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ORCIDSebastian Schelter
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- affiliation: University of Amsterdam, The Netherlands
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2020 – today
- 2024
[j24]Stefan Grafberger, Zeyu Zhang, Sebastian Schelter, Ce Zhang:
Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI. IEEE Data Eng. Bull. 47(1): 63-81 (2024)- [j23]Till Döhmen
, Radu Geacu, Madelon Hulsebos, Sebastian Schelter:
SchemaPile: A Large Collection of Relational Database Schemas. Proc. ACM Manag. Data 2(3): 172 (2024) - [j22]Tim Januschowski, Yuyang Wang, Jan Gasthaus, Syama Sundar Rangapuram, Caner Turkmen, Jasper Zschiegner, Lorenzo Stella, Michael Bohlke-Schneider, Danielle C. Maddix, Konstantinos Benidis, Alexander Alexandrov, Christos Faloutsos, Sebastian Schelter:
A Flexible Forecasting Stack. Proc. VLDB Endow. 17(12): 3883-3892 (2024) - [j21]Sebastian Schelter, Stefan Grafberger, Maarten de Rijke:
Snapcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning. Proc. VLDB Endow. 17(12): 4273-4276 (2024) - [j20]Songgaojun Deng, Olivier Sprangers, Ming Li, Sebastian Schelter, Maarten de Rijke:
Domain Generalization in Time Series Forecasting. ACM Trans. Knowl. Discov. Data 18(5): 113:1-113:24 (2024) - [j19]Sergey Redyuk, Zoi Kaoudi, Sebastian Schelter, Volker Markl:
Assisted design of data science pipelines. VLDB J. 33(4): 1129-1153 (2024) - [c58]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines". DEEM@SIGMOD 2024: 7-11 - [c57]Zeyu Zhang, Paul Groth, Iacer Calixto, Sebastian Schelter:
Directions Towards Efficient and Automated Data Wrangling with Large Language Models. ICDEW 2024: 301-304 - [c56]Barrie Kersbergen, Olivier Sprangers, Frank Kootte, Shubha Guha, Maarten de Rijke, Sebastian Schelter:
Etude - Evaluating the Inference Latency of Session-Based Recommendation Models at Scale. ICDE 2024: 5177-5183 - [c55]Bojan Karlas, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu, Ce Zhang:
Data Debugging with Shapley Importance over Machine Learning Pipelines. ICLR 2024 - [i18]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines". CoRR abs/2404.19591 (2024) - [i17]Zeyu Zhang, Paul Groth, Iacer Calixto, Sebastian Schelter:
AnyMatch - Efficient Zero-Shot Entity Matching with a Small Language Model. CoRR abs/2409.04073 (2024) - [i16]Sebastian Schelter, Stefan Grafberger:
Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code! CoRR abs/2409.10081 (2024) - 2023
- [j18]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. Proc. ACM Manag. Data 1(2): 128:1-128:26 (2023) - [j17]Stefan Grafberger, Shubha Guha, Paul Groth, Sebastian Schelter:
MLWHATIF: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? Proc. VLDB Endow. 16(12): 4002-4005 (2023) - [c54]Fatemeh Sarvi, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke:
How to Make an Outlier? Studying the Effect of Presentational Features on the Outlierness of Items in Product Search Results. CHIIR 2023: 346-350 - [c53]Sebastian Schelter:
Reconstructing and Querying ML Pipeline Intermediates. CIDR 2023 - [c52]Shubha Guha, Falaah Arif Khan, Julia Stoyanovich, Sebastian Schelter:
Automated Data Cleaning Can Hurt Fairness in Machine Learning-based Decision Making. ICDE 2023: 3747-3754 - [c51]Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke:
On the Impact of Outlier Bias on User Clicks. SIGIR 2023: 18-27 - [c50]Sebastian Schelter, Mozhdeh Ariannezhad, Maarten de Rijke:
Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation. SIGIR 2023: 2011-2015 - [c49]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Bojan Karlas, Ce Zhang:
Proactively Screening Machine Learning Pipelines with ARGUSEYES. SIGMOD Conference Companion 2023: 91-94 - [c48]Mozhdeh Ariannezhad, Ming Li, Sebastian Schelter, Maarten de Rijke:
A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping. WSDM 2023: 87-95 - [c47]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Provenance Tracking for End-to-End Machine Learning Pipelines. WWW (Companion Volume) 2023: 1512 - [d2]Stefan Grafberger, Shubha Guha, Paul Groth, Sebastian Schelter:
shubhaguha/mlwhatif-demo: Demo for VLDB 2023. Zenodo, 2023
- [i15]Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke:
On the Impact of Outlier Bias on User Clicks. CoRR abs/2305.00857 (2023) - [i14]Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei, Meng Cao, Sebastian Schelter, Ce Zhang:
Improving Retrieval-Augmented Large Language Models via Data Importance Learning. CoRR abs/2307.03027 (2023) - [i13]Olivier Sprangers, Wander Wadman, Sebastian Schelter, Maarten de Rijke:
Hierarchical Forecasting at Scale. CoRR abs/2310.12809 (2023) - 2022
- [j16]Julia Stoyanovich, Serge Abiteboul, Bill Howe, H. V. Jagadish, Sebastian Schelter:
Responsible data management. Commun. ACM 65(6): 64-74 (2022) - [j15]Sebastian Schelter:
Letter from the Special Issue Editor. IEEE Data Eng. Bull. 45(1): 2-3 (2022) - [j14]Sergey Redyuk, Zoi Kaoudi, Sebastian Schelter, Volker Markl:
DORIAN in action: Assisted Design of Data Science Pipelines. Proc. VLDB Endow. 15(12): 3714-3717 (2022) - [j13]Stefan Grafberger, Paul Groth, Julia Stoyanovich, Sebastian Schelter:
Data distribution debugging in machine learning pipelines. VLDB J. 31(5): 1103-1126 (2022) - [c46]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang:
Screening Native Machine Learning Pipelines with ArgusEyes. CIDR 2022 - [c45]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards data-centric what-if analysis for native machine learning pipelines. DEEM@SIGMOD 2022: 3:1-3:5 - [c44]Till Döhmen, Madelon Hulsebos, Christian Beecks, Sebastian Schelter:
GitSchemas: A Dataset for Automating Relational Data Preparation Tasks. ICDE Workshops 2022: 74-78 - [c43]Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter, Maarten de Rijke:
ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping. SIGIR 2022: 1240-1250 - [c42]Barrie Kersbergen, Olivier Sprangers, Sebastian Schelter:
Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale. SIGMOD Conference 2022: 150-159 - [c41]Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke:
Understanding and Mitigating the Effect of Outliers in Fair Ranking. WSDM 2022: 861-869 - [c40]Mozhdeh Ariannezhad, Mohamed Yahya, Edgar Meij, Sebastian Schelter, Maarten de Rijke:
Understanding Financial Information Seeking Behavior from User Interactions with Company Filings. WWW (Companion Volume) 2022: 586-594 - [e5]Julia Stoyanovich, Jens Teubner, Paolo Guagliardo, Milos Nikolic, Andreas Pieris, Jan Mühlig, Fatma Özcan, Sebastian Schelter, H. V. Jagadish, Meihui Zhang:
Proceedings of the 25th International Conference on Extending Database Technology, EDBT 2022, Edinburgh, UK, March 29 - April 1, 2022. OpenProceedings.org 2022, ISBN 978-3-89318-086-8 [contents] - [d1]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang:
schelterlabs/arguseyes. Zenodo, 2022 - [i12]Benjamin Longxiang Wang, Sebastian Schelter:
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items. CoRR abs/2201.13313 (2022) - [i11]Bojan Karlas, David Dao, Matteo Interlandi, Bo Li, Sebastian Schelter, Wentao Wu, Ce Zhang:
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines. CoRR abs/2204.11131 (2022) - 2021
- [j12]Sebastian Schelter:
Letter from the Special Issue Editor. IEEE Data Eng. Bull. 44(1): 2 (2021) - [c39]Stefan Grafberger, Julia Stoyanovich, Sebastian Schelter:
Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. CIDR 2021 - [c38]Mozhdeh Ariannezhad, Sami Jullien, Pim Nauts, Min Fang, Sebastian Schelter, Maarten de Rijke:
Understanding Multi-channel Customer Behavior in Retail. CIKM 2021: 2867-2871 - [c37]Sergey Redyuk, Zoi Kaoudi, Volker Markl, Sebastian Schelter:
Automating Data Quality Validation for Dynamic Data Ingestion. EDBT 2021: 61-72 - [c36]Sebastian Schelter, Tammo Rukat, Felix Biessmann:
JENGA - A Framework to Study the Impact of Data Errors on the Predictions of Machine Learning Models. EDBT 2021: 529-534 - [c35]Barrie Kersbergen, Sebastian Schelter:
Learnings from a Retail Recommendation System on Billions of Interactions at bol.com. ICDE 2021: 2447-2452 - [c34]Olivier Sprangers, Sebastian Schelter, Maarten de Rijke:
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. KDD 2021: 1510-1520 - [c33]Sebastian Schelter, Stefan Grafberger, Ted Dunning:
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. SIGMOD Conference 2021: 1545-1557 - [c32]Stefan Grafberger, Shubha Guha, Julia Stoyanovich, Sebastian Schelter:
MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines. SIGMOD Conference 2021: 2736-2739 - [i10]Olivier Sprangers, Sebastian Schelter, Maarten de Rijke:
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. CoRR abs/2106.01682 (2021) - [i9]Olivier Sprangers, Sebastian Schelter, Maarten de Rijke:
Parameter Efficient Deep Probabilistic Forecasting. CoRR abs/2112.02905 (2021) - [i8]Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke:
Understanding and Mitigating the Effect of Outliers in Fair Ranking. CoRR abs/2112.11251 (2021) - 2020
- [j11]Sebastian Schelter, Julia Stoyanovich:
Taming Technical Bias in Machine Learning Pipelines. IEEE Data Eng. Bull. 43(4): 39-50 (2020) - [j10]Robin Anil, Gökhan Çapan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, Özgür Yilmazel:
Apache Mahout: Machine Learning on Distributed Dataflow Systems. J. Mach. Learn. Res. 21: 127:1-127:6 (2020) - [j9]Sebastian Schelter:
Technical Perspective: Query Optimization for Faster Deep CNN Explanations. SIGMOD Rec. 49(1): 60 (2020) - [c31]Sebastian Schelter:
"Amnesia" - Machine Learning Models That Can Forget User Data Very Fast. CIDR 2020 - [c30]Ji Zhang, Yuanzhang Wang, Yangtao Wang, Ke Zhou, Sebastian Schelter, Ping Huang, Bin Cheng, Yongguang Ji:
Tier-Scrubbing: An Adaptive and Tiered Disk Scrubbing Scheme with Improved MTTD and Reduced Cost. DAC 2020: 1-6 - [c29]Amir Aghasadeghi, Vera Zaychik Moffitt, Sebastian Schelter, Julia Stoyanovich:
Zooming Out on an Evolving Graph. EDBT 2020: 25-36 - [c28]Sergey Redyuk, Volker Markl, Sebastian Schelter:
Towards Unsupervised Data Quality Validation on Dynamic Data. EDBT/ICDT Workshops 2020 - [c27]Sebastian Schelter, Yuxuan He, Jatin Khilnani, Julia Stoyanovich:
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions. EDBT 2020: 395-398 - [c26]Mozhdeh Ariannezhad, Sebastian Schelter, Maarten de Rijke:
Demand Forecasting in the Presence of Privileged Information. AALTD@PKDD/ECML 2020: 46-62 - [c25]Sebastian Schelter:
Three Challenges in Building Industrial-Scale Recommender Systems. ORSUM@RecSys 2020 - [c24]Edo Liberty, Zohar S. Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, Alex Smola:
Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference 2020: 731-737 - [c23]Sebastian Schelter, Tammo Rukat, Felix Bießmann:
Learning to Validate the Predictions of Black Box Classifiers on Unseen Data. SIGMOD Conference 2020: 1289-1299 - [c22]Ji Zhang, Ping Huang, Ke Zhou, Ming Xie, Sebastian Schelter:
HDDse: Enabling High-Dimensional Disk State Embedding for Generic Failure Detection System of Heterogeneous Disks in Large Data Centers. USENIX ATC 2020: 111-126 - [e4]Sebastian Schelter, Steven Whang, Julia Stoyanovich:
Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, In conjunction with the 2020 ACM SIGMOD/PODS Conference, DEEM@SIGMOD 2020, Portland, OR, USA, June 14, 2020. ACM 2020, ISBN 978-1-4503-8023-2 [contents] - [i7]Fatemeh Sarvi, Nikos Voskarides, Lois Mooiman, Sebastian Schelter, Maarten de Rijke:
A Comparison of Supervised Learning to Match Methods for Product Search. CoRR abs/2007.10296 (2020) - [i6]Mariya Hendriksen, Ernst Kuiper, Pim Nauts, Sebastian Schelter, Maarten de Rijke:
Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers. CoRR abs/2012.08777 (2020)
2010 – 2019
- 2019
- [j8]Felix Bießmann, Tammo Rukat, Philipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas:
DataWig: Missing Value Imputation for Tables. J. Mach. Learn. Res. 20: 175:1-175:6 (2019) - [j7]Andreas Kunft, Asterios Katsifodimos, Sebastian Schelter, Sebastian Breß, Tilmann Rabl, Volker Markl:
An Intermediate Representation for Optimizing Machine Learning Pipelines. Proc. VLDB Endow. 12(11): 1553-1567 (2019) - [c21]Sebastian Schelter, Stefan Grafberger, Philipp Schmidt, Tammo Rukat, Mario Kießling, Andrey Taptunov, Felix Bießmann, Dustin Lange:
Differential Data Quality Verification on Partitioned Data. ICDE 2019: 1940-1945 - [c20]Sergey Redyuk, Sebastian Schelter, Tammo Rukat, Volker Markl, Felix Bießmann:
Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data. HILDA@SIGMOD 2019: 4:1-4:4 - [c19]Sebastian Schelter, Felix Bießmann, Dustin Lange, Tammo Rukat, Philipp Schmidt, Stephan Seufert, Pierre Brunelle, Andrey Taptunov:
Unit Testing Data with Deequ. SIGMOD Conference 2019: 1993-1996 - [c18]Sebastian Schelter, Neoklis Polyzotis, Manasi Vartak, Stephan Seufert:
DEEM 2019: Workshop on Data Management for End-to-End Machine Learning. SIGMOD Conference 2019: 2066-2067 - [c17]Sebastian Schelter, Ufuk Celebi, Ted Dunning:
Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative Filtering. SSDBM 2019: 61-72 - [c16]Tilmann Rabl, Christoph Brücke, Philipp Härtling, Stella Stars, Rodrigo Escobar Palacios, Hamesh Patel, Satyam Srivastava, Christoph Boden, Jens Meiners, Sebastian Schelter:
ADABench - Towards an Industry Standard Benchmark for Advanced Analytics. TPCTC 2019: 47-63 - [e3]Sebastian Schelter, Neoklis Polyzotis, Stephan Seufert, Manasi Vartak:
Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, DEEM@SIGMOD 2019, Amsterdam, The Netherlands, June 30, 2019. ACM 2019, ISBN 978-1-4503-6797-4 [contents] - [i5]Sebastian Schelter, Yuxuan He, Jatin Khilnani, Julia Stoyanovich:
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions. CoRR abs/1911.12587 (2019) - 2018
- [j6]Sebastian Schelter, Felix Bießmann, Tim Januschowski, David Salinas, Stephan Seufert, Gyuri Szarvas:
On Challenges in Machine Learning Model Management. IEEE Data Eng. Bull. 41(4): 5-15 (2018) - [j5]Sebastian Schelter, Jérôme Kunegis:
On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl. J. Web Sci. 4(4): 53-66 (2018) - [j4]Sebastian Schelter, Dustin Lange, Philipp Schmidt, Meltem Celikel, Felix Bießmann, Andreas Grafberger:
Automating Large-Scale Data Quality Verification. Proc. VLDB Endow. 11(12): 1781-1794 (2018) - [c15]Felix Bießmann, David Salinas, Sebastian Schelter, Philipp Schmidt, Dustin Lange:
"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data. CIKM 2018: 2017-2025 - [c14]Christoph Boden, Tilmann Rabl, Sebastian Schelter, Volker Markl:
Benchmarking Distributed Data Processing Systems for Machine Learning Workloads. TPCTC 2018: 42-57 - [e2]Sebastian Schelter, Stephan Seufert, Arun Kumar:
Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, DEEM@SIGMOD 2018, Houston, TX, USA, June 15, 2018. ACM 2018 [contents] - 2017
- [j3]Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang:
