http://iet.metastore.ingenta.com
1887

Real-time optimal route recommendations using MapReduce

Real-time optimal route recommendations using MapReduce

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Big Data Recommender Systems - Volume 2: Application Paradigms — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

To avoid complications related to the decision-making process, recommendation systems are introduced to suggest a ranked list of items which most meet special user's requirements. One of the useful types of Recommendation Systems is Route Recommendation System (RRS). The Route Recommendation apps provide a variety of services for their users. Some of these services are beating the traffic, finding the new and ideal route that depends on roads condition, aiding disabled people to find their destination independently, guiding strangers such as tourists in an unfamiliar area, leading pedestrian in emergency, etc. In this chapter, we will present an overview of RRSs and their details. After presenting the basic concepts, we can classify them based on services which they provide. Besides, we are going to discuss about the input data and answer the question “Why it is big?” Our aim is to provide you with a layered architecture of RRSs which can deal with such big data and also be able to serve optimal real-time recommendation. In order to achieve our purpose, the big data technologies mapped to each layer are introduced. Moreover, we will set up a brief discussion about MapReduce paradigm and its strengths as one of the techniques to make parallel computation possible.

Chapter Contents:

  • 17.1 Introduction
  • 17.2 An overview of RRSs
  • 17.2.1 Recommendation Systems
  • 17.2.2 Route Recommendation Systems
  • 17.2.3 Classification of RRSs
  • 17.2.3.1 RRS for individual drivers
  • 17.2.3.2 RRS for pedestrians
  • 17.2.3.3 RRS for riding bicycle
  • 17.2.3.4 RRS for disabled people
  • 17.2.3.5 RRSs for taxi drivers to pick up passengers
  • 17.2.3.6 RRS for passengers
  • 17.2.3.7 More gentle RRSs for senior citizens
  • 17.2.3.8 RRS for sailing
  • 17.3 The requirements for RRS
  • 17.3.1 Data requirements
  • 17.3.2 Big or small Data?
  • 17.3.3 Real-time issue
  • 17.3.4 An architecture
  • 17.3.4.1 MapReduce
  • 17.3.5 The categories of requirements from another perspective
  • 17.4 Summary
  • References

Inspec keywords: parallel processing; Big Data; real-time systems; decision making; recommender systems; traffic engineering computing

Other keywords: road condition; real-time optimal route recommendations; RSS layered architecture; route recommendation system; MapReduce; recommendation systems; parallel computation; optimal real-time recommendation; Big Data; decision-making process; ranked list; route recommendation apps; user requirements

Subjects: Search engines; Data handling techniques; Traffic engineering computing; Parallel software

Preview this chapter:
Zoom in
Zoomout

Real-time optimal route recommendations using MapReduce, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch17-1.gif /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch17-2.gif

Related content

content/books/10.1049/pbpc035g_ch17
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address