24 citations · 76 across the 9 of their papers we have counts for
9 papers
What-if Analysis for Business Professionals: Current Practices and Future Opportunities
Sneha Gathani, Zhicheng Liu, Peter J. Haas +1
What-if analysis (WIA) is essential for data-driven decision-making, allowing users to assess how changes in variables impact outcomes and explore alternative scenarios. Existing W…
Augmenting Decision Making via Interactive What-If Analysis
Sneha Gathani, Madelon Hulsebos, James Gale +2
The fundamental goal of business data analysis is to improve business decisions using data. Business users often make decisions to achieve key performance indicators (KPIs) such as…
Exact PPS Sampling with Bounded Sample Size
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
Probability proportional to size (PPS) sampling schemes with a target sample size aim to produce a sample comprising a specified number of items while ensuring that each item i…
Stochastic Package Queries in Probabilistic Databases
Matteo Brucato, Nishant Yadav, Azza Abouzied +2
We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a package (bag of tuples in a relational d…
Temporally-Biased Sampling Schemes for Online Model Management
Brian Hentschel, Peter J. Haas, Yuanyuan Tian
To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily,…
Unknown Examples & Machine Learning Model Generalization
Yeounoh Chung, Peter J. Haas, Eli Upfal +1
Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key…