Yunyao Li and Shivakumar Vaithyanathan
November 14, 2018
Role of AI in Enterprise Applications
The recent return of AI summer and the enthusiastic uptake of AI in the commercial world can be loosely attributed to three innovations: Apple’s Siri, Google’s self-driving cars, and IBM Watson Jeopardy. This enthusiasm stems from the belief that AI will influence a wide range of applications across multiple industry segments. While such enthusiasm is, […]
Read more
Amol Deshpande, Ashwin Machanavajjhala
October 9, 2018
Privacy Challenges in the Post-GDPR World: A Data Management Perspective
After being largely neglected in the rush to capitalize on the promise and the potential of Big Data, data privacy and data stewardship issues have resurfaced in industry with a vengeance over the last year. This has been driven, in part, by the increased scrutiny by regulatory bodies all over the world and subsequent legislations, […]
Read more
Arun Kumar
August 21, 2018
ML/AI Systems and Applications: Is the SIGMOD/VLDB Community Losing Relevance?
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 more
Aditya Parameswaran
June 25, 2018
Visual Data Exploration: A Fertile Ground for Data Management Research
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 […]
Read more
Ihab Ilyas
April 18, 2018
Data cleaning is a machine learning problem that needs data systems help!
When dealing with real-world data, dirty data is the norm rather than the exception. We continuously need to predict correct values, impute missing ones, and find links between various data artefacts such as schemas and records. We need to stop treating data cleaning as a piecemeal exercise (resolving different types of errors in isolation), and […]
Read more