Probabilistic Demand Forecasting at Scale. Proc. VLDB Endow. 10(12): 1694-1705 (2017) - [j2]Andreas Kunft, Asterios Katsifodimos, Sebastian Schelter, Tilmann Rabl, Volker Markl:
BlockJoin: Efficient Matrix Partitioning Through Joins. Proc. VLDB Endow. 10(13): 2061-2072 (2017) - [c13]Till Rohrmann, Sebastian Schelter, Tilmann Rabl, Volker Markl:
Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Systems. BTW 2017: 269-288 - [c12]Sebastian Schelter, Jérôme Kunegis:
'Dark Germany': Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing. SocInfo (1) 2017: 277-288 - [c11]Sebastian Schelter, Jérôme Kunegis:
'Dark Germany': Temporal Characteristics and Connectivity Patterns in Online Far-Right Protests Against Refugee Housing. WebSci 2017: 415-416 - [e1]Sebastian Schelter, Reza Zadeh:
Proceedings of the 1st Workshop on Data Management for End-to-End Machine Learning, DEEM@SIGMOD 2017, Chicago, IL, USA, May 14, 2017. ACM 2017, ISBN 978-1-4503-5026-6 [contents] - [i4]Sebastian Schelter, Jérôme Kunegis:
'Dark Germany': Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing. CoRR abs/1707.07594 (2017) - 2016
- [b1]Sebastian Schelter:
Scaling data mining in massively parallel dataflow systems. Technical University of Berlin, Germany, 2016 - [c10]Asterios Katsifodimos, Sebastian Schelter:
Apache Flink: Stream Analytics at Scale. IC2E Workshops 2016: 193 - [c9]Sebastian Schelter, Felix Bießmann, Malisa Zobel, Nedelina Teneva:
Structural Patterns in the Rise of Germany's New Right on Facebook. ICDM Workshops 2016: 440-445 - [c8]Sebastian Schelter, Jérôme Kunegis:
Tracking the Trackers: A Large-Scale Analysis of Embedded Web Trackers. ICWSM 2016: 679-682 - [i3]Sebastian Schelter, Jérôme Kunegis:
On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl. CoRR abs/1607.07403 (2016) - [i2]Nikolaas Steenbergen, Sebastian Schelter, Felix Bießmann:
Doubly stochastic large scale kernel learning with the empirical kernel map. CoRR abs/1609.00585 (2016) - 2015
- [c7]Sebastian Schelter, Juan Soto, Volker Markl, Douglas Burdick, Berthold Reinwald, Alexandre V. Evfimievski:
Efficient sample generation for scalable meta learning. ICDE 2015: 1191-1202 - [c6]Sergey Dudoladov, Chen Xu, Sebastian Schelter, Asterios Katsifodimos, Stephan Ewen, Kostas Tzoumas, Volker Markl:
Optimistic Recovery for Iterative Dataflows in Action. SIGMOD Conference 2015: 1439-1443 - 2014
- [j1]Alexander Alexandrov, Rico Bergmann, Stephan Ewen, Johann-Christoph Freytag, Fabian Hueske, Arvid Heise, Odej Kao, Marcus Leich, Ulf Leser, Volker Markl, Felix Naumann, Mathias Peters, Astrid Rheinländer, Matthias J. Sax, Sebastian Schelter, Mareike Höger, Kostas Tzoumas, Daniel Warneke:
The Stratosphere platform for big data analytics. VLDB J. 23(6): 939-964 (2014) - [c5]Sebastian Schelter:
Scaling data mining in massively parallel dataflow systems. SIGMOD PhD Symposium 2014: 11-15 - [i1]Sebastian Schelter, Venu Satuluri, Reza Zadeh:
Factorbird - a Parameter Server Approach to Distributed Matrix Factorization. CoRR abs/1411.0602 (2014) - 2013
- [c4]Sebastian Schelter, Stephan Ewen, Kostas Tzoumas, Volker Markl:
"All roads lead to Rome": optimistic recovery for distributed iterative data processing. CIKM 2013: 1919-1928 - [c3]Sebastian Schelter, Christoph Boden, Martin Schenck, Alexander Alexandrov, Volker Markl:
Distributed matrix factorization with mapreduce using a series of broadcast-joins. RecSys 2013: 281-284 - [c2]Stephan Ewen, Sebastian Schelter, Kostas Tzoumas, Daniel Warneke, Volker Markl:
Iterative parallel data processing with stratosphere: an inside look. SIGMOD Conference 2013: 1053-1056 - 2012
- [c1]Sebastian Schelter, Christoph Boden, Volker Markl:
Scalable similarity-based neighborhood methods with MapReduce. RecSys 2012: 163-170
Coauthor Index
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