The collection of time series data increases as more monitoring and automation are being
deployed. These deployments range in scale from an Internet of things (IoT) device located …

In this paper, we present the design, implementation, and evaluation of a system that
executes complex event queries over real-time streams of RFID readings encoded as …

Advances in data acquisition and sensor technologies are leading towards the development
of “high fan-in” architectures: widely distributed systems whose edges consist of numerous …

Massive data series collections are becoming a reality for virtually every scientific and social
domain, and have to be processed and analyzed, in order to extract useful knowledge …

This experience paper summarizes the key lessons we learned throughout the design and
implementation of the Aurora stream-processing engine. For the past 2 years, we have built …

Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of
experimental data using database technologies. To bridge the gap between the needs of the …

Scientific applications are still poorly served by contemporary relational database systems.
At best, the system provides a bridge towards an external library using user-defined …

Data and metadata in datasets experience many different kinds of change. Values are
inserted, deleted or updated; rows appear and disappear; columns are added or …

Since scientific investigation is one of the most important providers of massive amounts of
ordered data, there is a renewed interest in array data processing in the context of Big Data …

There is an increasingly pressing need, by several applications in diverse domains, for
developing techniques able to index and mine very large collections of sequences, or data …