Big Data in Complex Systems

Abstract

The series "Studies in Big Data" (SBD) publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.More information about this series at

Using distribution analysis for parameter selection in repstream

“…To evaluate our method's efficacy we will examine external validation metrics, specifically the purity and the F -measure scores. External validation metrics are commonly used [14,15] to evaluate the performance of clustering methods in an objective sense against the stated ideal clustering, which is represented by labels for each data point, showing which belong together and which should belong in different clusters.…”

“…For these evaluations we use F -measure as our external validation metric. One major problem with purity, as noted in [14], is that purity is an unreliable indicator of performance, despite its popularity as a comparison tool between clustering algorithms. Purity has the problem has no penalty for producing too many clusters, and splitting classes up into multiple clusters.…”

“…A data stream is defined as a set of d-dimensional data points x 1 , x 2 , ..., x m ... arriving at time stamps t 1 , t 2 , ..., t m ... where a data point x i = [x 1 i , ...x d i ] is a ddimensional vector [14]. The data stream is potentially unlimited in length and data points are sampled in an unpredictable way from a data distribution that can change and shift over time.…”

Design of a clinical database to support research purposes: Challenges and solutions

“…A data storage system contains two parts: A hardware infrastructure in the lower layer and storage methods or mechanisms on the top layer. The hardware infrastructure is a combination of both hardware equipment such as servers, routers, network links, and software components such as operating systems (Hassanien et al, 2015). In general, data storage systems must be equipped with multiple application programming interfaces (APIs), rapid query, or other software models for analyzing or interacting with data in the physical layer .…”

“…MapReduce is a powerful programming model for large-scale applications that uses a simple technique that emerged from those used in the area of distributed databases (Hassanien et al, 2015). MapReduce is a parallel programming framework developed by Google based on GFS for global analysis in big data (Blanke, 2014).…”