Archive for the Databases category
Boris Glavic
January 24, 2023
Why Uncertainty is Unavoidable and What We Can Do About That
Uncertainty arises naturally in many application domain due to measurement errors, human error in data entry or transformation, missing data and bias in data collection, missing values, 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 […]
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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 […]
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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 […]
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Kurt Stockinger
June 20, 2019
The Rise of Natural Language Interfaces to Databases
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 […]
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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 […]
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