Due to the falling costs of data acquisition and storage, researchers and industry analysts
often want to find all instances of rare events in large datasets. For instance, scientists can …

As data analytics becomes more crucial to digital systems, so grows the importance of
characterizing the database queries that admit a more efficient evaluation. We consider the …

This paper overviews an approach that addresses machine learning over relational data as
a database problem. This is justified by two observations. First, the input to the learning task …

In this paper, we present a middleware framework that runs on top of a SQL data system with
the purpose of increasing the interactivity of geospatial visualization dashboards. The …

Various sources such as daily business operations and sensors from different IoT
applications constantly generate a lot of data. They are often loaded into a data warehouse …

Adaptive join algorithms have recently attracted a lot of attention in emerging applications
that provide data through autonomous data sources in diverse network environments. Their …

Join is arguably the most costly and frequently used operation in relational query
processing. Join algorithms usually spend the majority of their time on scanning and …

In the context of Big Data, complex data from heterogeneous and distributed sources is
potentially unlimited in number. The analysis of these data is now at the center of the …

Massive growth in the big data makes difficult to analyse and retrieve the useful information
from the set of available data's. Existing approaches cannot guarantee an efficient retrieval …

Huge amounts of georeferenced data streams are arriving daily to data stream management
systems that are deployed for serving highly scalable and dynamic applications. There are …