Multimedia services placement algorithm for cloud–fog hierarchical environments
With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Comp...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Santos, Fillipe [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
14 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data - Chown, Justin ELSEVIER, 2016, the international journal for the computer and telecommunications industry, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:191 ; year:2022 ; day:1 ; month:07 ; pages:78-91 ; extent:14 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.comcom.2022.04.009 |
|---|
| Katalog-ID: |
ELV058021566 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV058021566 | ||
| 003 | DE-627 | ||
| 005 | 20230626050140.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.comcom.2022.04.009 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica |
| 035 | |a (DE-627)ELV058021566 | ||
| 035 | |a (ELSEVIER)S0140-3664(22)00113-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |q VZ |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.87 |2 bkl | ||
| 100 | 1 | |a Santos, Fillipe |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multimedia services placement algorithm for cloud–fog hierarchical environments |
| 264 | 1 | |c 2022transfer abstract | |
| 300 | |a 14 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. | ||
| 520 | |a With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. | ||
| 650 | 7 | |a Multimedia services |2 Elsevier | |
| 650 | 7 | |a Placement strategies |2 Elsevier | |
| 650 | 7 | |a Cloud-to-fog networks |2 Elsevier | |
| 700 | 1 | |a Immich, Roger |4 oth | |
| 700 | 1 | |a Madeira, Edmundo R.M. |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Chown, Justin ELSEVIER |t Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data |d 2016 |d the international journal for the computer and telecommunications industry |g Amsterdam [u.a.] |w (DE-627)ELV019098014 |
| 773 | 1 | 8 | |g volume:191 |g year:2022 |g day:1 |g month:07 |g pages:78-91 |g extent:14 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.comcom.2022.04.009 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 44.87 |j Gastroenterologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 191 |j 2022 |b 1 |c 0701 |h 78-91 |g 14 | ||
| author_variant |
f s fs |
|---|---|
| matchkey_str |
santosfillipeimmichrogermadeiraedmundorm:2022----:utmdaevcslcmnagrtmocodohe |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
44.87 |
| publishDate |
2022 |
| allfields |
10.1016/j.comcom.2022.04.009 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica (DE-627)ELV058021566 (ELSEVIER)S0140-3664(22)00113-X DE-627 ger DE-627 rakwb eng 510 VZ 610 VZ 44.87 bkl Santos, Fillipe verfasserin aut Multimedia services placement algorithm for cloud–fog hierarchical environments 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks Elsevier Immich, Roger oth Madeira, Edmundo R.M. oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:191 year:2022 day:1 month:07 pages:78-91 extent:14 https://doi.org/10.1016/j.comcom.2022.04.009 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 191 2022 1 0701 78-91 14 |
| spelling |
10.1016/j.comcom.2022.04.009 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica (DE-627)ELV058021566 (ELSEVIER)S0140-3664(22)00113-X DE-627 ger DE-627 rakwb eng 510 VZ 610 VZ 44.87 bkl Santos, Fillipe verfasserin aut Multimedia services placement algorithm for cloud–fog hierarchical environments 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks Elsevier Immich, Roger oth Madeira, Edmundo R.M. oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:191 year:2022 day:1 month:07 pages:78-91 extent:14 https://doi.org/10.1016/j.comcom.2022.04.009 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 191 2022 1 0701 78-91 14 |
| allfields_unstemmed |
10.1016/j.comcom.2022.04.009 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica (DE-627)ELV058021566 (ELSEVIER)S0140-3664(22)00113-X DE-627 ger DE-627 rakwb eng 510 VZ 610 VZ 44.87 bkl Santos, Fillipe verfasserin aut Multimedia services placement algorithm for cloud–fog hierarchical environments 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks Elsevier Immich, Roger oth Madeira, Edmundo R.M. oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:191 year:2022 day:1 month:07 pages:78-91 extent:14 https://doi.org/10.1016/j.comcom.2022.04.009 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 191 2022 1 0701 78-91 14 |
| allfieldsGer |
10.1016/j.comcom.2022.04.009 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica (DE-627)ELV058021566 (ELSEVIER)S0140-3664(22)00113-X DE-627 ger DE-627 rakwb eng 510 VZ 610 VZ 44.87 bkl Santos, Fillipe verfasserin aut Multimedia services placement algorithm for cloud–fog hierarchical environments 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks Elsevier Immich, Roger oth Madeira, Edmundo R.M. oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:191 year:2022 day:1 month:07 pages:78-91 extent:14 https://doi.org/10.1016/j.comcom.2022.04.009 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 191 2022 1 0701 78-91 14 |
