@inproceedings{prodan_towards_2022,
	title = {Towards {Extreme} and {Sustainable} {Graph} {Processing} for {Urgent} {Societal} {Challenges} in {Europe}},
	doi = {10.1109/CloudSummit54781.2022.00010},
	abstract = {The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70\% more efficient analytics than AliGraph, and 30 \% improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 \% lower greenhouse gas emissions for basic graph operations.},
	booktitle = {2022 {IEEE} {Cloud} {Summit}},
	author = {Prodan, Radu and Kimovski, Dragi and Bartolini, Andrea and Cochez, Michael and Iosup, Alexandru and Kharlamov, Evgeny and Rožanec, Jože and Vasiliu, Laurenţiu and Vărbănescu, Ana Lucia},
	month = oct,
	year = {2022},
	keywords = {Cognition, Data centers, Europe, Extreme data, Green products, Serverless computing, Technological innovation, Transforms, graph processing, serverless computing, sustainability},
	pages = {23--30},
}

{"_id":"bypM6XF98A6ydpTmW","bibbaseid":"prodan-kimovski-bartolini-cochez-iosup-kharlamov-roanec-vasiliu-etal-towardsextremeandsustainablegraphprocessingforurgentsocietalchallengesineurope-2022","author_short":["Prodan, R.","Kimovski, D.","Bartolini, A.","Cochez, M.","Iosup, A.","Kharlamov, E.","Rožanec, J.","Vasiliu, L.","Vărbănescu, A. L."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe","doi":"10.1109/CloudSummit54781.2022.00010","abstract":"The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.","booktitle":"2022 IEEE Cloud Summit","author":[{"propositions":[],"lastnames":["Prodan"],"firstnames":["Radu"],"suffixes":[]},{"propositions":[],"lastnames":["Kimovski"],"firstnames":["Dragi"],"suffixes":[]},{"propositions":[],"lastnames":["Bartolini"],"firstnames":["Andrea"],"suffixes":[]},{"propositions":[],"lastnames":["Cochez"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Iosup"],"firstnames":["Alexandru"],"suffixes":[]},{"propositions":[],"lastnames":["Kharlamov"],"firstnames":["Evgeny"],"suffixes":[]},{"propositions":[],"lastnames":["Rožanec"],"firstnames":["Jože"],"suffixes":[]},{"propositions":[],"lastnames":["Vasiliu"],"firstnames":["Laurenţiu"],"suffixes":[]},{"propositions":[],"lastnames":["Vărbănescu"],"firstnames":["Ana","Lucia"],"suffixes":[]}],"month":"October","year":"2022","keywords":"Cognition, Data centers, Europe, Extreme data, Green products, Serverless computing, Technological innovation, Transforms, graph processing, serverless computing, sustainability","pages":"23–30","bibtex":"@inproceedings{prodan_towards_2022,\n\ttitle = {Towards {Extreme} and {Sustainable} {Graph} {Processing} for {Urgent} {Societal} {Challenges} in {Europe}},\n\tdoi = {10.1109/CloudSummit54781.2022.00010},\n\tabstract = {The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70\\% more efficient analytics than AliGraph, and 30 \\% improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 \\% lower greenhouse gas emissions for basic graph operations.},\n\tbooktitle = {2022 {IEEE} {Cloud} {Summit}},\n\tauthor = {Prodan, Radu and Kimovski, Dragi and Bartolini, Andrea and Cochez, Michael and Iosup, Alexandru and Kharlamov, Evgeny and Rožanec, Jože and Vasiliu, Laurenţiu and Vărbănescu, Ana Lucia},\n\tmonth = oct,\n\tyear = {2022},\n\tkeywords = {Cognition, Data centers, Europe, Extreme data, Green products, Serverless computing, Technological innovation, Transforms, graph processing, serverless computing, sustainability},\n\tpages = {23--30},\n}\n\n","author_short":["Prodan, R.","Kimovski, D.","Bartolini, A.","Cochez, M.","Iosup, A.","Kharlamov, E.","Rožanec, J.","Vasiliu, L.","Vărbănescu, A. L."],"key":"prodan_towards_2022","id":"prodan_towards_2022","bibbaseid":"prodan-kimovski-bartolini-cochez-iosup-kharlamov-roanec-vasiliu-etal-towardsextremeandsustainablegraphprocessingforurgentsocietalchallengesineurope-2022","role":"author","urls":{},"keyword":["Cognition","Data centers","Europe","Extreme data","Green products","Serverless computing","Technological innovation","Transforms","graph processing","serverless computing","sustainability"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://api.zotero.org/groups/4799514/items?key=euYq5cdKpGK6xljQmpW8AeSb&format=bibtex&limit=100","dataSources":["qyx6bB8ujfH9zqTzu"],"keywords":["cognition","data centers","europe","extreme data","green products","serverless computing","technological innovation","transforms","graph processing","serverless computing","sustainability"],"search_terms":["towards","extreme","sustainable","graph","processing","urgent","societal","challenges","europe","prodan","kimovski","bartolini","cochez","iosup","kharlamov","rožanec","vasiliu","vărbănescu"],"title":"Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe","year":2022}