Kyle Deeds and Ziniu Wu

The Case for Cardinality Bounds: Principled Conservatism in Query Optimization

Databases, Query Processing No Comment

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

Utilizing the Synergy between Query Refinement and View Recommendation for Visual Data Exploration

data exploration, Databases, Recommendations, Visualization

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

Why Uncertainty is Unavoidable and What We Can Do About That

Analytics, Data Preparation, Databases

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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Similarity Search for Scalable Data Science: The Past, Present and Exciting Road Ahead

Data Science

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

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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