{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T13:53:23Z","timestamp":1770040403421,"version":"3.49.0"},"reference-count":187,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T00:00:00Z","timestamp":1527033600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"European Union\u2019s Horizon 2020","award":["688797"],"award-info":[{"award-number":["688797"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Data Semant"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s13740-018-0086-2","type":"journal-article","created":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T07:44:47Z","timestamp":1527061487000},"page":"65-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Big Data Semantics"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4519-0173","authenticated-orcid":false,"given":"Paolo","family":"Ceravolo","sequence":"first","affiliation":[]},{"given":"Antonia","family":"Azzini","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Angelini","sequence":"additional","affiliation":[]},{"given":"Tiziana","family":"Catarci","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Cudr\u00e9-Mauroux","sequence":"additional","affiliation":[]},{"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[]},{"given":"Alexandra","family":"Mazak","sequence":"additional","affiliation":[]},{"given":"Maurice","family":"Van Keulen","sequence":"additional","affiliation":[]},{"given":"Mustafa","family":"Jarrar","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Santucci","sequence":"additional","affiliation":[]},{"given":"Kai-Uwe","family":"Sattler","sequence":"additional","affiliation":[]},{"given":"Monica","family":"Scannapieco","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Wimmer","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Wrembel","sequence":"additional","affiliation":[]},{"given":"Fadi","family":"Zaraket","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,5,23]]},"reference":[{"key":"86_CR1","volume-title":"Understanding big data: analytics for enterprise class hadoop and streaming data","author":"P Zikopoulos","year":"2011","unstructured":"Zikopoulos P, Eaton C et al (2011) Understanding big data: analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media, New York"},{"key":"86_CR2","unstructured":"Ward JS, Barker A (2013) Undefined by data: a survey of big data definitions. arXiv preprint arXiv:1309.5821"},{"key":"86_CR3","first-page":"2014","volume-title":"The importance of big data: a definition","author":"MA Beyer","year":"2012","unstructured":"Beyer MA, Laney D (2012) The importance of big data: a definition. Gartner, Stamford, pp 2014\u20132018"},{"key":"86_CR4","first-page":"70","volume":"6","author":"D Laney","year":"2001","unstructured":"Laney D (2001) 3d data management: controlling data volume, velocity and variety. META Gr Res Note 6:70","journal-title":"META Gr Res Note"},{"key":"86_CR5","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.is.2014.07.006","volume":"47","author":"IAT Hashem","year":"2015","unstructured":"Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of \"big data\" on cloud computing: review and open research issues. Inf Syst 47:98\u2013115","journal-title":"Inf Syst"},{"key":"86_CR6","unstructured":"Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How big data can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234\u2013246 [Online]. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925527314004253 . Accessed 20 Feb 2018"},{"issue":"3","key":"86_CR7","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/MIC.2012.50","volume":"16","author":"S Madden","year":"2012","unstructured":"Madden S (2012) From databases to big data. IEEE Internet Comput 16(3):4\u20136","journal-title":"IEEE Internet Comput"},{"key":"86_CR8","unstructured":"Amazon A (2016) Amazon 2016 [Online]. https:\/\/aws.amazon.com . 2016-01-06"},{"key":"86_CR9","unstructured":"Hadoop A (2009) Hadoop [Online]. http:\/\/hadoop.apache.org . 