June 10, 2022
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 moreMay 10, 2022
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 moreApril 17, 2022
Data as a major component of a deep learning solution is often undervalued in the ML projects, which results in a lower-than-expected accuracy, requiring hours and hours of model tuning. According to Andrew Ng, 99% of the recent publications are model-centric with only 1% being data-centric. He argues that there should be a balance between […]
Read moreMarch 24, 2022
INTRODUCTION Over the last half-century, the design and implementation of declarative query processing techniques in relational database systems has been a foundational topic for both the academic and industrial database research communities. Despite this sustained study, the unfortunate reality is that the resulting solutions have largely remained a “black art”. This is due to the […]
Read moreFebruary 23, 2022
We expect virtually every database to be on Cloud shortly. All database vendors strive to survive in this competing market. In the era of cloud computing, databases will become easily accessible, and thus even non-technical users want to use such databases like their TVs. In this situation, we need to interact with databases through accessible […]
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