Examples of using Big data sources in English and their translations into Arabic
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(iv) To develop guidelines to classify the various types of big data sources and approaches.
The new big data sources require changes in the organization of data collection and data processing.
Proposals and activities for training, skills and capacity-building required to exploit big data sources for official statistics.
Overall, many countries replied positively with respect to the use of one or more of the big data sources.
For machine learning approaches that attempt to automatically discover natural experiments inside of big data sources, see Jensen et al.
It was suggested that more research is needed to overcome the methodological difficulties impeding the exploitation of big data sources.
For machine learning approaches that attempt to automatically discover natural experiments inside of big data sources, see Jensen et al.
For examples of researchers expressing concern about non-representative nature of big data sources, see boyd and Crawford(2012), K.
The statistical community has started to explore in earnest the opportunities offered by big data sources for application in official statistics.
Two features of big data sources- their always-on nature and their size- greatly enhances our ability to learn from natural experiments when they occur.
Rather than thinking of big data sources as observing people in a natural setting, a more apt metaphor is observing people in a casino.
Applications of various big data sources to a wide range of statistical domains demonstrate that big data truly has the potential to improve official statistics.
This latter team will work, inter alia, on the classification of big data sources, building on that proposed by the ECE big data group.
Automating of input of digit data and wireless transfer to a centralized server are important first steps towards tapping into the potential of big data sources.
(b) To promote practical use of big data sources, including cross-border data, while building on existing precedents and finding solutions for the many existing challenges, including.
Some of the new indicators or proxies of those indicators could be based on big data sources, with improved timeliness and granular social and geo-spatial breakdown.
The potential of big data sources resides in the timely-- and sometimes realtime-- availability of large amounts of data, which are usually generated at minimal cost.
For more on construct validity, see Westen and Rosenthal(2003), and for more on construct validity in big data sources, Lazer(2015) and Chapter 2 of this book.
For more on construct validity, see Westen and Rosenthal(2003), and for more on construct validity in big data sources, Lazer(2015) and Chapter 2 of this book.
(b) Demonstrating the feasibility of efficient production of both novel products and" mainstream" official statistics using big data sources, and the possibility of replicating these approaches across different national contexts;