Archive for the Databases category

Kyle Deeds and Ziniu Wu

The Case for Cardinality Bounds: Principled Conservatism in Query Optimization

Databases, Query Processing

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

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 […]

Read more
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 […]

Read more
Kian-Lee Tan

Data Management For The Metaverse

Databases

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

Data-quality Driven Design of Databases

Big Data, 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

Categories