Big Data Reduction Methods: A Survey

Abstract

Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.

An Incremental Kernel Density Estimator for Data Stream Computation

“…estimation problem of large-scale data in the way of data stream computation. In future, we will try to combine the I-KDE with the random sample partition (RSP) model [22,23] of big data and seek the practical applications for the I-KDE, e.g., Bayesian classification, density-based clustering, and big data reduction [24].…”

Big Data Optimization Techniques: A Survey

“…Big data in Telecom: Telecom companies require a proper searching and analysis [15] of data to get deeper understanding into customer behavior, their service usage preferences, patterns and real-time interests. Here is where Big Data comes in.…”

Didžiųjų Duomenų Naudojimas Klientui Pažinti / Model of the Big Data Use for Customer Cognition

“…Ši rizika pasireiškia per 5V modelį, kurį sudaro penkios charakteristikos, apibūdinančios didžiuosius duomenis: kiekį, greitį, įvairovę, teisingumą ir vertę; yra nemažai atvejų, kai žinomi analizės metodai negali būti taikomi apdorojant didelius (kiekis), įvairių formatų (įvairovė) duomenų kiekius per priimtiną laiką (mažas greitis), dėl to gaunami netikslūs rezultatai (teisingumas), pagal kuriuos parengiamos klaidingos prognozės (žema vertė) (Krasnow Waterman ir Bruening, 2014). Siekiant tikslesnių rezultatų ir teisingesnių prognozių, didiesiems duomenims tvarkyti turėtų būti naudojamos specialios sistemos ir algoritmai (ur Rehman et al, 2016), kurie garantuotų greitesnį, patikimesnį kompleksiškų duomenų apdorojimą.…”