2009-03-06"},{"issue":"4","key":"86_CR10","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.2307\/41703503","volume":"36","author":"H Chen","year":"2012","unstructured":"Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165\u20131188","journal-title":"MIS Q"},{"issue":"1","key":"86_CR11","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/TKDE.2013.109","volume":"26","author":"X Wu","year":"2014","unstructured":"Wu X, Zhu X, Wu G-Q, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97\u2013107","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"86_CR12","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1111\/dpr.12142","volume":"34","author":"M Hilbert","year":"2016","unstructured":"Hilbert M (2016) Big data for development: a review of promises and challenges. Dev Policy Rev 34(1):135\u2013174","journal-title":"Dev Policy Rev"},{"key":"86_CR13","doi-asserted-by":"crossref","unstructured":"Assun\u00e7 ao MD, Calheiros RN, Bianchi S, Netto MA, Buyya R, (2015) Big data computing and clouds: trends and future directions. J Parallel Distrib Comput 79:3\u201315","DOI":"10.1016\/j.jpdc.2014.08.003"},{"issue":"13","key":"86_CR14","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.14778\/2733004.2733075","volume":"7","author":"V Markl","year":"2014","unstructured":"Markl V (2014) Breaking the chains: on declarative data analysis and data independence in the big data era. Proc VLDB Endow 7(13):1730\u20131733","journal-title":"Proc VLDB Endow"},{"key":"86_CR15","first-page":"20","volume-title":"OTM confederated international conferences \"On the move to meaningful internet systems\"","author":"E Damiani","year":"2003","unstructured":"Damiani E, Oliboni B, Quintarelli E, Tanca L (2003) Modeling semistructured data by using graph-based constraints. OTM confederated international conferences \"On the move to meaningful internet systems\". Springer, Berlin, pp 20\u201321"},{"key":"86_CR16","volume-title":"Common warehouse metamodel","author":"J Poole","year":"2003","unstructured":"Poole J, Chang D, Tolbert D, Mellor D (2003) Common warehouse metamodel. Developer\u2019s guide, Wiley, Hoboken"},{"key":"86_CR17","doi-asserted-by":"crossref","unstructured":"Ardagna C, Asal R, Damiani E, Vu Q (2015) From security to assurance in the cloud: a survey. ACM Comput Surv: CSUR 48(1):2:1\u20132:50","DOI":"10.1145\/2767005"},{"key":"86_CR18","doi-asserted-by":"publisher","first-page":"160018","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE et al (2016) The fair guiding principles for scientific data management and stewardship. Sci Data 3:160018","journal-title":"Sci Data"},{"key":"86_CR19","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-540-30145-5_2","volume-title":"Semantics of a networked world","author":"K Aberer","year":"2004","unstructured":"Aberer K, Catarci T, Cudr\u00e9-Mauroux P, Dillon T, Grimm S, Hacid M-S, Illarramendi A, Jarrar M, Kashyap V, Mecella M et al (2004) Emergent semantics systems. Semantics of a networked world. Semantics for grid databases. Springer, Berlin, pp 14\u201343"},{"key":"86_CR20","doi-asserted-by":"crossref","unstructured":"Cudr\u00e9-Mauroux P, Aberer K, Abdelmoty AI, Catarci T, Damiani E, Illaramendi A, Jarrar M, Meersman R, Neuhold EJ, Parent C et\u00a0al (2006) Viewpoints on emergent semantics. In: Spaccapietra S, Aberer K, Cudr\u00e9-Mauroux P (eds) Journal on data semantics VI. Springer, Berlin, pp 1\u201327","DOI":"10.1007\/11803034_1"},{"key":"86_CR21","doi-asserted-by":"crossref","unstructured":"Ardagna CA, Ceravolo P, Damiani E (2016) Big data analytics as-a-service: Issues and challenges. In: IEEE International conference on Big Data (Big Data). IEEE, pp 3638\u20133644","DOI":"10.1109\/BigData.2016.7841029"},{"issue":"2","key":"86_CR22","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","volume":"19","author":"M