EAN: 9783030021009

SPRINGERBRIEFS IN COMPUTER SCIENCE MOBILE DAT...

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.

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