Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.

Scaling XML Using a Beowulf Cluster

We describe a series of experiments which test the performance and scalability of an XML records system deployed on a Beowulf cluster of open source XML databases. Using the open source cityEHR health records system as an example, we first ran experiments to determine the feasibility and optimal size of database instances running on Raspberry Pi and low-cost Intel computers. We describe the implementation of a Data Access Layer for create, read, query and delete operations, using XForms submissions, which encapsulates all database access. We then present the results of testing the scalability and performance of this implementation on clusters of one to sixteen physical database nodes. We conclude that Beowulf clustering provides an effective and cost-efficient mechanism for scaling XML records systems.