Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleSeptember 2021
From ML models to intelligent applications: the rise of MLOps
Proceedings of the VLDB Endowment (PVLDB), Volume 14, Issue 13Page 3419https://doi.org/10.14778/3484224.3484240The last 5+ years in ML have focused on building the best models, hyperparameter optimization, parallel training, massive neural networks, etc. Now that the building of models has become easy, models are being integrated into every piece of software and ...
- research-articleAugust 2019
Opportunities for data management research in the era of horizontal AI/ML
Proceedings of the VLDB Endowment (PVLDB), Volume 12, Issue 12Page 2323https://doi.org/10.14778/3352063.3352149AI/ML is becoming a horizontal technology: its application is expanding to more domains, and its integration touches more parts of the technology stack. Given the strong dependence of ML on data, this expansion creates a new space for applying data ...
- abstractJune 2019
DEEM 2019: Workshop on Data Management for End-to-End Machine Learning
SIGMOD '19: Proceedings of the 2019 International Conference on Management of DataPages 2066–2067https://doi.org/10.1145/3299869.3323598The DEEM workshop brings together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in machine learning application scenarios.
... - research-articleMay 2018
MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis
SIGMOD '18: Proceedings of the 2018 International Conference on Management of DataPages 1285–1300https://doi.org/10.1145/3183713.3196934Model diagnosis is the process of analyzing machine learning (ML) model performance to identify where the model works well and where it doesn't. It is a key part of the modeling process and helps ML developers iteratively improve model accuracy. Often, ...
- ArticleDecember 2017
A meta-learning perspective on cold-start recommendations for items
NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing SystemsPages 6907–6917Matrix factorization (MF) is one of the most popular techniques for product recommendation, but is known to suffer from serious cold-start problems. Item cold-start problems are particularly acute in settings such as Tweet recommendation where new items ...
- research-articleMay 2017
Towards Visualization Recommendation Systems
Data visualization is often used as the first step while performing a variety of analytical tasks. With the advent of large, high-dimensional datasets and significant interest in data science, there is a need for tools that can support rapid visual ...
- research-articleJune 2016
ModelDB: a system for machine learning model management
- Manasi Vartak,
- Harihar Subramanyam,
- Wei-En Lee,
- Srinidhi Viswanathan,
- Saadiyah Husnoo,
- Samuel Madden,
- Matei Zaharia
HILDA '16: Proceedings of the Workshop on Human-In-the-Loop Data AnalyticsArticle No.: 14, Pages 1–3https://doi.org/10.1145/2939502.2939516Building a machine learning model is an iterative process. A data scientist will build many tens to hundreds of models before arriving at one that meets some acceptance criteria (e.g. AUC cutoff, accuracy threshold). However, the current style of model ...
- research-articleSeptember 2015
SeeDB: efficient data-driven visualization recommendations to support visual analytics
Proceedings of the VLDB Endowment (PVLDB), Volume 8, Issue 13Pages 2182–2193https://doi.org/10.14778/2831360.2831371Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a ...
- research-articleAugust 2015
A demonstration of the BigDAWG polystore system
- A. Elmore,
- J. Duggan,
- M. Stonebraker,
- M. Balazinska,
- U. Cetintemel,
- V. Gadepally,
- J. Heer,
- B. Howe,
- J. Kepner,
- T. Kraska,
- S. Madden,
- D. Maier,
- T. Mattson,
- S. Papadopoulos,
- J. Parkhurst,
- N. Tatbul,
- M. Vartak,
- S. Zdonik
Proceedings of the VLDB Endowment (PVLDB), Volume 8, Issue 12Pages 1908–1911https://doi.org/10.14778/2824032.2824098This paper presents BigDAWG, a reference implementation of a new architecture for "Big Data" applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data ...
- research-articleAugust 2014
SeeDB: automatically generating query visualizations
Proceedings of the VLDB Endowment (PVLDB), Volume 7, Issue 13Pages 1581–1584https://doi.org/10.14778/2733004.2733035Data analysts operating on large volumes of data often rely on visualizations to interpret the results of queries. However, finding the right visualization for a query is a laborious and time-consuming task. We demonstrate SeeDB, a system that partially ...
- research-articleJune 2014
GenBase: a complex analytics genomics benchmark
- Rebecca Taft,
- Manasi Vartak,
- Nadathur Rajagopalan Satish,
- Narayanan Sundaram,
- Samuel Madden,
- Michael Stonebraker
SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of DataPages 177–188https://doi.org/10.1145/2588555.2595633This paper introduces a new benchmark designed to test database management system (DBMS) performance on a mix of data management tasks (joins, filters, etc.) and complex analytics (regression, singular value decomposition, etc.) Such mixed workloads are ...
- demonstrationJune 2013
CHIC: a combination-based recommendation system
SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of DataPages 981–984https://doi.org/10.1145/2463676.2465270Current recommender systems are focused largely on recommending items based on similarity. For instance, Netflix can recommend movies similar to previously viewed movies, and Amazon can recommend items based on ratings of similar users. Although ...
- demonstrationJune 2010
QRelX: generating meaningful queries that provide cardinality assurance
SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of dataPages 1215–1218https://doi.org/10.1145/1807167.1807323In many business and consumer applications, queries have cardinality constraints. However, current database systems provide minimal support for cardinality assurance. Consequently, users must adopt a cumbersome trial-and-error approach to find queries ...
- research-articleApril 2009
The ASSISTment Builder: Supporting the Life Cycle of Tutoring System Content Creation
- Leena Razzaq,
- Jozsef Patvarczki,
- Shane F. Almeida,
- Manasi Vartak,
- Mingyu Feng,
- Neil T. Heffernan,
- Kenneth R. Koedinger
IEEE Transactions on Learning Technologies (IEEETLT), Volume 2, Issue 2Pages 157–166https://doi.org/10.1109/TLT.2009.23Content creation is a large component of the cost of creating educational software. Estimates are that approximately 200 hours of development time are required for every hour of instruction. We present an authoring tool designed to reduce this cost as ...