Big Data Semantics

Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinate...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal on data semantics 2018-06, Vol.7 (2), p.65-85
Hauptverfasser: Ceravolo, Paolo, Azzini, Antonia, Angelini, Marco, Catarci, Tiziana, Cudré-Mauroux, Philippe, Damiani, Ernesto, Mazak, Alexandra, Van Keulen, Maurice, Jarrar, Mustafa, Santucci, Giuseppe, Sattler, Kai-Uwe, Scannapieco, Monica, Wimmer, Manuel, Wrembel, Robert, Zaraket, Fadi
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 85
container_issue 2
container_start_page 65
container_title Journal on data semantics
container_volume 7
creator Ceravolo, Paolo
Azzini, Antonia
Angelini, Marco
Catarci, Tiziana
Cudré-Mauroux, Philippe
Damiani, Ernesto
Mazak, Alexandra
Van Keulen, Maurice
Jarrar, Mustafa
Santucci, Giuseppe
Sattler, Kai-Uwe
Scannapieco, Monica
Wimmer, Manuel
Wrembel, Robert
Zaraket, Fadi
description Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.
doi_str_mv 10.1007/s13740-018-0086-2
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2052868555</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2052868555</sourcerecordid><originalsourceid>FETCH-LOGICAL-c407t-4ec731a1d4a88fb3874eb3b855d18d382d22f5dc010e28bc356037925ecc512a3</originalsourceid><addsrcrecordid>eNp1jz1PwzAQhi0EElXpwsZWidlwd_5khEIBqRIDMFuO41SpaFLsdODf4yoIJm65G573PT2MXSBcIYC5ziiMBA5oOYDVnI7YBK1GTiDh-PcWdMpmOW-gjEahLUzY-V27nt_7wc9f49Z3QxvyGTtp_EeOs589Ze_Lh7fFE1-9PD4vblc8SDADlzEYgR5r6a1tKmGNjJWorFI12lpYqokaVQdAiGSrIJQGYW5IxRAUkhdTdjn27lL_uY95cJt-n7ry0hEosrpUqULhSIXU55xi43ap3fr05RDcwd6N9q7Yu4O9o5KhMZML261j-mv-P_QNB55YzA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2052868555</pqid></control><display><type>article</type><title>Big Data Semantics</title><source>SpringerLink Journals - AutoHoldings</source><creator>Ceravolo, Paolo ; Azzini, Antonia ; Angelini, Marco ; Catarci, Tiziana ; Cudré-Mauroux, Philippe ; Damiani, Ernesto ; Mazak, Alexandra ; Van Keulen, Maurice ; Jarrar, Mustafa ; Santucci, Giuseppe ; Sattler, Kai-Uwe ; Scannapieco, Monica ; Wimmer, Manuel ; Wrembel, Robert ; Zaraket, Fadi</creator><creatorcontrib>Ceravolo, Paolo ; Azzini, Antonia ; Angelini, Marco ; Catarci, Tiziana ; Cudré-Mauroux, Philippe ; Damiani, Ernesto ; Mazak, Alexandra ; Van Keulen, Maurice ; Jarrar, Mustafa ; Santucci, Giuseppe ; Sattler, Kai-Uwe ; Scannapieco, Monica ; Wimmer, Manuel ; Wrembel, Robert ; Zaraket, Fadi</creatorcontrib><description>Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.</description><identifier>ISSN: 1861-2032</identifier><identifier>EISSN: 1861-2040</identifier><identifier>DOI: 10.1007/s13740-018-0086-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Analytics ; Artificial Intelligence ; Big Data ; Computer Science ; Data management ; Data processing ; Database Management ; Information Storage and Retrieval ; Information Systems Applications (incl.Internet) ; IT in Business ; Literature reviews ; Original Article ; Semantics</subject><ispartof>Journal on data semantics, 2018-06, Vol.7 (2), p.65-85</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Copyright Springer Science &amp; Business Media 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c407t-4ec731a1d4a88fb3874eb3b855d18d382d22f5dc010e28bc356037925ecc512a3</citedby><cites>FETCH-LOGICAL-c407t-4ec731a1d4a88fb3874eb3b855d18d382d22f5dc010e28bc356037925ecc512a3</cites><orcidid>0000-0002-4519-0173</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s13740-018-0086-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s13740-018-0086-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Ceravolo, Paolo</creatorcontrib><creatorcontrib>Azzini, Antonia</creatorcontrib><creatorcontrib>Angelini, Marco</creatorcontrib><creatorcontrib>Catarci, Tiziana</creatorcontrib><creatorcontrib>Cudré-Mauroux, Philippe</creatorcontrib><creatorcontrib>Damiani, Ernesto</creatorcontrib><creatorcontrib>Mazak, Alexandra</creatorcontrib><creatorcontrib>Van Keulen, Maurice</creatorcontrib><creatorcontrib>Jarrar, Mustafa</creatorcontrib><creatorcontrib>Santucci, Giuseppe</creatorcontrib><creatorcontrib>Sattler, Kai-Uwe</creatorcontrib><creatorcontrib>Scannapieco, Monica</creatorcontrib><creatorcontrib>Wimmer, Manuel</creatorcontrib><creatorcontrib>Wrembel, Robert</creatorcontrib><creatorcontrib>Zaraket, Fadi</creatorcontrib><title>Big Data Semantics</title><title>Journal on data semantics</title><addtitle>J Data Semant</addtitle><description>Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.