sam Madden:
SkyQuery: An Aerial Drone Video Sensing Platform. CoRR abs/2103.14699 (2021) - [i57]Lujing Cen, Andreas Kipf, Ryan Marcus, Tim Kraska:
LEA: A Learned Encoding Advisor for Column Stores. CoRR abs/2105.08830 (2021) - [i56]Ibrahim Sabek, Kapil Vaidya, Dominik Horn, Andreas Kipf, Tim Kraska:
When Are Learned Models Better Than Hash Functions? CoRR abs/2107.01464 (2021) - [i55]Ani Kristo, Kapil Vaidya, Tim Kraska:
Defeating duplicates: A re-design of the LearnedSort algorithm. CoRR abs/2107.03290 (2021) - [i54]Mihail Stoian, Andreas Kipf, Ryan Marcus, Tim Kraska:
PLEX: Towards Practical Learned Indexing. CoRR abs/2108.05117 (2021) - [i53]Ibrahim Sabek, Tim Kraska:
The Case for Learned In-Memory Joins. CoRR abs/2111.08824 (2021) - [i52]Benjamin Spector, Andreas Kipf, Kapil Vaidya, Chi Wang, Umar Farooq Minhas, Tim Kraska:
Bounding the Last Mile: Efficient Learned String Indexing. CoRR abs/2111.14905 (2021) - 2020
- [j45]Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David R. Karger:
ARDA: Automatic Relational Data Augmentation for Machine Learning. Proc. VLDB Endow. 13(9): 1373-1387 (2020) - [j44]Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, Tim Kraska:
Benchmarking Learned Indexes. Proc. VLDB Endow. 14(1): 1-13 (2020) - [j43]Jialin Ding, Vikram Nathan, Mohammad Alizadeh, Tim Kraska:
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads. Proc. VLDB Endow. 14(2): 74-86 (2020) - [j42]Michael Stonebraker, Timothy G. Mattson, Tim Kraska, Vijay Gadepally:
Poly'19 Workshop Summary: GDPR. SIGMOD Rec. 49(3): 55-58 (2020) - [j41]Yeounoh Chung, Tim Kraska, Neoklis Polyzotis, Ki Hyun Tae, Steven Euijong Whang:
Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach. IEEE Trans. Knowl. Data Eng. 32(12): 2284-2296 (2020) - [c78]Michael J. Cafarella, David J. DeWitt, Vijay Gadepally, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Matei Zaharia:
A Polystore Based Database Operating System (DBOS). Poly/DMAH@VLDB 2020: 3-24 - [c77]Gennady L. Andrienko, Natalia V. Andrienko, Steven Mark Drucker, Jean-Daniel Fekete, Danyel Fisher, Stratos Idreos, Tim Kraska, Guoliang Li, Kwan-Liu Ma, Jock D. Mackinlay, Antti Oulasvirta, Tobias Schreck, Heidrun Schumann, Michael Stonebraker, David Auber, Nikos Bikakis, Panos K. Chrysanthis, George Papastefanatos, Mohamed A. Sharaf:
Big Data Visualization and Analytics: Future Research Challenges and Emerging Applications. EDBT/ICDT Workshops 2020 - [c76]Jeremy Kepner, Andreas Kipf, Darren Engwirda, Navin Vembar, Michael Jones, Lauren Milechin, Vijay Gadepally, Chris Hill, Tim Kraska, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell, Michael Houle, Andrew C. Kirby, Anna Klein, Julie Mullen, Andrew Prout, Albert Reuther, Antonio Rosa, Sid Samsi, Charles Yee, Peter Michaleas:
Fast Mapping onto Census Blocks. HPEC 2020: 1-8 - [c75]Parimarjan Negi, Ryan Marcus, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Cost-Guided Cardinality Estimation: Focus Where it Matters. ICDE Workshops 2020: 154-157 - [c74]Andrew Crotty, Alex Galakatos, Tim Kraska:
Getting Swole: Generating Access-Aware Code with Predicate Pullups. ICDE 2020: 1273-1284 - [c73]Songtao He, Favyen Bastani, Arjun Balasingam, Karthik Gopalakrishnan, Ziwen Jiang, Mohammad Alizadeh, Hari Balakrishnan, Michael J. Cafarella, Tim Kraska, Sam Madden:
BeeCluster: drone orchestration via predictive optimization. MobiSys 2020: 299-311 - [c72]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned garbage collection. MAPL@PLDI 2020: 38-44 - [c71]Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann:
RadixSpline: a single-pass learned index. aiDM@SIGMOD 2020: 5:1-5:5 - [c70]Matthias Jasny, Tobias Ziegler, Tim Kraska, Uwe Röhm, Carsten Binnig:
DB4ML - An In-Memory Database Kernel with Machine Learning Support. SIGMOD Conference 2020: 159-173 - [c69]Erfan Zamanian, Julian Shun, Carsten Binnig, Tim Kraska:
Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks. SIGMOD Conference 2020: 511-526 - [c68]Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Yinan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David B. Lomet, Tim Kraska:
ALEX: An Updatable Adaptive Learned Index. SIGMOD Conference 2020: 969-984 - [c67]Vikram Nathan, Jialin Ding, Mohammad Alizadeh, Tim Kraska:
Learning Multi-Dimensional Indexes. SIGMOD Conference 2020: 985-1000 - [c66]Ani Kristo, Kapil Vaidya, Ugur Çetintemel, Sanchit Misra, Tim Kraska:
The Case for a Learned Sorting Algorithm. SIGMOD Conference 2020: 1001-1016 - [c65]Philipp Eichmann, Emanuel Zgraggen, Carsten Binnig, Tim Kraska:
IDEBench: A Benchmark for Interactive Data Exploration. SIGMOD Conference 2020: 1555-1569 - [c64]Favyen Bastani, Songtao He, Arjun Balasingam, Karthik Gopalakrishnan, Mohammad Alizadeh, Hari Balakrishnan, Michael J. Cafarella, Tim Kraska, Sam Madden:
MIRIS: Fast Object Track Queries in Video. SIGMOD Conference 2020: 1907-1921 - [c63]Ryan Marcus, Emily Zhang, Tim Kraska:
CDFShop: Exploring and Optimizing Learned Index Structures. SIGMOD Conference 2020: 2789-2792 - [i51]Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David R. Karger:
ARDA: Automatic Relational Data Augmentation for Machine Learning. CoRR abs/2003.09758 (2020) - [i50]Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich:
Context-Aware Parse Trees. CoRR abs/2003.11118 (2020) - [i49]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Learning to Steer Query Optimizers. CoRR abs/2004.03814 (2020) - [i48]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned Garbage Collection. CoRR abs/2004.13301 (2020) - [i47]Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, Thomas Neumann:
RadixSpline: A Single-Pass Learned Index. CoRR abs/2004.14541 (2020) - [i46]Jeremy Kepner, Darren Engwirda, Vijay Gadepally, Chris Hill, Tim Kraska, Michael Jones, Andreas Kipf, Lauren Milechin, Navin Vembar:
Fast Mapping onto Census Blocks. CoRR abs/2005.03156 (2020) - [i45]Oscar R. Moll, Favyen Bastani, Sam Madden, Mike Stonebraker, Vijay Gadepally, Tim Kraska:
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling. CoRR abs/2005.09141 (2020) - [i44]Kapil Vaidya, Eric Knorr, Tim Kraska, Michael Mitzenmacher:
Partitioned Learned Bloom Filter. CoRR abs/2006.03176 (2020) - [i43]Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, Timothy G. Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich:
MISIM: An End-to-End Neural Code Similarity System. CoRR abs/2006.05265 (2020) - [i42]Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, Tim Kraska:
Benchmarking Learned Indexes. CoRR abs/2006.12804 (2020) - [i41]Jialin Ding, Vikram Nathan, Mohammad Alizadeh, Tim Kraska:
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads. CoRR abs/2006.13282 (2020) - [i40]Michael J. Cafarella, David J. DeWitt, Vijay Gadepally, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Matei Zaharia:
DBOS: A Proposal for a Data-Centric Operating System. CoRR abs/2007.11112 (2020) - [i39]Vikram Nathan, Jialin Ding, Tim Kraska, Mohammad Alizadeh:
Cortex: Harnessing Correlations to Boost Query Performance. CoRR abs/2012.06683 (2020) - [i38]Hussam Abu-Libdeh, Deniz Altinbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou Li, Andy Ly, Christopher Olston:
Learned Indexes for a Google-scale Disk-based Database. CoRR abs/2012.12501 (2020)
2010 – 2019
- 2019
- [j40]Erfan Zamanian, Xiangyao Yu, Michael Stonebraker, Tim Kraska:
Rethinking Database High Availability with RDMA Networks. Proc. VLDB Endow. 12(11): 1637-1650 (2019) - [j39]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul:
Neo: A Learned Query Optimizer. Proc. VLDB Endow. 12(11): 1705-1718 (2019) - [j38]Leonhard F. Spiegelberg, Tim Kraska:
Tuplex: Robust, Efficient Analytics When Python Rules. Proc. VLDB Endow. 12(12): 1958-1961 (2019) - [j37]Junjay Tan, Thanaa M. Ghanem, Matthew Perron, Xiangyao Yu, Michael Stonebraker, David J. DeWitt, Marco Serafini, Ashraf Aboulnaga, Tim Kraska:
Choosing A Cloud DBMS: Architectures and Tradeoffs. Proc. VLDB Endow. 12(12): 2170-2182 (2019) - [j36]Anastasia Ailamaki, Periklis Chrysogelos, Amol Deshpande, Tim Kraska:
The SIGMOD 2019 Research Track Reviewing System. SIGMOD Rec. 48(2): 47-54 (2019) - [j35]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:
The Seattle Report on Database Research. SIGMOD Rec. 48(4): 44-53 (2019) - [c62]Kevin Zeng Hu, Michiel A. Bakker, Stephen Li, Tim Kraska, César A. Hidalgo:
VizML: A Machine Learning Approach to Visualization Recommendation. CHI 2019: 128 - [c61]Kevin Zeng Hu, Snehalkumar (Neil) S. Gaikwad, Madelon Hulsebos, Michiel A. Bakker, Emanuel Zgraggen, César A. Hidalgo, Tim Kraska, Guoliang Li, Arvind Satyanarayan, Çagatay Demiralp:
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository. CHI 2019: 662 - [c60]Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, Vikram Nathan:
SageDB: A Learned Database System. CIDR 2019 - [c59]Lorenzo De Stefani, Leonhard F. Spiegelb