Chen","year":"2014","unstructured":"Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171\u2013209","journal-title":"Mob Netw Appl"},{"key":"86_CR23","doi-asserted-by":"crossref","unstructured":"Azzini A, Ceravolo P (2013) Consistent process mining over big data triple stores. In: IEEE international congress on Big Data (BigData Congress). IEEE, pp 54\u201361","DOI":"10.1109\/BigData.Congress.2013.17"},{"key":"86_CR24","doi-asserted-by":"crossref","unstructured":"Woods WA (1975) What\u2019s in a link: foundations for semantic networks. In: Representation and understanding. Elsevier, pp 35\u201382","DOI":"10.1016\/B978-0-12-108550-6.50007-0"},{"key":"86_CR25","doi-asserted-by":"publisher","unstructured":"Franklin MJ, Halevy AY, Maier D (2005) From databases to dataspaces: a new abstraction for information management. SIGMOD Rec 34(4):27\u201333 [Online]. https:\/\/doi.org\/10.1145\/1107499.1107502","DOI":"10.1145\/1107499.1107502"},{"key":"86_CR26","doi-asserted-by":"publisher","unstructured":"Smith K, Seligman L, Rosenthal A, Kurcz C, Greer M, Macheret C, Sexton M, Eckstein A (2014) Big metadata: the need for principled metadata management in big data ecosystems. In: Proceedings of workshop on data analytics in the Cloud, series DanaC\u201914. ACM, New York, pp 13:1\u201313:4 [Online]. https:\/\/doi.org\/10.1145\/2627770.2627776","DOI":"10.1145\/2627770.2627776"},{"issue":"2","key":"86_CR27","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1111\/jbl.12010","volume":"34","author":"MA Waller","year":"2013","unstructured":"Waller MA, Fawcett SE (2013) Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J Bus Logist 34(2):77\u201384","journal-title":"J Bus Logist"},{"key":"86_CR28","unstructured":"Borkar V, Carey MJ, Li C (2012) Inside big data management: ogres, onions, or parfaits? In: Proceedings of the 15th international conference on extending database technology. ACM, pp 3\u201314"},{"key":"86_CR29","volume-title":"Hadoop: the definitive guide","author":"T White","year":"2012","unstructured":"White T (2012) Hadoop: the definitive guide. O\u2019Reilly Media Inc, Sebastopol"},{"issue":"2","key":"86_CR30","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.bdr.2015.01.005","volume":"2","author":"H Jagadish","year":"2015","unstructured":"Jagadish H (2015) Big data and science: myths and reality. Big Data Res 2(2):49\u201352","journal-title":"Big Data Res"},{"issue":"4","key":"86_CR31","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.bdr.2015.01.001","volume":"2","author":"P P\u00e4\u00e4kk\u00f6nen","year":"2015","unstructured":"P\u00e4\u00e4kk\u00f6nen P, Pakkala D (2015) Reference architecture and classification of technologies, products and services for big data systems. Big Data Res 2(4):166\u2013186","journal-title":"Big Data Res"},{"key":"86_CR32","doi-asserted-by":"crossref","unstructured":"Ardagna C, Bellandi V, Bezzi M, Ceravolo P, Damiani E, Hebert C (June 2017) A model-driven methodology for big data analytics-as-a-service. In: Proceedings of BigData Congress, Honolulu. HI, USA","DOI":"10.1109\/BigDataCongress.2017.23"},{"key":"86_CR33","doi-asserted-by":"publisher","unstructured":"Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proc VLDB Endow 5(12):2032\u20132033. https:\/\/doi.org\/10.14778\/2367502.2367572","DOI":"10.14778\/2367502.2367572"},{"key":"86_CR34","doi-asserted-by":"crossref","unstructured":"Ardagna CA, Bellandi V, Bezzi M, Ceravolo P, Damiani E, Hebert C (2018) Model-based big data analytics-as-a-service: take big data to the next level. IEEE Trans Serv Comput PP(99):1\u20131","DOI":"10.1109\/TSC.2018.2816941"},{"key":"86_CR35","doi-asserted-by":"crossref","unstructured":"Liao C, Squicciarini A (2015) Towards provenance-based anomaly detection in mapreduce. In: 15th IEEE\/ACM international symposium on cluster, cloud and grid computing (CCGrid), vol 2015. IEEE, pp 647\u2013656","DOI":"10.1109\/CCGrid.2015.16"},{"issue":"2","key":"86_CR36","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/2814710.2814713","volume":"44","author":"J