Temporal biomedical data analytics

A Fuzzy TOPSIS Approach for Big Data Analytics Platform Selection

Big data sizes are constantly increasing. Big data analytics is where advanced analytic techniques are applied on big data sets. Analytics based on large data samples reveals and leverages business change. The popularity of big data analytics platforms, which are often available as open-source, has not remained unnoticed by big companies. Google uses MapReduce for PageRank and inverted indexes....

The STARK Framework for Spatio-Temporal Data Analytics on Spark

Big Data sets can contain all types of information: from server log files to tracking information of mobile users with their location at a point in time. Apache Spark has been widely accepted for Big Data analytics because of its very fast processing model. However, Spark has no native support for spatial or spatio-temporal data. Spatial filters or joins using, e.g., a contains predicate are no...

Spatio-Temporal Big Data Analytics for Environmental Health

The framework for our proposed big data analytics platform is shown in Figure 1. Two complimentary systems support the wide variety of spatial analytics algorithms and techniques we are providing. On the left half of Figure 1, the more-traditional unix filesystem supports high-throughput computation (e.g., MPI [Snir et al., 1995], OpenMP [Dagum and Menon, 1998], GPGPU/CUDA Luebke et al. [2006])...

Interactive Temporal Association Analytics

Traditional temporal association mining systems, once supplied with a specific parameter setting such as time periods of interest, minimum support and confidence, generate the rule set from scratch. This one-at-a-time paradigm forces the analysts to perform successive trial-and-error iterations to finally discover interesting temporal patterns. This process is not only prohibitively time and re...

Uncertainty-aware Visual Analytics for Spatio-temporal Data Exploration