Financial firms commonly process and store billions of time-series data, generated
continuously and at a high frequency. To support efficient data storage and retrieval …

The digital transition is one of the biggest challenges of the new millennium. One of the key
drivers of this transition is the need to adapt to the rapidly changing and heterogeneous …

La variabilidad climática ha aumentado la necesidad de sistemas de monitoreo atmosférico
a nivel local. Los datos meteorológicos son útiles para predicción climática, agricultura y …

For efficient flight testing the data acquisition, reliable storage and ability to timely review the
recorded test data are essential. With modern sensor suites and electronics the data …

This chapter discusses the revolutionary incorporation of machine learning (ML) with
industrial loT. Beginning by defining lloT, history, architecture, and applications. Further …

As the complexity of modern software systems continues to grow, observability and
performance monitoring have become integral aspects of system design and operation …

Dentro do mundo da energia renovável, a interseção com a inteligência artificial (IA) e
machine learning (ML) é fundamental para otimizar a eficiência na gestão de energia ea …

O objetivo deste trabalho é avaliar a performance de SGBDs na inserção e consulta de
dados de séries temporais procurando identificar quando Bancos de Dados de Séries …

Las soluciones tradicionales de monitoreo meteorológico suelen ser costosas y
generalmente se encuentran limitadas a grandes organizaciones. Esto deja a muchas …

Abstract Internet of Things (IoT) systems generate massive amounts of time series data that
need to be stored for historical analysis. As a result, Database Management Systems …