Duggan","year":"2015","unstructured":"Duggan J, Elmore AJ, Stonebraker M, Balazinska M, Howe B, Kepner J, Madden S, Maier D, Mattson T, Zdonik S (2015) The BigDAWG polystore system. SIGMOD Rec 44(2):11\u201316","journal-title":"SIGMOD Rec"},{"key":"86_CR37","doi-asserted-by":"crossref","unstructured":"Sowmya R, Suneetha K (2017) Data mining with big data. In: 11th international conference on intelligent systems and control (ISCO). IEEE, pp 246\u2013250","DOI":"10.1109\/ISCO.2017.7855990"},{"key":"86_CR38","doi-asserted-by":"crossref","unstructured":"Zhou W, Mapara S, Ren Y, Li Y, Haeberlen A, Ives Z, Loo BT, Sherr M (2012) Distributed time-aware provenance. In: Proceedings of the VLDB endowment, vol 6, no 2. VLDB Endowment, pp 49\u201360","DOI":"10.14778\/2535568.2448939"},{"key":"86_CR39","unstructured":"Akoush S, Sohan R, Hopper A (2013) Hadoopprov: towards provenance as a first class citizen in mapreduce. In: TaPP"},{"key":"86_CR40","doi-asserted-by":"crossref","unstructured":"Glavic B (2014) Big data provenance: challenges and implications for benchmarking. In: Rabl T, Poess M, Baru C, Jacobsen H-A (eds) Specifying big data benchmarks. Springer, Berlin, Heidelberg, pp 72\u201380","DOI":"10.1007\/978-3-642-53974-9_7"},{"key":"86_CR41","doi-asserted-by":"crossref","unstructured":"Berti-Equille L, Ba ML (2016) Veracity of big data: challenges of cross-modal truth discovery. J. Data Inf Qual 7(3):12:1\u201312:3","DOI":"10.1145\/2935753"},{"key":"86_CR42","doi-asserted-by":"crossref","unstructured":"Kl\u00e4s M, Putz W, Lutz T (2016) Quality evaluation for big data: a scalable assessment approach and first evaluation results. In: 2016 joint conference of the international workshop on software measurement and the international conference on software process and product measurement (IWSM-MENSURA). IEEE, pp 115\u2013124","DOI":"10.1109\/IWSM-Mensura.2016.026"},{"key":"86_CR43","doi-asserted-by":"crossref","unstructured":"Daiber J, Jakob M, Hokamp C, Mendes PN (2013) Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th international conference on semantic systems. ACM, pp 121\u2013124","DOI":"10.1145\/2506182.2506198"},{"key":"86_CR44","doi-asserted-by":"publisher","unstructured":"Shin J, Wu S, Wang F, De Sa C, Zhang C, R\u00e9 C (July 2015) Incremental knowledge base construction using DeepDive. Proc VLDB Endow 8(11), 1310\u20131321. ISSN 2150-8097. https:\/\/doi.org\/10.14778\/2809974.2809991","DOI":"10.14778\/2809974.2809991"},{"key":"86_CR45","unstructured":"Chiticariu L, Krishnamurthy R, Li Y, Raghavan S, Reiss FR, Vaithyanathan S (2010) Systemt: an algebraic approach to declarative information extraction. In: Proceedings of the association for computational linguistics, pp 128\u2013137"},{"key":"86_CR46","unstructured":"Fuhring P, Naumann F (2007) Emergent data quality annotation and visualization [Online]. https:\/\/hpi.de\/fileadmin\/user_upload\/fachgebiete\/naumann\/publications\/2007\/Emergent_Data_Quality_Annotation_and_Visualization.pdf . Accessed 20 Feb 2018"},{"key":"86_CR47","doi-asserted-by":"publisher","unstructured":"Bondiombouy C, Kolev B, Levchenko O, Valduriez P (2016) Multistore big data integration with CloudMdsQL. In: Hameurlain A, K\u00fcng J, Wagner R, Chen Q (eds) Transactions on large-scale data-and knowledge-centered systems XXVIII: special issue on database-and expert-systems applications. Springer, Berlin, Heidelberg, pp 48\u201374. https:\/\/doi.org\/10.1007\/978-3-662-53455-7_3","DOI":"10.1007\/978-3-662-53455-7_3"},{"key":"86_CR48","doi-asserted-by":"crossref","unstructured":"Bergamaschi S, Beneventano D, Mandreoli F, Martoglia R, Guerra F, Orsini M, Po L, Vincini M, Simonini G, Zhu S , Gagliardelli L, Magnotta L (2018) From data integration to big data integration. In: Flesca S, Greco S, Masciari E, Sacc\u00e0 D (eds) A comprehensive guide through