scholarly journals BORA: Routing and Aggregation for Distributed Processing of Spatio-Temporal Range Queries

Author(s):  
Goce Trajcevski ◽  
Hui Ding ◽  
Peter Scheuermann ◽  
Isabel F. Cruz
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peter Baumann ◽  
Dimitar Misev ◽  
Vlad Merticariu ◽  
Bang Pham Huu

AbstractMulti-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not all science and engineering domains where they typically represent spatio-temporal sensor, image, simulation output, or statistics “datacubes”. As classic database technology does not support arrays adequately, such data today are maintained mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for flexible ad-hoc analytics on large n-D arrays, similar to what SQL offers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison differentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the field; for example, subsetting tests were designed in a way that systems cannot be tuned to specifically these queries. It is hoped that this gives a representative overview to all who want to immerse into the field as well as a clear guidance to those who need to choose the best suited datacube tool for their application. This article presents results of the Research Data Alliance (RDA) Array Database Assessment Working Group (ADA:WG), a subgroup of the Big Data Interest Group. It has elicited the state of the art in Array Databases, technically supported by IEEE GRSS and CODATA Germany, to answer the question: how can data scientists and engineers benefit from Array Database technology? As it turns out, Array Databases can offer significant advantages in terms of flexibility, functionality, extensibility, as well as performance and scalability—in total, the database approach of offering “datacubes” analysis-ready heralds a new level of service quality. Investigation shows that there is a lively ecosystem of technology with increasing uptake, and proven array analytics standards are in place. Consequently, such approaches have to be considered a serious option for datacube services in science, engineering and beyond. Tools, though, vary greatly in functionality and performance as it turns out.


2021 ◽  
Author(s):  
Rico Stecher ◽  
Ilkka Muukkonen ◽  
Viljami R Salmela ◽  
Sophie-Marie Rostalski ◽  
Géza Gergely Ambrus ◽  
...  

The recognition of facial identity is essential for social interactions. Despite extensive prior fMRI and EEG/MEG research on the neural representations of familiar faces, we know little about the spatio-temporal dynamics of face identity information. Therefore, we applied a novel multimodal approach, by fusioning the neuronal responses recorded in an fMRI and an EEG experiment. We analyzed the neural responses to naturally varying famous faces and traced how face identity emerges over time in different areas of the brain. We found that image invariant face identity information prevails over an extended time period (from 150 to 810 ms after stimulus onset) in the representational geometry of a broadly distributed network of parietal, temporal, and frontal areas with overlapping temporal profiles. These results challenge the current hierarchical models of face perception and suggest instead concerted and parallel activation of multiple nodes in the brain's identity coding network while processing information of familiar faces.


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