| allfieldsSound |
10.1016/j.comcom.2022.04.009 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica (DE-627)ELV058021566 (ELSEVIER)S0140-3664(22)00113-X DE-627 ger DE-627 rakwb eng 510 VZ 610 VZ 44.87 bkl Santos, Fillipe verfasserin aut Multimedia services placement algorithm for cloud–fog hierarchical environments 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks Elsevier Immich, Roger oth Madeira, Edmundo R.M. oth Enthalten in Elsevier Science Chown, Justin ELSEVIER Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data 2016 the international journal for the computer and telecommunications industry Amsterdam [u.a.] (DE-627)ELV019098014 volume:191 year:2022 day:1 month:07 pages:78-91 extent:14 https://doi.org/10.1016/j.comcom.2022.04.009 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 191 2022 1 0701 78-91 14 |
| language |
English |
| source |
Enthalten in Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data Amsterdam [u.a.] volume:191 year:2022 day:1 month:07 pages:78-91 extent:14 |
| sourceStr |
Enthalten in Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data Amsterdam [u.a.] volume:191 year:2022 day:1 month:07 pages:78-91 extent:14 |
| format_phy_str_mv |
Article |
| bklname |
Gastroenterologie |
| institution |
findex.gbv.de |
| topic_facet |
Multimedia services Placement strategies Cloud-to-fog networks |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data |
| authorswithroles_txt_mv |
Santos, Fillipe @@aut@@ Immich, Roger @@oth@@ Madeira, Edmundo R.M. @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV019098014 |
| dewey-sort |
3510 |
| id |
ELV058021566 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058021566</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626050140.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.comcom.2022.04.009</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058021566</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0140-3664(22)00113-X</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">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Santos, Fillipe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multimedia services placement algorithm for cloud–fog hierarchical environments</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">14</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multimedia services</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Placement strategies</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Cloud-to-fog networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Immich, Roger</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Madeira, Edmundo R.M.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Chown, Justin ELSEVIER</subfield><subfield code="t">Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data</subfield><subfield code="d">2016</subfield><subfield code="d">the international journal for the computer and telecommunications industry</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV019098014</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:191</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:1</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:78-91</subfield><subfield code="g">extent:14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.comcom.2022.04.009</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.87</subfield><subfield code="j">Gastroenterologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">191</subfield><subfield code="j">2022</subfield><subfield code="b">1</subfield><subfield code="c">0701</subfield><subfield code="h">78-91</subfield><subfield code="g">14</subfield></datafield></record></collection>
|
| author |
Santos, Fillipe |
| spellingShingle |
Santos, Fillipe ddc 510 ddc 610 bkl 44.87 Elsevier Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks Multimedia services placement algorithm for cloud–fog hierarchical environments |
| authorStr |
Santos, Fillipe |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV019098014 |
| format |
electronic Article |
| dewey-ones |
510 - Mathematics 610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
510 VZ 610 VZ 44.87 bkl Multimedia services placement algorithm for cloud–fog hierarchical environments Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks Elsevier |
| topic |
ddc 510 ddc 610 bkl 44.87 Elsevier Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks |
| topic_unstemmed |
ddc 510 ddc 610 bkl 44.87 Elsevier Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks |
| topic_browse |
ddc 510 ddc 610 bkl 44.87 Elsevier Multimedia services Elsevier Placement strategies Elsevier Cloud-to-fog networks |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
r i ri e r m er erm |
| hierarchy_parent_title |
Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data |
| hierarchy_parent_id |
ELV019098014 |
| dewey-tens |
510 - Mathematics 610 - Medicine & health |
| hierarchy_top_title |
Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV019098014 |
| title |
Multimedia services placement algorithm for cloud–fog hierarchical environments |
| ctrlnum |