</description><subject>Analytics</subject><subject>Artificial Intelligence</subject><subject>Big Data</subject><subject>Computer Science</subject><subject>Data management</subject><subject>Data processing</subject><subject>Database Management</subject><subject>Information Storage and Retrieval</subject><subject>Information Systems Applications (incl.Internet)</subject><subject>IT in Business</subject><subject>Literature reviews</subject><subject>Original Article</subject><subject>Semantics</subject><issn>1861-2032</issn><issn>1861-2040</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1jz1PwzAQhi0EElXpwsZWidlwd_5khEIBqRIDMFuO41SpaFLsdODf4yoIJm65G573PT2MXSBcIYC5ziiMBA5oOYDVnI7YBK1GTiDh-PcWdMpmOW-gjEahLUzY-V27nt_7wc9f49Z3QxvyGTtp_EeOs589Ze_Lh7fFE1-9PD4vblc8SDADlzEYgR5r6a1tKmGNjJWorFI12lpYqokaVQdAiGSrIJQGYW5IxRAUkhdTdjn27lL_uY95cJt-n7ry0hEosrpUqULhSIXU55xi43ap3fr05RDcwd6N9q7Yu4O9o5KhMZML261j-mv-P_QNB55YzA</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Ceravolo, Paolo</creator><creator>Azzini, Antonia</creator><creator>Angelini, Marco</creator><creator>Catarci, Tiziana</creator><creator>Cudré-Mauroux, Philippe</creator><creator>Damiani, Ernesto</creator><creator>Mazak, Alexandra</creator><creator>Van Keulen, Maurice</creator><creator>Jarrar, Mustafa</creator><creator>Santucci, Giuseppe</creator><creator>Sattler, Kai-Uwe</creator><creator>Scannapieco, Monica</creator><creator>Wimmer, Manuel</creator><creator>Wrembel, Robert</creator><creator>Zaraket, Fadi</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-4519-0173</orcidid></search><sort><creationdate>20180601</creationdate><title>Big Data Semantics</title><author>Ceravolo, Paolo ; Azzini, Antonia ; Angelini, Marco ; Catarci, Tiziana ; Cudré-Mauroux, Philippe ; Damiani, Ernesto ; Mazak, Alexandra ; Van Keulen, Maurice ; Jarrar, Mustafa ; Santucci, Giuseppe ; Sattler, Kai-Uwe ; Scannapieco, Monica ; Wimmer, Manuel ; Wrembel, Robert ; Zaraket, Fadi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-4ec731a1d4a88fb3874eb3b855d18d382d22f5dc010e28bc356037925ecc512a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Analytics</topic><topic>Artificial Intelligence</topic><topic>Big Data</topic><topic>Computer Science</topic><topic>Data management</topic><topic>Data processing</topic><topic>Database Management</topic><topic>Information Storage and Retrieval</topic><topic>Information Systems Applications (incl.Internet)</topic><topic>IT in Business</topic><topic>Literature reviews</topic><topic>Original Article</topic><topic>Semantics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ceravolo, Paolo</creatorcontrib><creatorcontrib>Azzini, Antonia</creatorcontrib><creatorcontrib>Angelini, Marco</creatorcontrib><creatorcontrib>Catarci, Tiziana</creatorcontrib><creatorcontrib>Cudré-Mauroux, Philippe</creatorcontrib><creatorcontrib>Damiani, Ernesto</creatorcontrib><creatorcontrib>Mazak, Alexandra</creatorcontrib><creatorcontrib>Van Keulen, Maurice</creatorcontrib><creatorcontrib>Jarrar, Mustafa</creatorcontrib><creatorcontrib>Santucci, Giuseppe</creatorcontrib><creatorcontrib>Sattler, Kai-Uwe</creatorcontrib><creatorcontrib>Scannapieco, Monica</creatorcontrib><creatorcontrib>Wimmer, Manuel</creatorcontrib><creatorcontrib>Wrembel, Robert</creatorcontrib><creatorcontrib>Zaraket, Fadi</creatorcontrib><collection>CrossRef</collection><jtitle>Journal on data semantics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ceravolo, Paolo</au><au>Azzini, Antonia</au><au>Angelini, Marco</au><au>Catarci, Tiziana</au><au>Cudré-Mauroux, Philippe</au><au>Damiani, Ernesto</au><au>Mazak, Alexandra</au><au>Van Keulen, Maurice</au><au>Jarrar, Mustafa</au><au>Santucci, Giuseppe</au><au>Sattler, Kai-Uwe</au><au>Scannapieco, Monica</au><au>Wimmer, Manuel</au><au>Wrembel, Robert</au><au>Zaraket, Fadi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Big Data Semantics</atitle><jtitle>Journal on data semantics</jtitle><stitle>J Data Semant</stitle><date>2018-06-01</date><risdate>2018</risdate><volume>7</volume><issue>2</issue><spage>65</spage><epage>85</epage><pages>65-85</pages><issn>1861-2032</issn><eissn>1861-2040</eissn><abstract>Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s13740-018-0086-2</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-4519-0173</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1861-2032
ispartof Journal on data semantics, 2018-06, Vol.7 (2), p.65-85
issn 1861-2032
1861-2040
language eng
recordid cdi_proquest_journals_2052868555
source SpringerLink Journals - AutoHoldings
subjects Analytics
Artificial Intelligence
Big Data
Computer Science
Data management
Data processing
Database Management
Information Storage and Retrieval
Information Systems Applications (incl.Internet)
IT in Business
Literature reviews
Original Article
Semantics
title Big Data Semantics
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T10%3A29%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Big%20Data%20Semantics&rft.jtitle=Journal%20on%20data%20semantics&rft.au=Ceravolo,%20Paolo&rft.date=2018-06-01&rft.volume=7&rft.issue=2&rft.spage=65&rft.epage=85&rft.pages=65-85&rft.issn=1861-2032&rft.eissn=1861-2040&rft_id=info:doi/10.1007/s13740-018-0086-2&rft_dat=%3Cproquest_cross%3E2052868555%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2052868555&rft_id=info:pmid/&rfr_iscdi=true