With the rapidly decreasing prices for storage and storage systems ever larger data sets
become economical. While only few years ago only successful transactions would be …

Extracting top-k keywords and documents using weighting schemes are popular techniques
employed in text mining and machine learning for different analysis and retrieval tasks. The …

For getting up-to-date insight into online services, extracted data has to be processed in
near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) …

Sheer increase in volume of data over the last decade has triggered research in cluster
computing frameworks that enable web enterprises to extract big insights from big data …

The discovering of patterns regarding how, when, and where users interact with mobile
applications reveals important insights for mobile service providers. In this work, we exploit …

Obtaining the right set of data for evaluating the fulfillment of different quality factors in the
extract-transform-load (ETL) process design is rather challenging. First, the real data might …

For the architecture community, reasonable simulation time is a strong requirement in
addition to performance data accuracy. However, emerging big data and AI workloads are …

While cluster computing frameworks are continuously evolving to provide real-time data
analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics …

Natural language is a common type of input for data processing systems. Therefore, it is
often required to have a large testing data set of this type. In this context, the task to …