國立高雄大學圖資館 |

Language: English

Back

Mobile data mining

SpringerLink (Online service)

Mobile data mining

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mobile data miningby Yuan Yao, Xing Su, Hanghang Tong.
Author:	Yao, Yuan.
other author:	Su, Xing.
Published:	Cham :Springer International Publishing :2018.
Description:	ix, 58 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-3-030-02101-6
ISBN:	9783030021016$q(electronic bk.)

Mobile data mining
Yao, Yuan.

Mobile data mining[electronic resource] /by Yuan Yao, Xing Su, Hanghang Tong. - Cham :Springer International Publishing :2018. - ix, 58 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

1 Introduction -- 2 Data Capturing and Processing -- 3 Feature Engineering -- 4 Hierarchical Model -- 5 Personalized Model -- 6 Online Model -- 7 Conclusions.

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.

ISBN: 9783030021016$q(electronic bk.)

Standard No.: 10.1007/978-3-030-02101-6doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Mobile data mining
LDR:02765nmm a2200337 a 4500 001 545586
003 DE-He213
005 20181031141544.0
006 m d
007 cr nn 008maaau
008 190530s2018 gw s 0 eng d
020 $a 9783030021016$q(electronic bk.)
020 $a 9783030021009$q(paper)
024 7 $a 10.1007/978-3-030-02101-6 $2 doi
035 $a 978-3-030-02101-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UT $2 bicssc
072 7 $a COM069000 $2 bisacsh
072 7 $a UT $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b Y25 2018
100 1 $a Yao, Yuan. $3 708537
245 1 0 $a Mobile data mining $h [electronic resource] / $c by Yuan Yao, Xing Su, Hanghang Tong.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a ix, 58 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
505 0 $a 1 Introduction -- 2 Data Capturing and Processing -- 3 Feature Engineering -- 4 Hierarchical Model -- 5 Personalized Model -- 6 Online Model -- 7 Conclusions.
520 $a 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.
650 0 $a Data mining. $3 184440
650 0 $a Mobile computing. $3 185560
650 0 $a Computational intelligence. $3 210824
650 1 4 $a Information Systems and Communication Service. $3 274025
650 2 4 $a Computer Communication Networks. $3 218087
700 1 $a Su, Xing. $3 824584
700 1 $a Tong, Hanghang. $3 824585
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in computer science. $3 559641
856 4 0 $u https://doi.org/10.1007/978-3-030-02101-6
950 $a Computer Science (Springer-11645)