The proposed method is made by considering following scenario under consideration An Airport has huge amount of data related to number of flights, data and time of arrival and dispatch, flight routes, No. of airports operating in each country, list of active airlines in each country. The problem they faced till now it’s, they have ability to analyze limited data from databases. The Proposed model intension is to develop a model for the airline data to provide platform for new analytics based on the following queries.
Data Description
In this use case there are 3 data sets. Final_airlines, routes.dat, airports_mod.dat
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*Air Ports data set i.e airports_mod.dat*
It contains the following fields
Airport ID Unique OpenFlights identifier for this airport.
Name Name of airport. May or may not contain the City name.
City Main city served by airport. May be spelled differently from Name.
Country Country or territory where airport is located.
IATA/FAA 3-letter FAA code, for airports located in Country “United States of America”.
3-letter IATA code, for all other airports.
Blank if not assigned.
ICAO 4-letter ICAO code.
Blank if not assigned.
Latitude Decimal degrees, usually to six significant digits. Negative is South, positive is North.
Longitude Decimal degrees, usually to six significant digits. Negative is West, positive is East.
Altitude In feet.
Timezone Hours offset from UTC. Fractional hours are expressed as decimals, eg. India is 5.5.
DST Daylight savings time. One of E (Europe), A (US/Canada), S (South America), O (Australia), Z (New Zealand), N (None) or U (Unknown). See also: Help: Time
Tz database time Timezone in “tz” (Olson) format, eg. “America/Los_Angeles”. zone
*Air Lines Data set:*
It contains the following fields:
Airline Unique OpenFlights identifier for this airline. ID
Name Name of the airline.
Alias Alias of the airline. For example, All Nippon Airways is commonly known as “ANA”.
IATA 2-letter IATA code, if available.
ICAO 3-letter ICAO code, if available.
Callsign Airline callsign.
Country Country or territory where airline is incorporated.
Active “Y” if the airline is or has until recently been operational, “N” if it is defunct. This field is not reliable: in particular, major airlines that stopped flying long ago, but have not had their IATA code reassigned (eg. Ansett/AN), will incorrectly show as “Y”
*Routes Data set i.e routes.dat*
It contains the following fields
Airline 2-letter (IATA) or 3-letter (ICAO) code of the airline.
Airline ID Unique OpenFlights identifier for airline (see Airline).
Source airport 3-letter (IATA) or 4-letter (ICAO) code of the source airport.
Source airport ID Unique OpenFlights identifier for source airport (see Airport)
Destination airport 3-letter (IATA) or 4-letter (ICAO) code of the destination airport.
Destination airport ID Unique OpenFlights identifier for destination airport (see Airport)
Codeshare “Y” if this flight is a codeshare (that is, not operated by Airline, but another carrier), empty otherwise.
Stops Number of stops on this flight (“0” for direct)
Equipment 3-letter codes for plane type(s) generally used on this flight, separated by spaces
CONCLUSION
This paper addresses the related work of distributed data bases that were found in literature, challenges ahead with big data, and a case study on airline data analysis using Hive. Author attempted to explore detailed analysis on airline data sets such as listing airports operating in the India, list of airlines having zero stops, list of airlines operating with code share which country has highest airports and list of active airlines in united state. Here author focused on the processing the big data sets using hive component of hadoop ecosystem in distributed environment. This work will benefit the developers and business analysts in accessing and processing their user queries.