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 moreJune 20, 2019
The vision of natural language interfaces to databases (NLIDBs) is to make data stores more accessible for a wide range of non-tech savvy end users with the ultimate goal to talk to a database (almost) like to a human. While initially the database community focused on relational databases, there is currently a renaissance of building […]
Read moreAugust 21, 2018
Overview of DEEM 2018 The ACM SIGMOD Second Workshop on Data Management for End-to-End Machine Learning (DEEM) was successfully held last June in Houston, TX. The goal of DEEM is to bring together researchers and practitioners at the intersection of applied machine learning (ML) and data management/systems research to discuss data management/systems issues in ML […]
Read moreJune 25, 2018
Information visualization is an essential tool in the arsenal of a data scientist: visualizations help identify trends and patterns, spot outliers and anomalies, and verify hypotheses. Moreover, visualizations are visceral and intuitive: they tell us stories about our data; they educate, delight, inform, enthrall, amaze, and clarify. This has led to the overwhelming popularity of […]
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