Social Computing for Mobile Big Data
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operat...
Ausführliche Beschreibung
Autor*in: |
Xing Zhang [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2016 |
---|
Schlagwörter: |
---|
Systematik: |
|
---|
Übergeordnetes Werk: |
Enthalten in: Computer - Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970, 49(2016), 9, Seite 86-90 |
---|---|
Übergeordnetes Werk: |
volume:49 ; year:2016 ; number:9 ; pages:86-90 |
Links: |
---|
DOI / URN: |
10.1109/MC.2016.267 |
---|
Katalog-ID: |
OLC1981121862 |
---|
LEADER | 01000caa a2200265 4500 | ||
---|---|---|---|
001 | OLC1981121862 | ||
003 | DE-627 | ||
005 | 20230714211047.0 | ||
007 | tu | ||
008 | 161013s2016 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1109/MC.2016.267 |2 doi | |
028 | 5 | 2 | |a PQ20161012 |
035 | |a (DE-627)OLC1981121862 | ||
035 | |a (DE-599)GBVOLC1981121862 | ||
035 | |a (PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950 | ||
035 | |a (KEY)0007916220160000049000900086socialcomputingformobilebigdata | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 004 |q DE-600 |
084 | |a SA 3520 |q AVZ |2 rvk | ||
084 | |a 54.00 |2 bkl | ||
100 | 0 | |a Xing Zhang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Social Computing for Mobile Big Data |
264 | 1 | |c 2016 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
520 | |a Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. | ||
650 | 4 | |a Marketing | |
650 | 4 | |a Social networks | |
650 | 4 | |a Wireless networks | |
650 | 4 | |a Big Data | |
650 | 4 | |a Optimization | |
700 | 0 | |a Zhenglei Yi |4 oth | |
700 | 0 | |a Zhi Yan |4 oth | |
700 | 0 | |a Geyong Min |4 oth | |
700 | 0 | |a Wenbo Wang |4 oth | |
700 | 0 | |a Ahmed Elmokashfi |4 oth | |
700 | 0 | |a Sabita Maharjan |4 oth | |
700 | 0 | |a Yan Zhang |4 oth | |
773 | 0 | 8 | |i Enthalten in |t Computer |d Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970 |g 49(2016), 9, Seite 86-90 |w (DE-627)129296201 |w (DE-600)121237-0 |w (DE-576)014489465 |x 0018-9162 |7 nnns |
773 | 1 | 8 | |g volume:49 |g year:2016 |g number:9 |g pages:86-90 |
856 | 4 | 1 | |u http://dx.doi.org/10.1109/MC.2016.267 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-MAT | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_30 | ||
912 | |a GBV_ILN_32 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_231 | ||
912 | |a GBV_ILN_2002 | ||
912 | |a GBV_ILN_2004 | ||
912 | |a GBV_ILN_2059 | ||
912 | |a GBV_ILN_2111 | ||
936 | r | v | |a SA 3520 |
936 | b | k | |a 54.00 |q AVZ |
951 | |a AR | ||
952 | |d 49 |j 2016 |e 9 |h 86-90 |
author_variant |
x z xz |
---|---|
matchkey_str |
article:00189162:2016----::oiloptnfro |
hierarchy_sort_str |
2016 |
bklnumber |
54.00 |
publishDate |
2016 |
allfields |
10.1109/MC.2016.267 doi PQ20161012 (DE-627)OLC1981121862 (DE-599)GBVOLC1981121862 (PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950 (KEY)0007916220160000049000900086socialcomputingformobilebigdata DE-627 ger DE-627 rakwb eng 004 DE-600 SA 3520 AVZ rvk 54.00 bkl Xing Zhang verfasserin aut Social Computing for Mobile Big Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. Marketing Social networks Wireless networks Big Data Optimization Zhenglei Yi oth Zhi Yan oth Geyong Min oth Wenbo Wang oth Ahmed Elmokashfi oth Sabita Maharjan oth Yan Zhang oth Enthalten in Computer Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970 49(2016), 9, Seite 86-90 (DE-627)129296201 (DE-600)121237-0 (DE-576)014489465 0018-9162 nnns volume:49 year:2016 number:9 pages:86-90 http://dx.doi.org/10.1109/MC.2016.267 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_30 GBV_ILN_32 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2059 GBV_ILN_2111 SA 3520 54.00 AVZ AR 49 2016 9 86-90 |
spelling |
10.1109/MC.2016.267 doi PQ20161012 (DE-627)OLC1981121862 (DE-599)GBVOLC1981121862 (PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950 (KEY)0007916220160000049000900086socialcomputingformobilebigdata DE-627 ger DE-627 rakwb eng 004 DE-600 SA 3520 AVZ rvk 54.00 bkl Xing Zhang verfasserin aut Social Computing for Mobile Big Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. Marketing Social networks Wireless networks Big Data Optimization Zhenglei Yi oth Zhi Yan oth Geyong Min oth Wenbo Wang oth Ahmed Elmokashfi oth Sabita Maharjan oth Yan Zhang oth Enthalten in Computer Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970 49(2016), 9, Seite 86-90 (DE-627)129296201 (DE-600)121237-0 (DE-576)014489465 0018-9162 nnns volume:49 year:2016 number:9 pages:86-90 http://dx.doi.org/10.1109/MC.2016.267 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_30 GBV_ILN_32 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2059 GBV_ILN_2111 SA 3520 54.00 AVZ AR 49 2016 9 86-90 |
allfields_unstemmed |
10.1109/MC.2016.267 doi PQ20161012 (DE-627)OLC1981121862 (DE-599)GBVOLC1981121862 (PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950 (KEY)0007916220160000049000900086socialcomputingformobilebigdata DE-627 ger DE-627 rakwb eng 004 DE-600 SA 3520 AVZ rvk 54.00 bkl Xing Zhang verfasserin aut Social Computing for Mobile Big Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. Marketing Social networks Wireless networks Big Data Optimization Zhenglei Yi oth Zhi Yan oth Geyong Min oth Wenbo Wang oth Ahmed Elmokashfi oth Sabita Maharjan oth Yan Zhang oth Enthalten in Computer Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970 49(2016), 9, Seite 86-90 (DE-627)129296201 (DE-600)121237-0 (DE-576)014489465 0018-9162 nnns volume:49 year:2016 number:9 pages:86-90 http://dx.doi.org/10.1109/MC.2016.267 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_30 GBV_ILN_32 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2059 GBV_ILN_2111 SA 3520 54.00 AVZ AR 49 2016 9 86-90 |
