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Customized Travel Planner using MapReduce and Approximation Algorithm

by Pallavi S Ghogare, Harmeet K. Khanuja
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 23
Year of Publication: 2015
Authors: Pallavi S Ghogare, Harmeet K. Khanuja
10.5120/21378-4294

Pallavi S Ghogare, Harmeet K. Khanuja . Customized Travel Planner using MapReduce and Approximation Algorithm. International Journal of Computer Applications. 119, 23 ( June 2015), 26-29. DOI=10.5120/21378-4294

@article{ 10.5120/21378-4294,
author = { Pallavi S Ghogare, Harmeet K. Khanuja },
title = { Customized Travel Planner using MapReduce and Approximation Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 23 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number23/21378-4294/ },
doi = { 10.5120/21378-4294 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:04:51.454371+05:30
%A Pallavi S Ghogare
%A Harmeet K. Khanuja
%T Customized Travel Planner using MapReduce and Approximation Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 23
%P 26-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is fun to travel but painful to arrange the trip. When travelers start off planning they need flights, accommodation and attractions. Which scattered across multiple websites on the internet? Traveler spends time scouting each of them for the best deals, and gets the attraction reviews from established planners in the market. It will be always good if traveler gives specified designations and time he wants to spend for the trip and some platform will automatically did everything for traveler with added bonus of optimal and customized itineraries. This system is designed for such travelers to design customized itineraries which will be optimal and consist of Point-Of-Interest (POI) selected by traveler, rather than go and visit the traditional and static trip plans by many travel agencies. This system is two-stage processing system for cost effective and optimal results. First is preprocessing stage works offline uses parallel processing engine as MapReduce to precompute Single-Day Itineraries. In further stage which is online the precomputed itineraries are combined to give multiday itineraries. These itineraries produced are optimal as per travelers selected Point-Of-Interest (POI). Here Greedy Approximation Algorithm is used to combine the single day itineraries. In this way Team-Orienteering-Problem (TOP) is transferred to Set-Packing Problem another NP-complete problem.

References
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Index Terms

Computer Science
Information Sciences

Keywords

MapReduce team-orienteering-Problem Itinerary Point-Of-Interest location-based service.

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