{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T04:41:08Z","timestamp":1773895268787,"version":"3.50.1"},"reference-count":120,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:p>\n            Computational notebooks (e.g., Jupyter, Google Colab) are widely used for interactive data science and machine learning. In those frameworks, users can start a\n            <jats:italic>session<\/jats:italic>\n            , then execute\n            <jats:italic>cells<\/jats:italic>\n            (i.e., a set of statements) to create variables, train models, visualize results, etc. Unfortunately, existing notebook systems do not offer live migration: when a notebook launches on a new machine, it loses its\n            <jats:italic>state<\/jats:italic>\n            , preventing users from continuing their tasks from where they had left off. This is because, unlike DBMS, the sessions directly rely on underlying kernels (e.g., Python\/R interpreters) without an additional data management layer. Existing techniques for preserving states, such as copying all variables or OS-level checkpointing, are unreliable (often fail), inefficient, and platform-dependent. Also, re-running code from scratch can be highly time-consuming.\n          <\/jats:p>\n          <jats:p>\n            In this paper, we introduce a new notebook system, Elastic-Notebook, that offers live migration via checkpointing\/restoration using a novel mechanism that is reliable, efficient, and platform-independent. Specifically, by observing all cell executions via transparent, lightweight monitoring, ElasticNotebook can find a reliable and efficient way (i.e.,\n            <jats:italic>replication plan<\/jats:italic>\n            ) for reconstructing the original session state, considering variable-cell dependencies, observed runtime, variable sizes, etc. To this end, our new graph-based optimization problem finds how to reconstruct all variables (efficiently) from a subset of variables that can be transferred across machines. We show that ElasticNotebook reduces end-to-end migration and restoration times by 85%-98% and 94%-99%, respectively, on a variety (i.e., Kaggle, JWST, and Tutorial) of notebooks with negligible runtime and memory overheads of &lt;2.5% and &lt;10%.\n          <\/jats:p>","DOI":"10.14778\/3626292.3626296","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T23:24:55Z","timestamp":1702337095000},"page":"119-133","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["ElasticNotebook: Enabling Live Migration for Computational Notebooks"],"prefix":"10.14778","volume":"17","author":[{"given":"Zhaoheng","family":"Li","sequence":"first","affiliation":[{"name":"Univ. of Illinois Urbana-Champaign"}]},{"given":"Pranav","family":"Gor","sequence":"additional","affiliation":[{"name":"Univ. of Illinois Urbana-Champaign"}]},{"given":"Rahul","family":"Prabhu","sequence":"additional","affiliation":[{"name":"Univ. of Illinois Urbana-Champaign"}]},{"given":"Hui","family":"Yu","sequence":"additional","affiliation":[{"name":"Univ. of Illinois Urbana-Champaign"}]},{"given":"Yuzhou","family":"Mao","sequence":"additional","affiliation":[{"name":"University of Michigan - Ann Arbor"}]},{"given":"Yongjoo","family":"Park","sequence":"additional","affiliation":[{"name":"Univ. of Illinois Urbana-Champaign"}]}],"member":"320","published-online":{"date-parts":[[2023,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/eScience55777.2022.00015"},{"key":"e_1_2_1_2_1","unstructured":"AndresHG. 2021. NLP GloVe BERT TF-IDF LSTM... Explained. www.kaggle.com\/code\/andreshg\/nlp-glove-bert-tf-idf-lstm-explained\/notebook.  AndresHG. 2021. NLP GloVe BERT TF-IDF LSTM... Explained. www.kaggle.com\/code\/andreshg\/nlp-glove-bert-tf-idf-lstm-explained\/notebook."},{"key":"e_1_2_1_3_1","volume-title":"DMTCP: Transparent Checkpointing for Cluster Computations and the Desktop. In 2009 IEEE International Symposium on Parallel & Distributed Processing (IPDPS'09)","author":"Ansel Jason","year":"2009","unstructured":"Jason Ansel , Kapil Arya , and Gene Cooperman . 2009 . DMTCP: Transparent Checkpointing for Cluster Computations and the Desktop. In 2009 IEEE International Symposium on Parallel & Distributed Processing (IPDPS'09) . IEEE, IEEE Computer Society, Rome, Italy, 1--12. Jason Ansel, Kapil Arya, and Gene Cooperman. 2009. DMTCP: Transparent Checkpointing for Cluster Computations and the Desktop. In 2009 IEEE International Symposium on Parallel & Distributed Processing (IPDPS'09). IEEE, IEEE Computer Society, Rome, Italy, 1--12."},{"key":"e_1_2_1_4_1","unstructured":"Microsoft Azure. 2023. Azure ML Studio. learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-run-jupyter-notebooks.  Microsoft Azure. 2023. Azure ML Studio. learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-run-jupyter-notebooks."},{"key":"e_1_2_1_5_1","unstructured":"Microsoft Azure. 2023. Microsoft Azure pay-as-you-go. azure.microsoft.com\/en-us\/pricing\/purchase-options\/pay-as-you-go\/.  Microsoft Azure. 2023. Microsoft Azure pay-as-you-go. azure.microsoft.com\/en-us\/pricing\/purchase-options\/pay-as-you-go\/."},{"key":"e_1_2_1_6_1","first-page":"288","article-title":"Fault tolerance-challenges, techniques and implementation in cloud computing","volume":"9","author":"Bala Anju","year":"2012","unstructured":"Anju Bala and Inderveer Chana . 2012 . Fault tolerance-challenges, techniques and implementation in cloud computing . International Journal of Computer Science Issues (IJCSI) 9 , 1 (2012), 288 . Anju Bala and Inderveer Chana. 2012. Fault tolerance-challenges, techniques and implementation in cloud computing. International Journal of Computer Science Issues (IJCSI) 9, 1 (2012), 288.","journal-title":"International Journal of Computer Science Issues (IJCSI)"},{"key":"e_1_2_1_7_1","unstructured":"Ekrem Bayar. 2022. Store Sales TS Forecasting - A Comprehensive Guide. www.kaggle.com\/code\/ekrembayar\/store-sales-ts-forecasting-a-comprehensive-guide\/notebook.  Ekrem Bayar. 2022. Store Sales TS Forecasting - A Comprehensive Guide. www.kaggle.com\/code\/ekrembayar\/store-sales-ts-forecasting-a-comprehensive-guide\/notebook."},{"key":"e_1_2_1_8_1","volume-title":"Cloud Service ranking using Checkpoint based Load balancing in real time scheduling of Cloud Computing. CoRR abs\/1905.03093","author":"Belgaum Mohammad Riyaz","year":"2019","unstructured":"Mohammad Riyaz Belgaum , Safeeullah Soomro , Zainab Alansari , and Muhammad Alam . 2019. Cloud Service ranking using Checkpoint based Load balancing in real time scheduling of Cloud Computing. CoRR abs\/1905.03093 ( 2019 ), 667--676. arXiv:1905.03093 arxiv.org\/abs\/1905.03093 Mohammad Riyaz Belgaum, Safeeullah Soomro, Zainab Alansari, and Muhammad Alam. 2019. Cloud Service ranking using Checkpoint based Load balancing in real time scheduling of Cloud Computing. CoRR abs\/1905.03093 (2019), 667--676. arXiv:1905.03093 arxiv.org\/abs\/1905.03093"},{"key":"e_1_2_1_9_1","volume-title":"Random Search for Hyper-Parameter Optimization. J. Mach. Learn. Res. 13, null (feb","author":"Bergstra James","year":"2012","unstructured":"James Bergstra and Yoshua Bengio . 2012. Random Search for Hyper-Parameter Optimization. J. Mach. Learn. Res. 13, null (feb 2012 ), 281--305. James Bergstra and Yoshua Bengio. 2012. Random Search for Hyper-Parameter Optimization. J. Mach. Learn. Res. 13, null (feb 2012), 281--305."},{"key":"e_1_2_1_10_1","unstructured":"Simon S\u00e9hier Bert Hubert Jacco Geul. 2020. WonderShaper. github.com\/magnific0\/wondershaper.  Simon S\u00e9hier Bert Hubert Jacco Geul. 2020. WonderShaper. github.com\/magnific0\/wondershaper."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTER.2010.18"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3595360.3595855"},{"key":"e_1_2_1_13_1","unstructured":"Chhaya Choudhary. 2023. Machine Learning and Deep learning Notebooks. github.com\/chhayac\/Machine-Learning-Notebooks.  Chhaya Choudhary. 2023. Machine Learning and Deep learning Notebooks. github.com\/chhayac\/Machine-Learning-Notebooks."},{"key":"e_1_2_1_14_1","unstructured":"Bokeh Contributors. 2023. Bokeh - Interaction. docs.bokeh.org\/en\/latest\/docs\/user_guide\/interaction.html.  Bokeh Contributors. 2023. Bokeh - Interaction. docs.bokeh.org\/en\/latest\/docs\/user_guide\/interaction.html."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00354-013-0302-4"},{"key":"e_1_2_1_16_1","unstructured":"CRIU. 2023. CRIU - Invisible file. criu.org\/Invisible_files.  CRIU. 2023. CRIU - Invisible file. criu.org\/Invisible_files."},{"key":"e_1_2_1_17_1","unstructured":"CRIU. 2023. Linux CRIU. criu.org\/Main_Page.  CRIU. 2023. Linux CRIU. criu.org\/Main_Page."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824127"},{"key":"e_1_2_1_19_1","unstructured":"JupyterHub Idle Culler. 2023. JupyterHub Idle Culler. github.com\/jupyterhub\/jupyterhub-idle-culler.  JupyterHub Idle Culler. 2023. JupyterHub Idle Culler. github.com\/jupyterhub\/jupyterhub-idle-culler."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/eScience51609.2021.00013"},{"key":"e_1_2_1_21_1","unstructured":"Nvidia Developer. 2023. Nvidia - CUDA. developer.nvidia.com\/cuda-toolkit.  Nvidia Developer. 2023. Nvidia - CUDA. developer.nvidia.com\/cuda-toolkit."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2503210.2503217"},{"key":"e_1_2_1_23_1","unstructured":"DimitreOliveira. 2019. Model stacking feature engineering and EDA. www.kaggle.com\/code\/dimitreoliveira\/model-stacking-feature-engineering-and-eda\/notebook.  DimitreOliveira. 2019. Model stacking feature engineering and EDA. www.kaggle.com\/code\/dimitreoliveira\/model-stacking-feature-engineering-and-eda\/notebook."},{"key":"e_1_2_1_24_1","unstructured":"Docker. 2013-2023. Docker documentation - Swarm mode overview. docs.docker.com\/engine\/swarm\/.  Docker. 2013-2023. Docker documentation - Swarm mode overview. docs.docker.com\/engine\/swarm\/."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2207676.2208293"},{"key":"e_1_2_1_26_1","unstructured":"dwd-daniel. 2022. UncomplicatedFirewall. wiki.ubuntu.com\/UncomplicatedFirewall.  dwd-daniel. 2022. UncomplicatedFirewall. wiki.ubuntu.com\/UncomplicatedFirewall."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380574"},{"key":"e_1_2_1_28_1","unstructured":"Lightning AI et al. 2018. PyTorch ModelCheckpoint. pytorch-lightning.readthedocs.io\/en\/stable\/api\/pytorch_lightning.callbacks.ModelCheckpoint.html.  Lightning AI et al. 2018. PyTorch ModelCheckpoint. pytorch-lightning.readthedocs.io\/en\/stable\/api\/pytorch_lightning.callbacks.ModelCheckpoint.html."},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"LRDR FORD-FULKERSON. 1962. Flows in Networks.  LRDR FORD-FULKERSON. 1962. Flows in Networks.","DOI":"10.1515\/9781400875184"},{"key":"e_1_2_1_30_1","unstructured":"Python Software Foundation. 2023. Python Hashlib. docs.python.org\/3\/library\/hashlib.html.  Python Software Foundation. 2023. Python Hashlib. docs.python.org\/3\/library\/hashlib.html."},{"key":"e_1_2_1_31_1","unstructured":"Python Software Foundation. 2023. Python - AST. docs.python.org\/3\/library\/ast.html.  Python Software Foundation. 2023. Python - AST. docs.python.org\/3\/library\/ast.html."},{"key":"e_1_2_1_32_1","unstructured":"Python Software Foundation. 2023. Python - Generators. wiki.python.org\/moin\/Generators.  Python Software Foundation. 2023. Python - Generators. wiki.python.org\/moin\/Generators."},{"key":"e_1_2_1_33_1","unstructured":"Python Software Foundation. 2023. Python JSON. docs.python.org\/3\/library\/json.html.  Python Software Foundation. 2023. Python JSON. docs.python.org\/3\/library\/json.html."},{"key":"e_1_2_1_34_1","unstructured":"Python Software Foundation. 2023. Python Marshal. docs.python.org\/3\/library\/marshal.html.  Python Software Foundation. 2023. Python Marshal. docs.python.org\/3\/library\/marshal.html."},{"key":"e_1_2_1_35_1","unstructured":"Python Software Foundation. 2023. Python Mmap. docs.python.org\/3\/library\/mmap.html.  Python Software Foundation. 2023. Python Mmap. docs.python.org\/3\/library\/mmap.html."},{"key":"e_1_2_1_36_1","unstructured":"Python Software Foundation. 2023. Python Object Reduction. docs.python.org\/3\/library\/pickle.html#object._reduce_.  Python Software Foundation. 2023. Python Object Reduction. docs.python.org\/3\/library\/pickle.html#object._reduce_."},{"key":"e_1_2_1_37_1","unstructured":"Python Software Foundation. 2023. Python Pickle Documentation. docs.python.org\/3\/library\/pickle.html.  Python Software Foundation. 2023. Python Pickle Documentation. docs.python.org\/3\/library\/pickle.html."},{"key":"e_1_2_1_38_1","unstructured":"The Uncertainty Quantification Foundation. 2023. Dill dump session. dill.readthedocs.io\/en\/latest\/dill.html.  The Uncertainty Quantification Foundation. 2023. Dill dump session. dill.readthedocs.io\/en\/latest\/dill.html."},{"key":"e_1_2_1_39_1","unstructured":"The Uncertainty Quantification Foundation. 2023. Dill - PyPi. pypi.org\/project\/dill\/.  The Uncertainty Quantification Foundation. 2023. Dill - PyPi. pypi.org\/project\/dill\/."},{"key":"e_1_2_1_40_1","unstructured":"Tian Gao. 2020. Python Watchpoints. pypi.org\/project\/watchpoints\/.  Tian Gao. 2020. Python Watchpoints. pypi.org\/project\/watchpoints\/."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.14778\/3436905.3436925"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTER.2018.00047"},{"key":"e_1_2_1_43_1","unstructured":"GDB. 2022. GDB Watchpoints. sourceware.org\/gdb\/download\/onlinedocs\/gdb\/Set-Watchpoints.html.  GDB. 2022. GDB Watchpoints. sourceware.org\/gdb\/download\/onlinedocs\/gdb\/Set-Watchpoints.html."},{"key":"e_1_2_1_44_1","unstructured":"Aur\u00e9lien Geron. 2023. Chapter 4 - Training Models. github.com\/ageron\/handson-ml3\/blob\/main\/04_training_linear_models.ipynb.  Aur\u00e9lien Geron. 2023. Chapter 4 - Training Models. github.com\/ageron\/handson-ml3\/blob\/main\/04_training_linear_models.ipynb."},{"key":"e_1_2_1_45_1","volume-title":"Machine Learning Notebooks","author":"Geron Aur\u00e9lien","unstructured":"Aur\u00e9lien Geron . 2023. Machine Learning Notebooks , 3 rd edition. github.com\/ageron\/handson-ml3. Aur\u00e9lien Geron. 2023. Machine Learning Notebooks, 3rd edition. github.com\/ageron\/handson-ml3.","edition":"3"},{"key":"e_1_2_1_46_1","unstructured":"Google. 2023. Google Colab. colab.research.google.com\/.  Google. 2023. Google Colab. colab.research.google.com\/."},{"key":"e_1_2_1_47_1","unstructured":"Google. 2023. Google Colab pay-as-you-go. colab.research.google.com\/signup.  Google. 2023. Google Colab pay-as-you-go. colab.research.google.com\/signup."},{"key":"e_1_2_1_48_1","unstructured":"Google and X. 2022. Google AI4Code - Understand Code in Python Notebooks. www.kaggle.com\/competitions\/AI4Code.  Google and X. 2022. Google AI4Code - Understand Code in Python Notebooks. www.kaggle.com\/competitions\/AI4Code."},{"key":"e_1_2_1_49_1","volume-title":"Proceedings of the 4th USENIX Conference on Theory and Practice of Provenance","author":"Philip","unstructured":"Philip J. Guo and Margo Seltzer. 2012. BURRITO: Wrapping Your Lab Notebook in Computational Infrastructure . In Proceedings of the 4th USENIX Conference on Theory and Practice of Provenance ( Boston, MA) (TaPP'12). USENIX Association, USA, 7. Philip J. Guo and Margo Seltzer. 2012. BURRITO: Wrapping Your Lab Notebook in Computational Infrastructure. In Proceedings of the 4th USENIX Conference on Theory and Practice of Provenance (Boston, MA) (TaPP'12). USENIX Association, USA, 7."},{"key":"e_1_2_1_50_1","unstructured":"HAProxy. 2023. HAProxy. www.haproxy.org\/.  HAProxy. 2023. HAProxy. www.haproxy.org\/."},{"key":"e_1_2_1_51_1","unstructured":"Sanskar Hasija. 2022. AI4Code Detailed EDA. www.kaggle.com\/code\/odins0n\/ai4code-detailed-eda.  Sanskar Hasija. 2022. AI4Code Detailed EDA. www.kaggle.com\/code\/odins0n\/ai4code-detailed-eda."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300500"},{"key":"e_1_2_1_53_1","volume-title":"Hex Technologies","author":"Inc.","year":"2023","unstructured":"Inc. Hex Technologies . 2023 . Hex 2.0: Reactivity, Graphs, and a little bit of Magic . hex.tech\/blog\/hex-two-point-oh\/. Inc. Hex Technologies. 2023. Hex 2.0: Reactivity, Graphs, and a little bit of Magic. hex.tech\/blog\/hex-two-point-oh\/."},{"key":"e_1_2_1_54_1","unstructured":"IBM. 2022. IBM Watson Studio Service. www.ibm.com\/docs\/en\/knowledge-accelerators\/1.0.0?topic=catalog-jupyter-notebook.  IBM. 2022. IBM Watson Studio Service. www.ibm.com\/docs\/en\/knowledge-accelerators\/1.0.0?topic=catalog-jupyter-notebook."},{"key":"e_1_2_1_55_1","unstructured":"Kaggle Inc. 2023. Kaggle. www.kaggle.com\/.  Kaggle Inc. 2023. Kaggle. www.kaggle.com\/."},{"key":"e_1_2_1_56_1","unstructured":"Kaggle Inc. 2023. Kaggle Forums - Product Feedback. www.kaggle.com\/discussions\/product-feedback.  Kaggle Inc. 2023. Kaggle Forums - Product Feedback. www.kaggle.com\/discussions\/product-feedback."},{"key":"e_1_2_1_57_1","unstructured":"Kaggle Inc. 2023. Kaggle Notebook Specifications. www.kaggle.com\/docs\/notebooks#technical-specifications.  Kaggle Inc. 2023. Kaggle Notebook Specifications. www.kaggle.com\/docs\/notebooks#technical-specifications."},{"key":"e_1_2_1_58_1","unstructured":"Space Telescope Science Institute. 2023. JWST Data Analysis Example. jwst-docs.stsci.edu\/jwst-post-pipeline-data-analysis\/data-analysis-example-jupyter-notebooks.  Space Telescope Science Institute. 2023. JWST Data Analysis Example. jwst-docs.stsci.edu\/jwst-post-pipeline-data-analysis\/data-analysis-example-jupyter-notebooks."},{"key":"e_1_2_1_59_1","volume-title":"CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM. arXiv:2008.10596 [cs.DC]","author":"Jain Twinkle","year":"2020","unstructured":"Twinkle Jain and Gene Cooperman . 2020 . CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM. arXiv:2008.10596 [cs.DC] Twinkle Jain and Gene Cooperman. 2020. CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM. arXiv:2008.10596 [cs.DC]"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368308.3415397"},{"key":"e_1_2_1_61_1","unstructured":"Project Jupyter. 2023. Jupyter Notebook. jupyter.org\/.  Project Jupyter. 2023. Jupyter Notebook. jupyter.org\/."},{"key":"e_1_2_1_62_1","volume-title":"Slater","author":"Juric Mario","year":"2021","unstructured":"Mario Juric , Steven Stetzler , and Colin T . Slater . 2021 . Checkpoint, Restore , and Live Migration for Science Platforms . arXiv:2101.05782 [astro-ph.IM] Mario Juric, Steven Stetzler, and Colin T. Slater. 2021. Checkpoint, Restore, and Live Migration for Science Platforms. arXiv:2101.05782 [astro-ph.IM]"},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the 9th USENIX Conference on Theory and Practice of Provenance","author":"Koop David","year":"2017","unstructured":"David Koop and Jay Patel . 2017 . Dataflow Notebooks: Encoding and Tracking Dependencies of Cells . In Proceedings of the 9th USENIX Conference on Theory and Practice of Provenance ( Seattle, WA) (TaPP'17). USENIX Association, USA, 17. David Koop and Jay Patel. 2017. Dataflow Notebooks: Encoding and Tracking Dependencies of Cells. In Proceedings of the 9th USENIX Conference on Theory and Practice of Provenance (Seattle, WA) (TaPP'17). USENIX Association, USA, 17."},{"key":"e_1_2_1_64_1","unstructured":"Martin Krasser. 2023. Machine learning notebooks. github.com\/krasserm\/machine-learning-notebook.  Martin Krasser. 2023. Machine learning notebooks. github.com\/krasserm\/machine-learning-notebook."},{"key":"e_1_2_1_65_1","unstructured":"Martin Krasser. 2023. Multi-class Classification. github.com\/krasserm\/machine-learning-notebooks\/blob\/master\/ml-ex3.ipynb.  Martin Krasser. 2023. Multi-class Classification. github.com\/krasserm\/machine-learning-notebooks\/blob\/master\/ml-ex3.ipynb."},{"key":"e_1_2_1_66_1","unstructured":"Kubernetes. 2023. Kubernetes. kubernetes.io\/.  Kubernetes. 2023. Kubernetes. kubernetes.io\/."},{"key":"e_1_2_1_67_1","unstructured":"SFU Database System Lab. 2022. Dataprep - Low-Code Data Preparation. dataprep.ai\/.  SFU Database System Lab. 2022. Dataprep - Low-Code Data Preparation. dataprep.ai\/."},{"key":"e_1_2_1_68_1","unstructured":"Colin Lagator. 2020. Arxiv Data Processing. www.kaggle.com\/code\/colinlagator\/arxiv-data-processing.  Colin Lagator. 2020. Arxiv Data Processing. www.kaggle.com\/code\/colinlagator\/arxiv-data-processing."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/2670979.2670985"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2010.129"},{"key":"e_1_2_1_71_1","unstructured":"Zhaoheng Li Pranav Gor Rahul Prabhu Hui Yu Yuzhou Mao and Yongjoo Park. 2023. ElasticNotebook: Enabling Live Migration for Computational Notebooks (Technical Report). arxiv.org\/abs\/2309.11083.  Zhaoheng Li Pranav Gor Rahul Prabhu Hui Yu Yuzhou Mao and Yongjoo Park. 2023. ElasticNotebook: Enabling Live Migration for Computational Notebooks (Technical Report). arxiv.org\/abs\/2309.11083."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00393"},{"key":"e_1_2_1_73_1","unstructured":"Arch Linux. 2023. chroot. wiki.archlinux.org\/title\/chroot.  Arch Linux. 2023. chroot. wiki.archlinux.org\/title\/chroot."},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.14778\/3447689.3447712"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514075"},{"key":"e_1_2_1_76_1","first-page":"600","article-title":"Fault tolerance model for reliable cloud computing","volume":"1","author":"Meshram Anjali D","year":"2013","unstructured":"Anjali D Meshram , AS Sambare , and SD Zade . 2013 . Fault tolerance model for reliable cloud computing . International Journal on Recent and Innovation Trends in Computing and Communication 1 , 7 (2013), 600 -- 603 . Anjali D Meshram, AS Sambare, and SD Zade. 2013. Fault tolerance model for reliable cloud computing. International Journal on Recent and Innovation Trends in Computing and Communication 1, 7 (2013), 600--603.","journal-title":"International Journal on Recent and Innovation Trends in Computing and Communication"},{"key":"e_1_2_1_77_1","unstructured":"Inc. MongoDB. 2023. BSON. pymongo.readthedocs.io\/en\/stable\/api\/bson\/index.html.  Inc. MongoDB. 2023. BSON. pymongo.readthedocs.io\/en\/stable\/api\/bson\/index.html."},{"key":"e_1_2_1_78_1","volume-title":"Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation","author":"Moritz Philipp","year":"2018","unstructured":"Philipp Moritz , Robert Nishihara , Stephanie Wang , Alexey Tumanov , Richard Liaw , Eric Liang , Melih Elibol , Zongheng Yang , William Paul , Michael I. Jordan , and Ion Stoica . 2018 . Ray: A Distributed Framework for Emerging AI Applications . In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation ( Carlsbad, CA, USA) (OSDI'18). USENIX Association, USA, 561--577. Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, and Ion Stoica. 2018. Ray: A Distributed Framework for Emerging AI Applications. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (Carlsbad, CA, USA) (OSDI'18). USENIX Association, USA, 561--577."},{"key":"e_1_2_1_79_1","unstructured":"Rob Mulla. 2020. Time Series forecasting with Prophet. www.kaggle.com\/code\/robikscube\/time-series-forecasting-with-prophet.  Rob Mulla. 2020. Time Series forecasting with Prophet. www.kaggle.com\/code\/robikscube\/time-series-forecasting-with-prophet."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407807"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3452788"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137789"},{"key":"e_1_2_1_83_1","unstructured":"The pip developers. 2023. Pip Freeze. pip.pypa.io\/en\/stable\/cli\/pip_freeze\/.  The pip developers. 2023. Pip Freeze. pip.pypa.io\/en\/stable\/cli\/pip_freeze\/."},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514073"},{"key":"e_1_2_1_85_1","volume-title":"PBC formerly RStudio","author":"Posit Software PBC","year":"2023","unstructured":"PBC Posit Software , PBC formerly RStudio . 2023 . Posit RStudio . posit.co\/. PBC Posit Software, PBC formerly RStudio. 2023. Posit RStudio. posit.co\/."},{"key":"e_1_2_1_86_1","unstructured":"Gabriel Preda. 2019. LANL Earthquake EDA and Prediction. www.kaggle.com\/code\/gpreda\/lanl-earthquake-eda-and-prediction.  Gabriel Preda. 2019. LANL Earthquake EDA and Prediction. www.kaggle.com\/code\/gpreda\/lanl-earthquake-eda-and-prediction."},{"key":"e_1_2_1_87_1","unstructured":"Kalilur Rahman. 2022. NFL Data Bowl 2023 - Offensive Plays EDA. www.kaggle.com\/code\/kalilurrahman\/nfl-data-bowl-2023-offensive-plays-eda\/notebook.  Kalilur Rahman. 2022. NFL Data Bowl 2023 - Offensive Plays EDA. www.kaggle.com\/code\/kalilurrahman\/nfl-data-bowl-2023-offensive-plays-eda\/notebook."},{"key":"e_1_2_1_88_1","unstructured":"DS Rahul. 2020. Agricultural Drought Prediction. www.kaggle.com\/code\/dsrhul\/agricultural-drought-prediction.  DS Rahul. 2020. Agricultural Drought Prediction. www.kaggle.com\/code\/dsrhul\/agricultural-drought-prediction."},{"key":"e_1_2_1_89_1","unstructured":"Mani Raj. 2022. Amex Dataset. www.kaggle.com\/code\/manirajheerakar\/amex-dataset.  Mani Raj. 2022. Amex Dataset. www.kaggle.com\/code\/manirajheerakar\/amex-dataset."},{"key":"e_1_2_1_90_1","unstructured":"Amazon Web Services. 2023. AWS JupyterHub. docs.aws.amazon.com\/emr\/latest\/ReleaseGuide\/emr-jupyterhub.html.  Amazon Web Services. 2023. AWS JupyterHub. docs.aws.amazon.com\/emr\/latest\/ReleaseGuide\/emr-jupyterhub.html."},{"key":"e_1_2_1_91_1","unstructured":"Shahules. 2022. Basic EDA Cleaning and GloVe. www.kaggle.com\/code\/shahules\/basic-eda-cleaning-and-glove\/notebook.  Shahules. 2022. Basic EDA Cleaning and GloVe. www.kaggle.com\/code\/shahules\/basic-eda-cleaning-and-glove\/notebook."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.14778\/3565838.3565855"},{"key":"e_1_2_1_93_1","unstructured":"shreyas thorat30. 2023. Plant disease classification SDP. www.kaggle.com\/code\/shreyasthorat30\/plant-disease-classification-sdp.  shreyas thorat30. 2023. Plant disease classification SDP. www.kaggle.com\/code\/shreyasthorat30\/plant-disease-classification-sdp."},{"key":"e_1_2_1_94_1","unstructured":"Andrey Shtrauss. 2022. Building an Asset Trading Strategy. www.kaggle.com\/code\/shtrausslearning\/building-an-asset-trading-strategy\/notebook.  Andrey Shtrauss. 2022. Building an Asset Trading Strategy. www.kaggle.com\/code\/shtrausslearning\/building-an-asset-trading-strategy\/notebook."},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/2528521.1508250"},{"key":"e_1_2_1_96_1","unstructured":"StackOverflow. 2019. Colab Session Timeout. stackoverflow.com\/questions\/57113226\/how-can-i-prevent-google-colab-from-disconnecting.  StackOverflow. 2019. Colab Session Timeout. stackoverflow.com\/questions\/57113226\/how-can-i-prevent-google-colab-from-disconnecting."},{"key":"e_1_2_1_97_1","unstructured":"Jupyter Development Team. 2023. nbconvert - Jupyter Notebook Conversion. github.com\/jupyter\/nbconvert.  Jupyter Development Team. 2023. nbconvert - Jupyter Notebook Conversion. github.com\/jupyter\/nbconvert."},{"key":"e_1_2_1_98_1","unstructured":"Nteract Team. 2023. Welcome to papermill. papermill.readthedocs.io\/en\/latest\/.  Nteract Team. 2023. Welcome to papermill. papermill.readthedocs.io\/en\/latest\/."},{"key":"e_1_2_1_99_1","unstructured":"The IPython Development Team. 2023. IPython Interactive Computing. ipython.org\/.  The IPython Development Team. 2023. IPython Interactive Computing. ipython.org\/."},{"key":"e_1_2_1_100_1","unstructured":"The IPython Development Team. 2023. Jupyter checkpoint. jupyter-server.readthedocs.io\/en\/latest\/developers\/contents.html.  The IPython Development Team. 2023. Jupyter checkpoint. jupyter-server.readthedocs.io\/en\/latest\/developers\/contents.html."},{"key":"e_1_2_1_101_1","unstructured":"The IPython Development Team. 2023. Jupyter Magics Class. ipython.readthedocs.io\/en\/stable\/config\/custommagics.html.  The IPython Development Team. 2023. Jupyter Magics Class. ipython.readthedocs.io\/en\/stable\/config\/custommagics.html."},{"key":"e_1_2_1_102_1","unstructured":"The IPython Development Team. 2023. Jupyter store magic. ipython.readthedocs.io\/en\/stable\/config\/extensions\/storemagic.html.  The IPython Development Team. 2023. Jupyter store magic. ipython.readthedocs.io\/en\/stable\/config\/extensions\/storemagic.html."},{"key":"e_1_2_1_103_1","unstructured":"The Matplotlib Development Team. 2023. Matplotlib. matplotlib.org\/.  The Matplotlib Development Team. 2023. Matplotlib. matplotlib.org\/."},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-018-0514-9"},{"key":"e_1_2_1_105_1","volume-title":"Cornell Virtual Workshop Tutorial Notebooks. github.com\/CornellCAC\/CVW_PyDataSci2.","author":"Cornell University","year":"2021","unstructured":"Cornell University . 2021 . Cornell Virtual Workshop Tutorial Notebooks. github.com\/CornellCAC\/CVW_PyDataSci2. Cornell University. 2021. Cornell Virtual Workshop Tutorial Notebooks. github.com\/CornellCAC\/CVW_PyDataSci2."},{"key":"e_1_2_1_106_1","unstructured":"Cornell University. 2021. Investigating Tweet Timelines Using Interactive Bokeh Scatterplots. github.com\/CornellCAC\/CVW_PyDataSci2\/blob\/master\/code\/interactive_visualization_with_bokeh.ipynb.  Cornell University. 2021. Investigating Tweet Timelines Using Interactive Bokeh Scatterplots. github.com\/CornellCAC\/CVW_PyDataSci2\/blob\/master\/code\/interactive_visualization_with_bokeh.ipynb."},{"key":"e_1_2_1_107_1","unstructured":"Cornell University. 2021. SKLearn Tweet Classification. github.com\/CornellCAC\/CVW_PyDataSci2\/blob\/master\/code\/sklearn_tweet_classification.ipynb.  