allfieldsGer |
10.1109/MC.2016.267 doi PQ20161012 (DE-627)OLC1981121862 (DE-599)GBVOLC1981121862 (PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950 (KEY)0007916220160000049000900086socialcomputingformobilebigdata DE-627 ger DE-627 rakwb eng 004 DE-600 SA 3520 AVZ rvk 54.00 bkl Xing Zhang verfasserin aut Social Computing for Mobile Big Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. Marketing Social networks Wireless networks Big Data Optimization Zhenglei Yi oth Zhi Yan oth Geyong Min oth Wenbo Wang oth Ahmed Elmokashfi oth Sabita Maharjan oth Yan Zhang oth Enthalten in Computer Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970 49(2016), 9, Seite 86-90 (DE-627)129296201 (DE-600)121237-0 (DE-576)014489465 0018-9162 nnns volume:49 year:2016 number:9 pages:86-90 http://dx.doi.org/10.1109/MC.2016.267 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_30 GBV_ILN_32 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2059 GBV_ILN_2111 SA 3520 54.00 AVZ AR 49 2016 9 86-90 |
allfieldsSound |
10.1109/MC.2016.267 doi PQ20161012 (DE-627)OLC1981121862 (DE-599)GBVOLC1981121862 (PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950 (KEY)0007916220160000049000900086socialcomputingformobilebigdata DE-627 ger DE-627 rakwb eng 004 DE-600 SA 3520 AVZ rvk 54.00 bkl Xing Zhang verfasserin aut Social Computing for Mobile Big Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. Marketing Social networks Wireless networks Big Data Optimization Zhenglei Yi oth Zhi Yan oth Geyong Min oth Wenbo Wang oth Ahmed Elmokashfi oth Sabita Maharjan oth Yan Zhang oth Enthalten in Computer Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970 49(2016), 9, Seite 86-90 (DE-627)129296201 (DE-600)121237-0 (DE-576)014489465 0018-9162 nnns volume:49 year:2016 number:9 pages:86-90 http://dx.doi.org/10.1109/MC.2016.267 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_30 GBV_ILN_32 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2059 GBV_ILN_2111 SA 3520 54.00 AVZ AR 49 2016 9 86-90 |
language |
English |
source |
Enthalten in Computer 49(2016), 9, Seite 86-90 volume:49 year:2016 number:9 pages:86-90 |
sourceStr |
Enthalten in Computer 49(2016), 9, Seite 86-90 volume:49 year:2016 number:9 pages:86-90 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Marketing Social networks Wireless networks Big Data Optimization |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
Computer |
authorswithroles_txt_mv |
Xing Zhang @@aut@@ Zhenglei Yi @@oth@@ Zhi Yan @@oth@@ Geyong Min @@oth@@ Wenbo Wang @@oth@@ Ahmed Elmokashfi @@oth@@ Sabita Maharjan @@oth@@ Yan Zhang @@oth@@ |
publishDateDaySort_date |
2016-01-01T00:00:00Z |
hierarchy_top_id |
129296201 |
dewey-sort |
14 |
id |
OLC1981121862 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1981121862</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714211047.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161013s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/MC.2016.267</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161012</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1981121862</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1981121862</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0007916220160000049000900086socialcomputingformobilebigdata</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 3520</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Xing Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Social Computing for Mobile Big Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Marketing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wireless networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Big Data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhenglei Yi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi Yan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Geyong Min</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenbo Wang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ahmed Elmokashfi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sabita Maharjan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yan Zhang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computer</subfield><subfield code="d">Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970</subfield><subfield code="g">49(2016), 9, Seite 86-90</subfield><subfield code="w">(DE-627)129296201</subfield><subfield code="w">(DE-600)121237-0</subfield><subfield code="w">(DE-576)014489465</subfield><subfield code="x">0018-9162</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:9</subfield><subfield code="g">pages:86-90</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/MC.2016.267</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_231</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2002</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 3520</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">49</subfield><subfield code="j">2016</subfield><subfield code="e">9</subfield><subfield code="h">86-90</subfield></datafield></record></collection>
|
author |