(DE-627)ELV058021566 (ELSEVIER)S0140-3664(22)00113-X |
| title_full |
Multimedia services placement algorithm for cloud–fog hierarchical environments |
| author_sort |
Santos, Fillipe |
| journal |
Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data |
| journalStr |
Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
78 |
| author_browse |
Santos, Fillipe |
| container_volume |
191 |
| physical |
14 |
| class |
510 VZ 610 VZ 44.87 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Santos, Fillipe |
| doi_str_mv |
10.1016/j.comcom.2022.04.009 |
| dewey-full |
510 610 |
| title_sort |
multimedia services placement algorithm for cloud–fog hierarchical environments |
| title_auth |
Multimedia services placement algorithm for cloud–fog hierarchical environments |
| abstract |
With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. |
| abstractGer |
With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. |
| abstract_unstemmed |
With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Multimedia services placement algorithm for cloud–fog hierarchical environments |
| url |
https://doi.org/10.1016/j.comcom.2022.04.009 |
| remote_bool |
true |
| author2 |
Immich, Roger Madeira, Edmundo R.M. |
| author2Str |
Immich, Roger Madeira, Edmundo R.M. |
| ppnlink |
ELV019098014 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.comcom.2022.04.009 |
| up_date |
2024-07-06T17:50:07.444Z |
| _version_ |
1803852927410896896 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058021566</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626050140.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.comcom.2022.04.009</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001799.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058021566</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0140-3664(22)00113-X</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">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Santos, Fillipe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multimedia services placement algorithm for cloud–fog hierarchical environments</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">14</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">With the rapid development of mobile communication, multimedia services have experienced explosive growth in the last few years. The high quantity of mobile users, both consuming and producing these services to and from the Cloud Computing (CC), can outpace the available bandwidth capacity. Fog Computing (FG) presents itself as a solution to improve on this and other issues. With a reduction in network latency, real-time applications benefit from improved response time and greater overall user experience. Taking this into account, the main goal of this work is threefold. Firstly, it is proposed a method to build an environment based on Cloud–Fog Computing (CFC). Secondly, it is designed two models based on Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve the nodes’ storage capacity to improve the positioning of multimedia services. Later, an algorithm for the multimedia service placement problem which is aware of data traffic prediction is proposed. The goal is to select the minimum number of nodes, considering their hardware capacities for providing multimedia services in such a way that the latency for servicing all the demands is minimized. An evaluation with actual data showed that the proposed algorithm selects the nodes closer to the user to meet their demands. This improves the services delivered to end-users and enhances the deployed network to mitigate provider costs. Moreover, reduce the demand to Cloud allowing turning off servers in the data center not to waste energy.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Multimedia services</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Placement strategies</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Cloud-to-fog networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Immich, Roger</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Madeira, Edmundo R.M.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Chown, Justin ELSEVIER</subfield><subfield code="t">Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data</subfield><subfield code="d">2016</subfield><subfield code="d">the international journal for the computer and telecommunications industry</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV019098014</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:191</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:1</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:78-91</subfield><subfield code="g">extent:14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.comcom.2022.04.009</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.87</subfield><subfield code="j">Gastroenterologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">191</subfield><subfield code="j">2022</subfield><subfield code="b">1</subfield><subfield code="c">0701</subfield><subfield code="h">78-91</subfield><subfield code="g">14</subfield></datafield></record></collection>
|
| score |
7.3985615 |