Kyle Deeds and Ziniu Wu
September 13, 2023
The Case for Cardinality Bounds: Principled Conservatism in Query Optimization
Introduction In the last decade, the database community has identified cardinality estimation as the primary stumbling block for modern query optimizers. Cardinality estimates, which estimate the size of sub-plan queries, are the primary basis for choosing between query plans, so poor estimates may result in catastrophic query execution plans. Research on this topic has consistently […]
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Mohamed Sharaf
February 16, 2023
Utilizing the Synergy between Query Refinement and View Recommendation for Visual Data Exploration
In this blog, we discuss the potential benefits of augmenting automated view recommendation solutions with query refinement techniques towards achieving insightful data exploration. Particularly, effective data exploration has been fueled by many approaches that rely on either view recommendation or query refinement, as two separate and independent techniques for gaining valuable insights from data. In […]
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Boris Glavic
January 24, 2023
Why Uncertainty is Unavoidable and What We Can Do About That
Uncertainty arises naturally in many application domains due to measurement errors, human error in data entry or transformation, missing data and bias in data collection, and many other reasons. When uncertainty is ignored during data preprocessing and analysis, this leads to hard to trace errors which can have severe real world implications such as false incarcerations […]
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karimaechihabi
December 29, 2022
Similarity Search for Scalable Data Science: The Past, Present and Exciting Road Ahead
Similarity search is a fundamental building block for a myriad of critical data science applications involving large collections of high-dimensional objects, including data discovery, data cleaning, information retrieval, classification, outlier detection and clustering. Similarity search finds objects in a collection close to a given query according to some definition of sameness. This challenging problem has […]
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Senjuti Basu Roy
October 26, 2022
Returning Top-K : Preference Aggregation or Sortition, or is there a Better Middle Ground?
Given a large number of users’ preferences (numerical or ordinal scores, ranked order) over a large number of objects, returning top-k results entails selecting a small list/set containing exactly k objects that are most “appropriate “. In this article, I will investigate two alternatives for selecting a top-k list/set that consumes such preference based inputs. […]
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