Xing Zhang |
spellingShingle |
Xing Zhang ddc 004 rvk SA 3520 bkl 54.00 misc Marketing misc Social networks misc Wireless networks misc Big Data misc Optimization Social Computing for Mobile Big Data |
authorStr |
Xing Zhang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)129296201 |
format |
Article |
dewey-ones |
004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0018-9162 |
topic_title |
004 DE-600 SA 3520 AVZ rvk 54.00 bkl Social Computing for Mobile Big Data Marketing Social networks Wireless networks Big Data Optimization |
topic |
ddc 004 rvk SA 3520 bkl 54.00 misc Marketing misc Social networks misc Wireless networks misc Big Data misc Optimization |
topic_unstemmed |
ddc 004 rvk SA 3520 bkl 54.00 misc Marketing misc Social networks misc Wireless networks misc Big Data misc Optimization |
topic_browse |
ddc 004 rvk SA 3520 bkl 54.00 misc Marketing misc Social networks misc Wireless networks misc Big Data misc Optimization |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
author2_variant |
z y zy z y zy g m gm w w ww a e ae s m sm y z yz |
hierarchy_parent_title |
Computer |
hierarchy_parent_id |
129296201 |
dewey-tens |
000 - Computer science, knowledge & systems |
hierarchy_top_title |
Computer |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)129296201 (DE-600)121237-0 (DE-576)014489465 |
title |
Social Computing for Mobile Big Data |
ctrlnum |
(DE-627)OLC1981121862 (DE-599)GBVOLC1981121862 (PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950 (KEY)0007916220160000049000900086socialcomputingformobilebigdata |
title_full |
Social Computing for Mobile Big Data |
author_sort |
Xing Zhang |
journal |
Computer |
journalStr |
Computer |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2016 |
contenttype_str_mv |
txt |
container_start_page |
86 |
author_browse |
Xing Zhang |
container_volume |
49 |
class |
004 DE-600 SA 3520 AVZ rvk 54.00 bkl |
format_se |
Aufsätze |
author-letter |
Xing Zhang |
doi_str_mv |
10.1109/MC.2016.267 |
dewey-full |
004 |
title_sort |
social computing for mobile big data |
title_auth |
Social Computing for Mobile Big Data |
abstract |
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. |
abstractGer |
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. |
abstract_unstemmed |
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_30 GBV_ILN_32 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2059 GBV_ILN_2111 |
container_issue |
9 |
title_short |
Social Computing for Mobile Big Data |
url |
http://dx.doi.org/10.1109/MC.2016.267 |
remote_bool |
false |
author2 |
Zhenglei Yi Zhi Yan Geyong Min Wenbo Wang Ahmed Elmokashfi Sabita Maharjan Yan Zhang |
author2Str |
Zhenglei Yi Zhi Yan Geyong Min Wenbo Wang Ahmed Elmokashfi Sabita Maharjan Yan Zhang |
ppnlink |
129296201 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth oth oth oth |
doi_str |
10.1109/MC.2016.267 |
up_date |
2024-07-04T04:18:01.331Z |
_version_ |
1803620640444383232 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1981121862</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714211047.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161013s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/MC.2016.267</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161012</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1981121862</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1981121862</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c897-9775735d8ab99b367790c531e99ca98488035798d167f78dd8d8a003bb6576950</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0007916220160000049000900086socialcomputingformobilebigdata</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 3520</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Xing Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Social Computing for Mobile Big Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Marketing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wireless networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Big Data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhenglei Yi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi Yan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Geyong Min</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenbo Wang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ahmed Elmokashfi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sabita Maharjan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yan Zhang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computer</subfield><subfield code="d">Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970</subfield><subfield code="g">49(2016), 9, Seite 86-90</subfield><subfield code="w">(DE-627)129296201</subfield><subfield code="w">(DE-600)121237-0</subfield><subfield code="w">(DE-576)014489465</subfield><subfield code="x">0018-9162</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:9</subfield><subfield code="g">pages:86-90</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/MC.2016.267</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_231</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2002</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 3520</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">49</subfield><subfield code="j">2016</subfield><subfield code="e">9</subfield><subfield code="h">86-90</subfield></datafield></record></collection>
|
score |
7.4001646 |