The eponymous social network has been at the center of a privacy storm this year. And every fresh Facebook content concern — be it about discrimination or hate speech or cultural insensitivity — adds to a damaging flood.
Advanced Search Abstract Big Data bring new opportunities to modern society and challenges to data scientists.
On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors.
These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures.
We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity.
They can lead to wrong statistical inferences and consequently wrong scientific conclusions.
What is new about Big Data and how they differ from the traditional small- or medium-scale data? This paper overviews the opportunities and challenges brought by Big Data, with emphasis on the distinguished features of Big Data and statistical and computational methods as well as computing architecture to deal with them.
Such a Big Data movement is driven by the fact that massive amounts of very high-dimensional or unstructured data are continuously produced and stored with much cheaper cost than they used to be.
For example, in genomics we have seen a dramatic drop in price for whole genome sequencing [ 1 ]. This is also true in other areas such as social media analysis, biomedical imaging, high-frequency finance, analysis of surveillance videos and retail sales.
The existing trend that data can be produced and stored more massively and cheaply is likely to maintain or even accelerate in the future [ 2 ]. This trend will have deep impact on science, engineering and business.
For example, scientific advances are becoming more and more data-driven and researchers will more and more think of themselves as consumers of data. The massive amounts of high-dimensional data bring both opportunities and new challenges to data analysis.
Valid statistical analysis for Big Data is becoming increasingly important. According to [ 3 ], two main goals of high-dimensional data analysis are to develop effective methods that can accurately predict the future observations and at the same time to gain insight into the relationship between the features and response for scientific purposes.
Furthermore, due to large sample size, Big Data give rise to two additional goals: In other words, Big Data give promises for: What are the challenges of analyzing Big Data? Big Data are characterized by high dimensionality and large sample size. These two features raise three unique challenges: This creates issues of heterogeneity, experimental variations and statistical biases, and requires us to develop more adaptive and robust procedures.The purpose of this online discussion is to take a broad look at the opportunities and challenges of using open data, visualisation, and other technology-based approaches to making data .
Oct 27, · The growing public and political alarm over how big data platforms stoke addiction and exploit people’s trust and information — and the idea that an overarching framework of not just laws but digital ethics might be needed to control this stuff — dovetails neatly with the alternative track that Apple has been pounding for years.
Exploring data: Graphs and numerical summaries Histograms It is a fundamental principle in modern practical data analysis that all investigations should begin, wherever possible, with one or more suitable diagrams of the data.
In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.
To design effective statistical procedures for exploring and predicting Big Data, we need to address Big Data problems such as heterogeneity, noise accumulation, spurious correlations and incidental endorgeneity, in addition to balancing the statistical accuracy and computational efficiency.
What is the consequence of this endogeneity?
The. Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors ( ): “In the age of big data the emphasis in industry has shifted to data analysis and rapid business decision making based on huge volumes of information”.
the consequence of these attitudinal aspects, correctly.