Can we extract the necessary information from all that big data in a timely manner?

(10-06-2022) In his PhD, Leandro Ordonez Ante researched a way to quickly extract the relevant information from the abundance of data generated in data intensive domains such as the Internet of Things.

The rapid commoditization of data storage, computing power, and network capacity over the last decades has led to an explosive growth of the volume of data available, setting a trend that is expected to accelerate in the coming years. According to a recent forecast by the International Data Corporation (IDC), the amount of data generated worldwide only in 2021 is set to reach 79 zettabytes (i.e., 79×10^21 bytes). Furthermore, this metric will compound over the next five years, placing the volume of data created globally at 181 zettabytes by 2025.

In the modern hyperconnected world, virtually every interaction we have with our surroundings leaves behind a digital footprint. Organizations in all sectors are increasingly turning to Internet of Things (IoT) technologies to monitor their operations and collect data concerning their business activity. As the volume, variety, and complexity of data grow larger, so does the difficulty for processing, analyzing, and distilling insights from it.

“Such big data sets have long outgrown the capabilities offered by traditional data management technologies”, Leandro explains.

“Most of the data processing conducted nowadays still predominantly relies on batch processing methods which are known to be subject to high latency. In such circumstances, organizations face the risk of making decisions and taking action on stale data, especially for latency-sensitive applications running on these large, high-dimensional data collections”, Leandro continues.

“In my PhD, I investigated how it is possible to still quickly extract the right information from this big data. More specifically I researched the understanding of the problem of enabling interactive low-latency querying on large multidimensional data sets”, Leandro concludes.

Read a more detailed summary or the entire PhD


PhD Title: Enabling Interactive Querying for Latency-Sensitive Applications on Big Datasets


Contact: Leandro Ordonez Ante, Filip De Turck, Tim Wauters

Leandro Ordonez Ante

Leandro Ordonez Ante was born in Popayan, Colombia, in 1988. He completed both his B.Sc. in Electronics Engineering and M.Sc. in Telematics Engineering at the University of Cauca in Colombia.

At the beginning of 2016, he started working as a scientific assistant at IDLab, Ghent University – imec, and by December of the same year, he started his Ph.D. on the topic of Adaptable Big Data Platforms for Data Visualization. Since then, his research focus has shifted towards mechanisms for enabling fast query processing on large multidimensional data for latency-sensitive applications. His Ph.D. research addresses several application domains including Online Analytical Processing, Smart Cities, and Virtual Reality video streaming.

Leandro is the main author of 4 international journal articles and 3 international conference papers. He additionally supervised three master students, assisted the System Programming course in the second year of the Bachelor in Computer Science, and served as Editorial Assistant for the journal IEEE Transactions on Network and Service Management. He is also one of the founders of the BitBang Company, a company devoted to software development and consulting services in data management that operates in Colombia since 2014.


Editor: Jeroen Ongenae - Illustrator: Roger Van Hecke