Archive for the Big Data category
Zhifeng Bao
September 17, 2022
Managing and Exploiting Massive Geolocation Data
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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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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bigvis2020
June 26, 2020
Big Data Visualization and Analytics: Future Research Challenges and Emerging Applications – Part 2
Data visualization and analytics are nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can […]
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bigvis2020
March 20, 2020
Big Data Visualization and Analytics: Future Research Challenges and Emerging Applications – Part 1
Data visualization and analytics are nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can […]
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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 […]
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