"Big Data" and its Origins
Against the background of explosive growth in data volume, velocity, and variety, I investigate the origins of the term "Big Data". Its origins are a bit murky and hence intriguing, involving both academics and industry, statistics and computer science, ultimately winding back to lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s. The Big Data phenomenon continues unabated, and the ongoing development of statistical machine learning tools continues to help us confront it.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Stochastic Models for Replication Origin Spacings in Eukaryotic DNA Replication
We consider eukaryotic DNA replication and in particular the role of replication origins in this process. We focus on origins which are `active' - that is, trigger themselves in the process before being read by the repli…
K-Origins: Better Colour Quantification for Neural Networks
K-Origins is a neural network layer designed to improve image-based network performances when learning colour, or intensities, is beneficial. Over 250 encoder-decoder convolutional networks are trained and tested on 16-b…
Decoderobject-detectionObject DetectionSemantic SegmentationHigh nucleotide skew palindromic DNA sequences function as replication origins due to their unzipping propensity
Locations of DNA replication initiation in prokaryotes, called "origins of replication", are well-characterized. However, a mechanistic understanding of the sequence-dependence of the local unzipping of double-stranded D…
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
The origins of herbal medicines are important for their treatment effect, which could be potentially distinguished by electronic nose system. As the odor fingerprint of herbal medicines from different origins can be tiny…
Dimensionality ReductionClassification of Honey Botanical and Geographical Sources using Mineral Profiles and Machine Learning
This paper proposes a machine learning-based approach for identifying honey floral and geographical sources using mineral element profiles. The proposed method comprises two steps: preprocessing and classification. The p…