Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …

There is a great thrust in industry toward the development of more feasible and viable tools
for storing fast-growing volume, velocity, and diversity of data, termed 'big data'. The …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Today's scientific simulations are producing vast volumes of data that cannot be stored and
transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and …

SciDB [4, 3] is a new open-source data management system intended primarily for use in
application domains that involve very large (petabyte) scale array data; for example …

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Modern analytics applications combine multiple functions from different libraries and …

SciDB is an open-source analytical database oriented toward the data management needs
of scientists. As such it mixes statistical and linear algebra operations with data management …

Organizations are often faced with the challenge of providing data management solutions for
large, heterogenous datasets that may have different underlying data and programming …

This paper presents BigDAWG, a reference implementation of a new architecture for “Big
Data” applications. Such applications not only call for large-scale analytics, but also for real …

Modern analytics applications use a diverse mix of libraries and functions. Unfortunately,
there is no optimization across these libraries, resulting in performance penalties as high as …