Autonomous convergence mechanisms for collaborative crowd-sourced data-modeling

Interoperability remains a central challenge of the Internet of Things (IoT). Standardized data representation can solve this problem. Data model convergence prevents redundancy and fosters reuse. The growth of the IoT demands a high number of data models. Collaborative approaches allow the creation...

Full description

Saved in:
Bibliographic Details
Main Authors: Lubben, Christian, Pahl, Marc-Oliver
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Analytical models
Collaboration
convergence
crowdsourcing
data
interoperability
IoT
modeling
open
Redundancy
Runtime
Scalability
Semantics
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 5
container_issue
container_start_page 1
container_title
container_volume
creator Lubben, Christian
Pahl, Marc-Oliver
description Interoperability remains a central challenge of the Internet of Things (IoT). Standardized data representation can solve this problem. Data model convergence prevents redundancy and fosters reuse. The growth of the IoT demands a high number of data models. Collaborative approaches allow the creation of numerous data models. The question to investigate is: Can assisted distributed model creation improve model convergence?This paper presents an approach to unify IoT data models during creation. It analyzes existing models to find similarities to new model candidates. Similar models shall be reused or extended to prevent information redundancy. Challenges are the accuracy of the similarity analysis and scalability.The evaluation shows linear scalability and high accuracy using a data set containing 1200 automatically converted data models from today's most relevant IoT data modeling initiatives: Project Haystack, IoTSchema, and BrickSchema.
doi_str_mv 10.1109/NOMS54207.2022.9789820
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9789820</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9789820</ieee_id><sourcerecordid>9789820</sourcerecordid><originalsourceid>FETCH-LOGICAL-i133t-362f08969dc9e8665956d3187bfba3ded5857ecbd9fdedbd80f6e945297c039a3</originalsourceid><addsrcrecordid>eNotj91KxDAUhKMguK77BIL0BbqeJG2Sc7ks_sFqL9TrJU1O10rbSNKu-PYW3KuZ4YNhhrFbDmvOAe9eq5e3shCg1wKEWKM2aAScsdXsuFJlAQq4PmcLIXWRowa8ZFcpfQEUGiQsWLWZxjCEPkwpc2E4UjzQ4CjryX3aoU19ypoQZ9R1tg7Rju2RMhfDj89TmKIjn3k72rwPnrp2OFyzi8Z2iVYnXbKPh_v37VO-qx6ft5td3nIpx1wq0YBBhd4hmXkolspLbnTd1FZ68qUpNbnaYzOH2htoFGFRCtQOJFq5ZDf_vS0R7b9j29v4uz_9l39VL1H5</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Autonomous convergence mechanisms for collaborative crowd-sourced data-modeling</title><source>IEEE Xplore All Conference Series</source><creator>Lubben, Christian ; Pahl, Marc-Oliver</creator><creatorcontrib>Lubben, Christian ; Pahl, Marc-Oliver</creatorcontrib><description>Interoperability remains a central challenge of the Internet of Things (IoT). Standardized data representation can solve this problem. Data model convergence prevents redundancy and fosters reuse. The growth of the IoT demands a high number of data models. Collaborative approaches allow the creation of numerous data models. The question to investigate is: Can assisted distributed model creation improve model convergence?This paper presents an approach to unify IoT data models during creation. It analyzes existing models to find similarities to new model candidates. Similar models shall be reused or extended to prevent information redundancy. Challenges are the accuracy of the similarity analysis and scalability.The evaluation shows linear scalability and high accuracy using a data set containing 1200 automatically converted data models from today's most relevant IoT data modeling initiatives: Project Haystack, IoTSchema, and BrickSchema.</description><identifier>EISSN: 2374-9709</identifier><identifier>EISBN: 9781665406017</identifier><identifier>EISBN: 1665406011</identifier><identifier>DOI: 10.1109/NOMS54207.2022.9789820</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptation models ; Analytical models ; Collaboration ; convergence ; crowdsourcing ; data ; interoperability ; IoT ; modeling ; open ; Redundancy ; Runtime ; Scalability ; Semantics</subject><ispartof>NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 2022, p.1-5</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9789820$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9789820$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lubben, Christian</creatorcontrib><creatorcontrib>Pahl, Marc-Oliver</creatorcontrib><title>Autonomous convergence mechanisms for collaborative crowd-sourced data-modeling</title><title>NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium</title><addtitle>NOMS</addtitle><description>Interoperability remains a central challenge of the Internet of Things (IoT). Standardized data representation can solve this problem. Data model convergence prevents redundancy and fosters reuse. The growth of the IoT demands a high number of data models. Collaborative approaches allow the creation of numerous data models. The question to investigate is: Can assisted distributed model creation improve model convergence?This paper presents an approach to unify IoT data models during creation. It analyzes existing models to find similarities to new model candidates. Similar models shall be reused or extended to prevent information redundancy. Challenges are the accuracy of the similarity analysis and scalability.The evaluation shows linear scalability and high accuracy using a data set containing 1200 automatically converted data models from today's most relevant IoT data modeling initiatives: Project Haystack, IoTSchema, and BrickSchema.