DAISY - Center for Data-intensive Systems

Our colleague Mohsin Iqbal presented today the paper "A Foundation for Spatio-Textual-Temporal Cube Analytics" in collaboration with Matteo Lissandrini and Torben Bach Pedersen in The 23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP2021) https://sites.google.com/view/dolap-2021/home

Find the paper in this link:http://www.info.univ-tours.fr/~marcel/dolap2021/paper3-long.pdf

ABSTRACT
Large amounts of spatial, textual, and temporal (STT) data are being produced daily. This is data containing an unstructured component (text), a spatial component (geographic position), and a time component (timestamp). Therefore, there is a need for a powerful and general way of analyzing STT data together. In this paper, we define and formalize the Spatio-Textual-Temporal Cube (STTCube) structure to enable combined effective and efficient analytical queries over STT data. Our novel data model over STT objects enables novel joint and integrated STT insights that are hard to obtain using existing methods. Moreover, we introduce the new concept of STT measures with associated novel STT-OLAP operators. To allow for efficient large-scale analytics, we present a pre-aggregation framework for exact and approximate computation of STT measures. Our comprehensive experimental evaluation on a real-world Twitter dataset confirms that our proposed methods reduce query response time by 1-5 orders of magnitude compared to the No Materialization baseline and decrease storage cost between 97% and 99.9% compared to the Full Materialization baseline while adding only a negligible overhead in the STTCube construction time. Moreover, approximate computation achieves an accuracy between 90% and 100% while reducing query response time by 3-5 orders of magnitude compared to No Materialization.

Data Engineering for Data Science (DEDS) Ph.D. positions (3 years) now available!
DEDS is jointly organized by Université Libre de Bruxelles (Belgium), Universitat Politècnica de Catalunya (Spain), Aalborg Universitet (Denmark), and the Athena Research and Innovation Centre (Greece). Partner organisations from research, industry and the public sector prominently contribute to the programme by training students and providing secondments in a wide range of domains including Energy, Finance, Health, Transport, and Customer Relationship and Support. https://deds.ulb.ac.be
DEDS operates under the Horizon 2020 - Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2020) framework.

Application deadline: February 7, 2021, midnight AoE (Anywhere on Earth)

Find the application details here: https://deds.ulb.ac.be/
and the research topics here: https://deds.ulb.ac.be/

DEDS - Data Engineering for Data Science Data is a key asset in modern society. Data Science, which focuses on deriving valuable insight and knowledge from raw data, is indispensable for any economic, governmental, and scientific activity. Data Engineering provides the data ecosystem (i.e., data management pipelines, tools and services) th...

A postdoctoral position in graph databases and machine learning for microbial genome recovery now available at Daisy!

This is a joint position between the Center for Data-intensive Systems (Daisy) and the Distributed, Embedded, and Intelligent Systems group (DEIS), available immediately with a flexible starting date within the next couple of months.
The topic is embedded within the context of the VILLUM Synergy project "Data Science meets Microbial Dark Matter". In this project, we want to improve the rate of recovery of microbial genomes and ensure evidence-based analysis by leveraging, expanding, and combining state-of-the-art methods within several fields in exponential growth: DNA sequencing, machine learning, and graph-based analysis. This ambitious goal can only be achieved by the synergy of both data science and bioscience and will thus push the boundaries of both fields.
Read more about the qualifications needed and the application procedure here: http://people.cs.aau.dk/~khose/Vacancy_Synergy.html

About Daisy (Center for Data-Intensive Systems):
Research at Daisy focuses on data-intensive systems, Semantic Web technologies, Web Science and engineering, spatio-temporal data management, business intelligence, and applications of machine learning. International evaluations place Daisy in the global top tier. For example, an independent study of publication performance in the top database outlets in the 10-year period 2001-2010 ranks Daisy second among all research groups in Europe. More information about Daisy can be found at http://daisy.aau.dk.

About the DEIS (Distributed, Embedded and Intelligent Systems)
The Distributed, Embedded and Intelligent Systems research group covers mathematical foundation, verification tools, validation methodologies, probabilistic graphical models and machine learning focusing on distributed, embedded and intelligent systems. This includes the design, implementation and models for the analysis and construction of distributed, embedded and intelligent systems as well as probabilistic models and algorithms for intelligent decision making and machine learning

http://people.cs.aau.dk/~khose/Vacancy_Synergy.html