Archive for the Databases category
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 […]
Read more
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 […]
Read more
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 […]
Read more
Kian-Lee Tan
June 10, 2022
Data Management For The Metaverse
In 2009, we wrote an article highlighting some database challenges in a co-space environment [1]. In such an environment, the physical space and the digital space co-exist in a “universe” and applications can manipulate the data flow within and across the two spaces. 13 years have since passed and progress on co-space research has been […]
Read more
Sebastian Link
May 10, 2022
Data-quality Driven Design of Databases
Financially, poor data quality costs organizations some ludicrous amounts of money. Worse, poor data quality is a strong inhibitor to the success of data science: No analytical method can create value from poor quality data. As a consequence, data science projects invest a majority of their resources on cleansing data. However, cleansing resists automation as […]
Read more