In recent years, real-time processing and analytics systems for big data--in the context of
Business Intelligence (BI)--have received a growing attention. The traditional BI platforms …

The success of companies hugely depends on how well they can analyze the available data
and extract meaningful knowledge. The Extract-Transform-Load (ETL) process is …

Smart meters are increasingly used worldwide. Smart meters are the advanced meters
capable of measuring energy consumption at a fine-grained time interval, eg, every 15 min …

The data warehouse is the most widely used database structure in many decision support
systems around the world. This is the reason why a lot of research has been conducted in …

With the widely used smart meters in the energy sector, anomaly detection becomes a
crucial mean to study the unusual consumption behaviors of customers, and to discover …

Improvement of data generating, processing, storing and networking technologies has made
storing, capturing and sharing of data easier and cheaper than before and has enabled …

There is an abundance of data, but a large volume of it is unusable. Data may be noisy,
unstructured, stored in incompatible for direct analysis medium or format, and often …

Big Data deal with new challenges such as data variety, data veracity (correct, incorrect,
misleading, etc.) and data completeness (provide a single part of the overall information.). In …

Among the so-called “4Vs”(volume, velocity, variety, and veracity) that characterize the
complexity of Big Data, this paper focuses on the issue of “Volume” in order to ensure good …

Big Data emerged after a big explosion of data from the Web 2.0, digital sensors, and social
media applications such as Facebook, Twitter, etc. In this constant growth of data, many …