Zhifeng Bao
Zhifeng Bao

Managing and Exploiting Massive Geolocation Data

Uncategorized No Comment

The sheer volume, variety, and velocity of data in this modern era have enabled significant advancements in many research areas. However, the advancements in the research community thanks to Big Data do not necessarily translate to the benefit of society; of ordinary people living ordinary lives. There is indeed a gap between breakthroughs in the […]

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Themis Palpanas
Themis Palpanas

A Brief History of Data Series Indexing: from Time Series to High-Dimensional Vectors and Deep Neural Network Embeddings 

Data Series

In this post, we motivate the need for efficient and effective solutions for data series similarity search, and we briefly present the work that has been done in this direction by the data series community. We also discuss the relationship to high-dimensional (high-d) vectors and deep neural network embeddings, point to the relevant efforts in […]

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Azza Abouzied
Azza Abouzied

From Curbing Epidemics to Investing: We can Help!

Decision Making, Epidemics

We built a tool, EpiPolicy, to help policy-makers better plan interventions to combat epidemics [13]. It was an eye-opening experience, where through collaborations and interviews with teams of epidemiologists, public health officials, and economists, we understood some of the complexities of decision-making on a momentous scale. Decisions and policies made by these teams can seriously […]

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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 […]

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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 […]

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