the Italian database research over the last 25 years. Springer, Cham, pp 43\u201359","DOI":"10.1007\/978-3-319-61893-7_3"},{"key":"86_CR49","doi-asserted-by":"crossref","unstructured":"Ramakrishnan R, Sridharan B, Douceur JR, Kasturi P, Krishnamachari-Sampath B, Krishnamoorthy K, Li P, Manu M, Michaylov S, Ramos R et\u00a0al (2017) Azure data lake store: a hyperscale distributed file service for big data analytics. In: Proceedings of the 2017 ACM international conference on management of data. ACM, pp 51\u201363","DOI":"10.1145\/3035918.3056100"},{"key":"86_CR50","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ymeth.2016.09.002","volume":"111","author":"M Masseroli","year":"2016","unstructured":"Masseroli M, Kaitoua A, Pinoli P, Ceri S (2016) Modeling and interoperability of heterogeneous genomic big data for integrative processing and querying. Methods 111:3\u201311","journal-title":"Methods"},{"key":"86_CR51","unstructured":"Scannapieco M, Virgillito A, Zardetto D (2013) Placing big data in official statistics: a big challenge? In: Proceedings of NTTS (new techniques and technologies for statistics), March 5\u20137, Brussels"},{"key":"86_CR52","unstructured":"Gualtieri M, Hopkins B (2014) SQL-For-Hadoop: 14 capable solutions reviewed. Forrester"},{"key":"86_CR53","doi-asserted-by":"crossref","unstructured":"Liu H, Kumar TA, Thomas JP (2015) Cleaning framework for big data-object identification and linkage. In: IEEE international congress on Big Data (BigData Congress). IEEE, pp 215\u2013221","DOI":"10.1109\/BigDataCongress.2015.38"},{"key":"86_CR54","doi-asserted-by":"publisher","unstructured":"Gulzar MA, Interlandi M, Han X, Li M, Condie T, Kim M (2017) Automated debugging in data-intensive scalable computing. In: Proceedings of the 2017 symposium on cloud computing, series SoCC \u201917. ACM, New York, pp 520\u2013534 [Online]. https:\/\/doi.org\/10.1145\/3127479.3131624","DOI":"10.1145\/3127479.3131624"},{"key":"86_CR55","unstructured":"de Wit T (2017) Using AIS to make maritime statistics. In: Proceedings of NTTS (New techniques and technologies for statistics), March 14\u201316, Brussels"},{"key":"86_CR56","doi-asserted-by":"crossref","unstructured":"Zardetto D, Scannapieco M, Catarci T (2010) Effective automated object matching. In: Proceedings of the 26th international conference on data engineering, ICDE 2010, March 1-6, Long Beach, California, USA, pp 757\u2013768","DOI":"10.1109\/ICDE.2010.5447904"},{"key":"86_CR57","doi-asserted-by":"crossref","unstructured":"Xin RS, Gonzalez JE, Franklin MJ, Stoica I (2013) Graphx: a resilient distributed graph system on spark. In: First international workshop on graph data management experiences and systems, GRADES 2013, co-loated with SIGMOD\/PODS, New York, NY, USA, June 24, p 2 [Online]. http:\/\/event.cwi.nl\/grades2013\/02-xin.pdf . Accessed 20 Feb 2018","DOI":"10.1145\/2484425.2484427"},{"key":"86_CR58","unstructured":"Junghanns M, Petermann A, G\u00f3mez K, Rahm E (2015) GRADOOP: scalable graph data management and analytics with hadoop. CoRR [Online]. arxiv:1506.00548"},{"key":"86_CR59","doi-asserted-by":"publisher","unstructured":"Yu J, Wu J, Sarwat M (2015) Geospark: a cluster computing framework for processing large-scale spatial data. In: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems, Bellevue, WA, USA, November 3\u20136, pp 70:1\u201370:4 [Online]. https:\/\/doi.org\/10.1145\/2820783.2820860","DOI":"10.1145\/2820783.2820860"},{"key":"86_CR60","doi-asserted-by":"publisher","unstructured":"You S, Zhang J, Gruenwald L (2015) Large-scale spatial join query processing in cloud. In: 31st IEEE international conference on data engineering workshops, ICDE workshops 2015, Seoul, South Korea, April 13\u201317, pp 34\u201341. [Online]. https:\/\/doi.org\/10.1109\/ICDEW.2015.7129541","DOI":"10.1109\/ICDEW.2015.7129541"},{"issue":"2","key":"86_CR61","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s13222-015-0190-5","volume":"15","author":"O