Cornell University. 2021. SKLearn Tweet Classification. github.com\/CornellCAC\/CVW_PyDataSci2\/blob\/master\/code\/sklearn_tweet_classification.ipynb."},{"key":"e_1_2_1_108_1","unstructured":"Cornell University. 2021. Twitter Networks. github.com\/CornellCAC\/CVW_PyDataSci2\/blob\/master\/code\/twitter_networks.ipynb.  Cornell University. 2021. Twitter Networks. github.com\/CornellCAC\/CVW_PyDataSci2\/blob\/master\/code\/twitter_networks.ipynb."},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196934"},{"key":"e_1_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3056101"},{"key":"e_1_2_1_111_1","unstructured":"Devlikamov Vlad. 2022. [TPS-Mar] Fast workflow using scikit-learn-intelex.www.kaggle.com\/code\/lordozvlad\/tps-mar-fast-workflow-using-scikit-learn-intelex\/notebook.  Devlikamov Vlad. 2022. [TPS-Mar] Fast workflow using scikit-learn-intelex.www.kaggle.com\/code\/lordozvlad\/tps-mar-fast-workflow-using-scikit-learn-intelex\/notebook."},{"key":"e_1_2_1_112_1","volume-title":"AIC model selection using Akaike weights. Psychonomic bulletin & review 11, 1","author":"Wagenmakers Eric-Jan","year":"2004","unstructured":"Eric-Jan Wagenmakers and Simon Farrell . 2004. AIC model selection using Akaike weights. Psychonomic bulletin & review 11, 1 ( 2004 ), 192--196. Eric-Jan Wagenmakers and Simon Farrell. 2004. AIC model selection using Akaike weights. Psychonomic bulletin & review 11, 1 (2004), 192--196."},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491418.3530296"},{"key":"e_1_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.14778\/3297753.3297763"},{"key":"e_1_2_1_115_1","first-page":"66","article-title":"Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time","volume":"44","author":"Xin Doris","year":"2021","unstructured":"Doris Xin , Devin Petersohn , Dixin Tang , Yifan Wu , Joseph E. Gonzalez , Joseph M. Hellerstein , Anthony D. Joseph , and Aditya G. Parameswaran . 2021 . Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time . IEEE Data Eng. Bull. 44 , 1 (2021), 66 -- 78 . sites.computer.org\/debull\/A21mar\/p66.pdf Doris Xin, Devin Petersohn, Dixin Tang, Yifan Wu, Joseph E. Gonzalez, Joseph M. Hellerstein, Anthony D. Joseph, and Aditya G. Parameswaran. 2021. Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time. IEEE Data Eng. Bull. 44, 1 (2021), 66--78. sites.computer.org\/debull\/A21mar\/p66.pdf","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_116_1","unstructured":"xxHash. 2023. xxHash - Extremely fast non-cryptographic hash algorithm. github.com\/Cyan4973\/xxHash.  xxHash. 2023. xxHash - Extremely fast non-cryptographic hash algorithm. github.com\/Cyan4973\/xxHash."},{"key":"e_1_2_1_117_1","unstructured":"Yandex. 2023. CatBoost - open-source gradient boosting library. catboost.ai\/.  Yandex. 2023. CatBoost - open-source gradient boosting library. catboost.ai\/."},{"key":"e_1_2_1_118_1","volume-title":"Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing","author":"Zaharia Matei","year":"2010","unstructured":"Matei Zaharia , Mosharaf Chowdhury , Michael J. Franklin , Scott Shenker , and Ion Stoica . 2010 . Spark: Cluster Computing with Working Sets . In Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing ( Boston, MA) (HotCloud'10). USENIX Association, USA, 10. Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster Computing with Working Sets. In Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (Boston, MA) (HotCloud'10). USENIX Association, USA, 10."},{"key":"e_1_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2014.2346293"},{"key":"e_1_2_1_120_1","doi-asserted-by":"publisher","DOI":"10.1145\/2877204"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3626292.3626296","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T23:10:58Z","timestamp":1704755458000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3626292.3626296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":120,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["10.14778\/3626292.3626296"],"URL":"https:\/\/doi.org\/10.14778\/3626292.3626296","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"2023-10-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}