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Big Data Analysis by Economic Activity, Annual

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Σχετικά με τον Πίνακα

Επικοινωνία

Constantinos Mina, Statistics Officer, Tel.: 22602112, Email: cmina@cystat.mof.gov.cy

Τελευταία Ενημέρωση

06/12/2021

Μονάδα

Number, Percentage

Πνευματική ιδιοκτησία

Ναι

Πηγή

Statistical Service of Cyprus

Κωδικός Πίνακα

1536042E

Now you have come to the page, Choose variable. This page give you the oportunity to select which variables and values you want to display in your result of the table. A variable is a property of a statistical unit. The page is divided into several boxes, one for each variable, where you can select values by click to highlight one or more values. It always starts with the statistics variable which is the main value counted in the table.

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INDICATOR Υποχρεωτικό

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Αρχή λέξης

Field for searching for a specific value in the list box. This is examples of values you can search for.TOTAL ENTERPRISES , Enterprises analysing big data, during the previous year , Enterprises analysed big data (own data) form smart devices or sensors (e.g. Machine to Machine -M2M- communications, digital sensors, Radio frequency identification tags RFID, etc.), during the previous year ,

Επιλεγμένα 0 Σύνολο 29

YEAR Υποχρεωτικό

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Επιλεγμένα 1 Σύνολο 3

MEASURE Υποχρεωτικό

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Επιλεγμένα 0 Σύνολο 2

ECONOMIC ACTIVITY (NACE Rev.2) Υποχρεωτικό

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Field for searching for a specific value in the list box. This is examples of values you can search for.Total (10+) , C , D-E ,

Επιλεγμένα 0 Σύνολο 8

Ο αριθμός των επιλεγμένων κελιών είναι:

(ο μέγιστος επιτρεπόμενος αριθμός είναι 10.000.000)

Η παρουσίαση στην οθόνη περιορίζεται σε 11.000 γραμμές και 30 στήλες

Ο αριθμός των επιλεγμένων κελιών ξεπερνά τον μέγιστο επιτρεπόμενο αριθμό 10.000.000

Υποσημειώσεις

... : Data not available
N.A.: Not applicable
Methodological information
1. Data refers to the survey period, unless otherwise is stated
2. Codes for economic activities NACE Rev.2
NACE C: Manufacturing
NACE D: Electricity, gas, steam and air conditioning supply
NACE E: Water supply; sewerage, waste management and remediation activities
NACE F: Construction
NACE G: Wholesale and retail trade; repair of motor vehicles and motorcycles
NACE H: Transportation and storage
NACE I: Accommodation and food service activities
NACE J: Information and communication
NACE L: Real estate activities
NACE M: Professional, scientific and technical activities
NACE N: Administrative and support service activities
NACE S: Other service activities

TOTAL ENTERPRISES
Enterprises analysing big data, during the previous year
Enterprises analysed big data (own data) form smart devices or sensors (e.g. Machine to Machine -M2M- communications, digital sensors, Radio frequency identification tags RFID, etc.), during the previous year
Enterprises analysed big data (geolocation data) from the use of portable devices (e.g. portable devices using mobile telephone networks, wireless connections or GPS), during the previous year
Enterprises analysed big data from social media (e.g. social networks, blogs, multimedia content sharing websites, etc.), during the previous year
Enterprises analysed big data from other big data sources not specified above (stock index data, transaction data, open web data), during the previous year
Big data analysis for the enterprise was performed by enterprise's own employees, during the previous year
Big data analysis for the enterprise was performed by external service providers, during the previous year
Enterprises performed big data analysis internally from any data source or exrternally, during the previous year
Enterprises performed big data analysis internally on data from smart devices or sensors (e.g. Machine to Machine -M2M- communications, digital sensors, Radio frequency identification tags RFID, etc.), during the previous year
Enterprises perform big data analysis internally on geolocation data from the use of portable devices (e.g. portable devices using mobile telephone networks, wireless connections or GPS), during the previous year
Enterprises performed big data analysis internally on data generated from social media (e.g. social networks, blogs, multimedia content sharing websites, etc.), during the previous year
Enterprises performed big data analysis internally on other big data sources not specified above (stock index data, transaction data, open web data), during the previous year
Enterprises used machine learning (e.g. deep learning) to analyse big data, during the previous year
Enterprises used Natural language processing (NLP), natural language generation( NLG) or speech recognition to analyse big data, during the previous year
Enterprises used other methods of big data analysis, during the previous year
Enterprises having another enterprise or organisation perform big data analysis for them, during the previous year
Enterprises that have considered performing big data analysis
Enterprises that have not performed big data analysis because the costs seemed too high compared to the benefits
Enterprises that have not performed big data analysis due to insufficient human resources, knowledge, skills
Enterprises that have not performed big data analysis due to insufficient sources of big data, either from within or outside your enterprise
Enterprises that have not performed big data analysis due to insufficient ICT infrastructure
Enterprises that have not performed big data analysis due to difficulties in complying with privacy laws
Enterprises that have not performed big data analysis because it is not a priority for the enterprise
Enterprises that have not performed big data analysis due to insufficient quality of the big data source(s)
Enterprises that have not performed big data analysis because big data analysis is not useful for the enterprise
Enterprises that have not performed big data analysis due to other factors
Enterprises that have sold (access to) any of their own big data, during the previous year
Enterprises that have purchased (access to) any big data, during the previous year

Big Data Analysis by Economic Activity, Annual

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