Saleh","year":"2015","unstructured":"Saleh O, Hagedorn S, Sattler K (2015) Complex event processing on linked stream data. Datenbank Spektrum 15(2):119\u2013129","journal-title":"Datenbank Spektrum"},{"key":"86_CR62","unstructured":"Kornacker M, Behm A, Bittorf V, Bobrovytsky T, Ching C, Choi A, Erickson J, Grund M, Hecht D, Jacobs M, Joshi I, Kuff L, Kumar D, Leblang A, Li N, Pandis I, Robinson H, Rorke D, Rus S, Russell J, Tsirogiannis D, Wanderman-Milne S, Yoder M (2015) Impala: a modern, open-source SQL engine for hadoop. In: CIDR 2015, seventh biennial conference on innovative data systems research, Asilomar, CA, USA, January 4\u20137, Online proceedings, 2015 [Online]. http:\/\/www.cidrdb.org\/cidr2015\/Papers\/CIDR15_Paper28.pdf"},{"key":"86_CR63","doi-asserted-by":"publisher","unstructured":"Costea A, Ionescu A, Raducanu B, Switakowski M, B\u00e2rca C, Sompolski J, Luszczak A, Szafranski M, de Nijs G, Boncz PA (2016) Vectorh: taking sql-on-hadoop to the next level. In: Proceedings of the 2016 international conference on management of data, SIGMOD conference 2016, San Francisco, CA, USA, June 26\u2013July 01, pp 1105\u20131117 [Online]. https:\/\/doi.org\/10.1145\/2882903.2903742","DOI":"10.1145\/2882903.2903742"},{"key":"86_CR64","doi-asserted-by":"crossref","unstructured":"Sch\u00e4tzle A, Przyjaciel-Zablocki M, Skilevic S, Lausen G (2016) S2RDF: RDF querying with SPARQL on spark. PVLDB 9(10):804\u2013815 [Online]. http:\/\/www.vldb.org\/pvldb\/vol9\/p804-schaetzle.pdf","DOI":"10.14778\/2977797.2977806"},{"key":"86_CR65","doi-asserted-by":"publisher","unstructured":"Cudr\u00e9-Mauroux P, Enchev I, Fundatureanu S, Groth PT, Haque A, Harth A, Keppmann FL, Miranker DP, Sequeda J, Wylot M (2013) Nosql databases for RDF: an empirical evaluation. In: The semantic Web\u2014ISWC 2013\u201412th international semantic web conference, Sydney, NSW, Australia, October 21\u201325, Proceedings, Part II, 2013, pp 310\u2013325 [Online]. https:\/\/doi.org\/10.1007\/978-3-642-41338-4_20","DOI":"10.1007\/978-3-642-41338-4_20"},{"key":"86_CR66","doi-asserted-by":"publisher","unstructured":"Appice A, Ceci M, Malerba D (2018) Relational data mining in the era of big data. In: Flesca S, Greco S, Masciari E, Sacc\u00e0 D (eds) A comprehensive guide through the Italian database research over the last 25 years. Springer, cham, pp 323\u2013339. https:\/\/doi.org\/10.1007\/978-3-319-61893-7_19","DOI":"10.1007\/978-3-319-61893-7_19"},{"key":"86_CR67","doi-asserted-by":"crossref","unstructured":"Khare S, An K, Gokhale AS, Tambe S, Meena A (2015) Reactive stream processing for data-centric publish\/subscribe. In: Proceedings of the 9th international conference on distributed event-based systems (DEBS). ACM, pp 234\u2013245","DOI":"10.1145\/2675743.2771880"},{"key":"86_CR68","doi-asserted-by":"crossref","unstructured":"Poggi F, Rossi D, Ciancarini P, Bompani L (2016) Semantic run-time models for self-adaptive systems: a case study. In: 2016 IEEE 25th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE). IEEE, pp 50\u201355","DOI":"10.1109\/WETICE.2016.20"},{"key":"86_CR69","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2015.11.121","volume":"209","author":"J-H Um","year":"2016","unstructured":"Um J-H, Lee S, Kim T-H, Jeong C-H, Song S-K, Jung H (2016) Semantic complex event processing model for reasoning research activities. Neurocomputing 209:39\u201345","journal-title":"Neurocomputing"},{"issue":"3","key":"86_CR70","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MC.2015.82","volume":"48","author":"M Giese","year":"2015","unstructured":"Giese M, Soylu A, Vega-Gorgojo G, Waaler A, Haase P, Jim\u00e9nez-Ruiz E, Lanti D, Rezk M, Xiao G, \u00d6z\u00e7ep \u00d6 et al (2015) Optique: zooming in on big data. Computer 48(3):60\u201367","journal-title":"Computer"},{"key":"86_CR71","unstructured":"Unece big data quality