Mobile Data Mining

Yuan Yao

ISBN 10: 3030021009 / ISBN 13: 9783030021009

Editore: Springer International Publishing Nov 2018, 2018

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This item is printed on demand - it takes 3-4 days longer - Neuware -This SpringerBrief presents a typical life-cycle of mobile data mining applications,including:data capturing and processing which determines what data tocollect, how to collect these data, and how to reduce the noise in the databased on smartphone sensorsfeature engineering which extracts andselects features to serve as the input of algorithms based on the collectedand processed datamodel and algorithm designIn particular, this brief concentrateson the model and algorithm design aspect, and explains three challenging requirementsof mobile data mining applications: energy-saving, personalization, and real-timeEnergy saving is a fundamental requirement of mobile applications, due to thelimited battery capacity of smartphones. The authors explore the existingpractices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobileapplications is personalization. Most of the existing methods tend to train generic models for all users, but the authors provide existing personalizedtreatments for mobile applications, as the behaviors may differ greatly fromone user to another in many mobile applications. The third requirement isreal-time. That is, the mobile application should return responses in a real-timemanner, meanwhile balancing effectiveness and efficiency.This SpringerBrief targets data mining and machine learning researchers and practitionersworking in these related fields. Advanced level students studying computer scienceand electrical engineering will also find this brief useful as a study guide. 68 pp. Englisch.

Codice articolo 9783030021009

Dati bibliografici

• Titolo: Mobile Data Mining
• Editore: Springer International Publishing Nov 2018
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3030021009
• ISBN 13: 9783030021009
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 4 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 4 millimetri di profondità
• Peso: 134 grammi

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Riassunto

This SpringerBrief presents a typical life-cycle of mobile data mining applications, including:

• data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensors
  
•  feature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed data
  
•  model and algorithm design

In particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-time

 Energy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors  explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization.  Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency.

 This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide.