Diversity and Inclusion

Starting in 2021, the Data Management community begins an integrated effort to promote diversity and inclusion in all aspects of our professional activities. MDM 2024 participates in this effort (alongside SIGMOD, VLDB, SoCC, ICDE and EDBT/ICDT) which celebrates the diversity in our community and welcomes everyone regardless of age, sex, gender identity, race, ethnicity, socioeconomic background, country of origin, religion, sexual orientation, physical ability, education, work experience, etc. It also welcomes people and opinions of all political persuasions, as long as they abide by the ACM policy against hate speech and harassment. Specific information can be found in the following dedicated web site: https://dbdni.github.io/

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D&I grants (applications closed)


D&I Keynote

Pınar Tözün

Data Processing at the Edge: From Satellites to Earth

Abstract: Satellites have become more widely available thanks to the reduction in the size and cost of their components. As a result, there has been an advent of smaller organizations having the ability to deploy satellites with a variety of data-intensive applications to run on them. One popular application is image analysis to detect, for example, land, ice, clouds, etc. for Earth observation. However, the resource-constrained nature of the devices deployed in satellites creates additional challenges for this resource-intensive application. This talk will present our work and lessons learned on building an Image Processing Unit (IPU) for a small satellite with a specific focus on determining the most suitable edge device to deploy on the satellite. Then, it will identify the crucial research directions to get more value out of data closer to the data sources, i.e., at the edge, for emerging data-intensive applications.
Bio: Pınar Tözün is an Associate Professor at IT University of Copenhagen. Before ITU, she was a research staff member at IBM Almaden Research Center. Prior to joining IBM, she received her PhD from EPFL. Her thesis received ACM SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention in 2016. Her research focuses on resource-aware machine learning, performance characterization of data-intensive systems, and scalability and efficiency of data-intensive systems on modern hardware.