framework [Online]. http:\/\/www1.unece.org\/stat\/platform\/display\/bigdata\/2014+Project . Accessed 20 Feb 2018"},{"key":"86_CR72","doi-asserted-by":"crossref","unstructured":"Severin J, Lizio M, Harshbarger J, Kawaji H, Daub CO, Hayashizaki Y, Bertin N, Forrest AR, Consortium F et\u00a0al (2014) Interactive visualization and analysis of large-scale sequencing datasets using zenbu. Nat Biotechnol 32(3):217\u2013219","DOI":"10.1038\/nbt.2840"},{"issue":"12","key":"86_CR73","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s10916-015-0344-x","volume":"39","author":"E Mezghani","year":"2015","unstructured":"Mezghani E, Exposito E, Drira K, Da Silveira M, Pruski C (2015) A semantic big data platform for integrating heterogeneous wearable data in healthcare. J Med Syst 39(12):185","journal-title":"J Med Syst"},{"issue":"7232","key":"86_CR74","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1038\/nature07634","volume":"457","author":"J Ginsberg","year":"2009","unstructured":"Ginsberg J, Mohebbi M, Patel R, Brammer L, Smolinski M, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012\u20131014","journal-title":"Nature"},{"key":"86_CR75","unstructured":"Sculley D, Holt G, Golovin D, Davydov E, Phillips T, Ebner D, Chaudhary V, Young M, Crespo J-F, Dennison D (2015) Hidden technical debt in machine learning systems. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems 28, Curran Associates, Inc., pp 2503\u20132511. http:\/\/papers.nips.cc\/paper\/5656-hidden-technical-debt-in-machine-learning-systems.pdf"},{"key":"86_CR76","doi-asserted-by":"publisher","unstructured":"Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):4:1\u20134:26. https:\/\/doi.org\/10.1145\/1365815.1365816","DOI":"10.1145\/1365815.1365816"},{"key":"86_CR77","doi-asserted-by":"crossref","unstructured":"Suriarachchi I, Plale B (2016) Provenance as essential infrastructure for data lakes. In: Proceedings of international workshop on provenance and annotation of data and processes. LNCS 9672","DOI":"10.1007\/978-3-319-40593-3_16"},{"key":"86_CR78","unstructured":"Terrizzano I, Schwarz P, Roth M, Colino JE (2015) Data wrangling: the challenging journey from the wild to the lake. In: Proceedings of conference on innovative data systems research (CIDR)"},{"key":"86_CR79","unstructured":"Teradata (2014) Putting the data lake to work: a guide to best practices. http:\/\/www.teradata.com\/Resources\/Best-Practice-Guides\/Putting-the-Data-Lake-to-Work-A-Guide-to-Bes . Accessed on 20 June 2017 [Online]"},{"key":"86_CR80","doi-asserted-by":"crossref","unstructured":"Batini C, Scannapieco M (2016) Data and information quality\u2014dimensions. Principles and techniques, series. In: Data-centric systems and applications. Springer","DOI":"10.1007\/978-3-319-24106-7"},{"key":"86_CR81","volume-title":"Challenges and opportunities with big data","author":"D Agrawal","year":"2011","unstructured":"Agrawal D, Bernstein P, Bertino E, Davidson S, Dayal U, Franklin M, Gehrke J, Haas L, Halevy A, Han J et al (2011) Challenges and opportunities with big data. Purdue University, Cyber Center Technical Reports"},{"issue":"13","key":"86_CR82","first-page":"1561","volume":"9","author":"M Liu","year":"2016","unstructured":"Liu M, Wang Q (2016) Rogas: a declarative framework for network analytics. Proceedings of international conference on very large data bases (VLDB) 9(13):1561\u20131564","journal-title":"Proceedings of international conference on very large data bases (VLDB)"},{"key":"86_CR83","doi-asserted-by":"crossref","unstructured":"Hasan O, Habegger B, Brunie L, Bennani N, Damiani E (2013) A discussion of privacy challenges in user profiling with big data techniques: the EEXCESS use case. In: IEEE international congress on Big Data (BigData Congress). IEEE, pp 25\u201330","DOI":"10.1109\/BigData.Congress.2013.13"},{"key":"86_CR84","unstructured":"Doan A, Ardalan A, Ballard JR, Das S, Govind Y, Konda