Archive for the Databases category

Kian-Lee Tan
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
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
Kurt Stockinger
Kurt Stockinger

The Rise of Natural Language Interfaces to Databases

data exploration, Databases, Search

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 more
Arun Kumar
Arun Kumar

ML/AI Systems and Applications: Is the SIGMOD/VLDB Community Losing Relevance?

Databases, Machine Learning

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
Aditya Parameswaran

Visual Data Exploration: A Fertile Ground for Data Management Research

Analytics, data exploration, Databases

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

Categories