Archive for the Big Data category
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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Melanie Herschel and Yannis Velegrakis
March 22, 2018
On Data Exploration in the era of Big Data
We are witnessing data of unprecedented volume, variety and velocity. Such data is collected from almost every aspect of human activity and stored in large repositories in order to be later analyzed and turned into useful insights. The storage model is not any more the one in which data is placed in predefined structures with […]
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