Page 49 - Contributed Paper Session (CPS) - Volume 6

CPS1490 Nehall Ahmed Farouk Mohamed
learning techniques that are used in big data predictive analytics. At last will
be the result, conclusion, and future.
2.1. Section 1: Big data and official statistics (examples, sources,
applications, and opportunities)
NSOs always seek to enhance and develop its statistical framework and
improve its official statistics mission. Experiences like the analysis of Traffic
Loop Detection Data, the analysis of social media massages, and also the Big
Data Flagship Project in Netherlands and Australia. It is preferred to read (Piet
J.H. Daas and etc. 2015) and (Siu-Ming Tam and Frederic Clarke 2015),
considering the whole levels in these projects in details. According to UN
statistical commission the sources of big data for official statistics are
categorized into 6 categories which are:
1) The administrative records of governmental or private sector, like:
electronic medical records, hospital visits, insurance records and bank
records.
2) Tracking device sources, like: tracking data from mobile telephones and
the Global Positioning System (GPS).
3) Commercial or transactional sources, like: credit card transactions and
online transactions -including transitions from mobile devices - .
4) Sensor networks sources, like: satellite imaging, road sensors and climate
sensors.
5) Opinion data sources, like: comments on social media.
6) Behavioral data sources, like: online searches and online page views.
(S.M. Tam & F. Clarke 2015) discussed each of these sources in depth,
explaining the differences between identified big data sources and
unidentified big data sources. Mentioning identified big data sources might
refers for example to satellite images and unidentified refers to online prices.
Both can be used in official statistics whether combined with data statistical
data or used in statistical calculations.
(Hilbert, M 2016) reviewed empirical evidence and about 180 articles
discussing big data for international development. This paper tries to emerge
official statistical development using big data from the international
development of it. As official statistics main purposes are help decision making
to develop economic sector, agriculture, health, banking, and public services.
So this is considered to be national and internal development aspects. (Hilbert,
M 2016) Focused on the micro level big data application, that aggregates the
macro level development applications, which are: (tracking words-tracking
locations - tracking nature - tracking behavior - tracking production- tracking
transitions -tracing other types).
These applications work on the different types of big data sources which
were mentioned before. In the study of (Kalampokis, Tambouris, & Tarabanis
2013), it showed how big data different sources can help improving several

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