</description><subject>Adaptation models</subject><subject>Analytical models</subject><subject>Collaboration</subject><subject>convergence</subject><subject>crowdsourcing</subject><subject>data</subject><subject>interoperability</subject><subject>IoT</subject><subject>modeling</subject><subject>open</subject><subject>Redundancy</subject><subject>Runtime</subject><subject>Scalability</subject><subject>Semantics</subject><issn>2374-9709</issn><isbn>9781665406017</isbn><isbn>1665406011</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj91KxDAUhKMguK77BIL0BbqeJG2Sc7ks_sFqL9TrJU1O10rbSNKu-PYW3KuZ4YNhhrFbDmvOAe9eq5e3shCg1wKEWKM2aAScsdXsuFJlAQq4PmcLIXWRowa8ZFcpfQEUGiQsWLWZxjCEPkwpc2E4UjzQ4CjryX3aoU19ypoQZ9R1tg7Rju2RMhfDj89TmKIjn3k72rwPnrp2OFyzi8Z2iVYnXbKPh_v37VO-qx6ft5td3nIpx1wq0YBBhd4hmXkolspLbnTd1FZ68qUpNbnaYzOH2htoFGFRCtQOJFq5ZDf_vS0R7b9j29v4uz_9l39VL1H5</recordid><startdate>20220425</startdate><enddate>20220425</enddate><creator>Lubben, Christian</creator><creator>Pahl, Marc-Oliver</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20220425</creationdate><title>Autonomous convergence mechanisms for collaborative crowd-sourced data-modeling</title><author>Lubben, Christian ; Pahl, Marc-Oliver</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i133t-362f08969dc9e8665956d3187bfba3ded5857ecbd9fdedbd80f6e945297c039a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptation models</topic><topic>Analytical models</topic><topic>Collaboration</topic><topic>convergence</topic><topic>crowdsourcing</topic><topic>data</topic><topic>interoperability</topic><topic>IoT</topic><topic>modeling</topic><topic>open</topic><topic>Redundancy</topic><topic>Runtime</topic><topic>Scalability</topic><topic>Semantics</topic><toplevel>online_resources</toplevel><creatorcontrib>Lubben, Christian</creatorcontrib><creatorcontrib>Pahl, Marc-Oliver</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lubben, Christian</au><au>Pahl, Marc-Oliver</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Autonomous convergence mechanisms for collaborative crowd-sourced data-modeling</atitle><btitle>NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium</btitle><stitle>NOMS</stitle><date>2022-04-25</date><risdate>2022</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eissn>2374-9709</eissn><eisbn>9781665406017</eisbn><eisbn>1665406011</eisbn><abstract>Interoperability remains a central challenge of the Internet of Things (IoT). Standardized data representation can solve this problem. Data model convergence prevents redundancy and fosters reuse. The growth of the IoT demands a high number of data models. Collaborative approaches allow the creation of numerous data models. The question to investigate is: Can assisted distributed model creation improve model convergence?This paper presents an approach to unify IoT data models during creation. It analyzes existing models to find similarities to new model candidates. Similar models shall be reused or extended to prevent information redundancy. Challenges are the accuracy of the similarity analysis and scalability.The evaluation shows linear scalability and high accuracy using a data set containing 1200 automatically converted data models from today's most relevant IoT data modeling initiatives: Project Haystack, IoTSchema, and BrickSchema.</abstract><pub>IEEE</pub><doi>10.1109/NOMS54207.2022.9789820</doi><tpages>5</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2374-9709
ispartof NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 2022, p.1-5
issn 2374-9709
language eng
recordid cdi_ieee_primary_9789820
source IEEE Xplore All Conference Series
subjects Adaptation models
Analytical models
Collaboration
convergence
crowdsourcing
data
interoperability
IoT
modeling
open
Redundancy
Runtime
Scalability
Semantics
title Autonomous convergence mechanisms for collaborative crowd-sourced data-modeling
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T19%3A43%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Autonomous%20convergence%20mechanisms%20for%20collaborative%20crowd-sourced%20data-modeling&rft.btitle=NOMS%202022-2022%20IEEE/IFIP%20Network%20Operations%20and%20Management%20Symposium&rft.au=Lubben,%20Christian&rft.date=2022-04-25&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.eissn=2374-9709&rft_id=info:doi/10.1109/NOMS54207.2022.9789820&rft.eisbn=9781665406017&rft.eisbn_list=1665406011&rft_dat=%3Cieee_CHZPO%3E9789820%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i133t-362f08969dc9e8665956d3187bfba3ded5857ecbd9fdedbd80f6e945297c039a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9789820&rfr_iscdi=true