51 citations · 144 across the 13 of their papers we have counts for
10 papers · 1 filter
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities
Doris Xin, Hui Miao, Aditya Parameswaran +1
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations. Unlike the traditional perception of ML in research, ML production pipelines a…
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
Yeounoh Chung, Tim Kraska, Neoklis Polyzotis +2
As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it…
The Case for Learned Index Structures
Tim Kraska, Alex Beutel, Ed H. Chi +2
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a re…
Towards a Workload for Evolutionary Analytics
Jeff LeFevre, Jagan Sankaranarayanan, Hakan Hacigumus +2
Emerging data analysis involves the ingestion and exploration of new data sets, application of complex functions, and frequent query revisions based on observing prior query answer…
RITA: An Index-Tuning Advisor for Replicated Databases
Quoc Trung Tran, Ivo Jimenez, Rui Wang +2
Given a replicated database, a divergent design tunes the indexes in each replica differently in order to specialize it for a specific subset of the workload. This specialization b…
Exploiting Opportunistic Physical Design in Large-scale Data Analytics
Jeff LeFevre, Jagan Sankaranarayanan, Hakan Hacigumus +3
Large-scale systems, such as MapReduce and Hadoop, perform aggressive materialization of intermediate job results in order to support fault tolerance. When jobs correspond to explo…