P, Li H, Paulson E, Zhang H et\u00a0al (2017) Toward a system building agenda for data integration. arXiv preprint arXiv:1710.00027"},{"key":"86_CR85","doi-asserted-by":"crossref","unstructured":"Flood M, Grant J, Luo H, Raschid L, Soboroff I, Yoo K (2016) Financial entity identification and information integration (feiii) challenge: the report of the organizing committee. In: Proceedings of the second international workshop on data science for macro-modeling. ACM, p 1","DOI":"10.1145\/2951894.2951904"},{"key":"86_CR86","doi-asserted-by":"crossref","unstructured":"Haryadi AF, Hulstijn J, Wahyudi A, Van Der Voort H, Janssen M (2016) Antecedents of big data quality: an empirical examination in financial service organizations. In: IEEE international conference on Big Data (Big Data). IEEE, pp 116\u2013121","DOI":"10.1109\/BigData.2016.7840595"},{"key":"86_CR87","first-page":"164","volume-title":"Context semantic analysis: a knowledge-based technique for computing inter-document similarity","author":"F Benedetti","year":"2016","unstructured":"Benedetti F, Beneventano D, Bergamaschi S (2016) Context semantic analysis: a knowledge-based technique for computing inter-document similarity. Springer International Publishing, Berlin, pp 164\u2013178"},{"key":"86_CR88","doi-asserted-by":"crossref","unstructured":"Ford E, Carroll JA, Smith HE, Scott D, Cassell JA (2016) Extracting information from the text of electronic medical records to improve case detection: a systematic review. J Am Med Inform Assoc 23(5):1007\u20131015. https:\/\/doi.org\/10.1093\/jamia\/ocv180","DOI":"10.1093\/jamia\/ocv180"},{"key":"86_CR89","doi-asserted-by":"publisher","unstructured":"Haas D, Krishnan S, Wang J, Franklin MJ, Wu E (2015) Wisteria: nurturing scalable data cleaning infrastructure. Proc VLDB Endow 8(12):2004\u20132007. https:\/\/doi.org\/10.14778\/2824032.2824122","DOI":"10.14778\/2824032.2824122"},{"key":"86_CR90","unstructured":"Cabot J, Toman D, Parsons J, Pastor O, Wrembel R (2016) Big data and conceptual models: are they mutually compatible? In: International conference on conceptual modeling (ER), panel discussion [Online]. http:\/\/er2016.cs.titech.ac.jp\/program\/panel.html . Accessed 20 Feb 2018"},{"key":"86_CR91","unstructured":"Voigt M, Pietschmann S, Grammel L, Mei\u00dfner K (2012) Context-aware recommendation of visualization components. In: Proceedings of the 4th international conference on information, process, and knowledge management. Citeseer, pp 101\u2013109"},{"key":"86_CR92","doi-asserted-by":"publisher","unstructured":"Soylu A, Giese M, Jimenez-Ruiz E, Kharlamov E, Zheleznyakov D, Horrocks I (2013) OptiqueVQS: towards an ontology-based visual query system for big data. In: Proceedings of the fifth international conference on management of emergent digital ecosystems, series, MEDES \u201913. ACM, New York, pp 119\u2013126 [Online]. https:\/\/doi.org\/10.1145\/2536146.2536149","DOI":"10.1145\/2536146.2536149"},{"issue":"2","key":"86_CR93","first-page":"71","volume":"50","author":"G McKenzie","year":"2015","unstructured":"McKenzie G, Janowicz K, Gao S, Yang J-A, Hu Y (2015) POI pulse: a multi-granular, semantic signature-based information observatory for the interactive visualization of big geosocial data. Cartographica Int J Geogr Inf Geovis 50(2):71\u201385","journal-title":"Cartographica Int J Geogr Inf Geovis"},{"key":"86_CR94","doi-asserted-by":"publisher","unstructured":"Habib MB, Van Keulen (2016) TwitterNEED: a hybrid approach for named entity extraction and disambiguation for tweet. Nat Lang Eng 22(3):423\u2013456. https:\/\/doi.org\/10.1017\/S1351324915000194","DOI":"10.1017\/S1351324915000194"},{"key":"86_CR95","doi-asserted-by":"publisher","unstructured":"Magnani M, Montesi D (2010) A survey on uncertainty management in data integration. JDIQ 2(1):5:1\u20135:33. https:\/\/doi.org\/10.1145\/1805286.1805291","DOI":"10.1145\/1805286.1805291"},{"key":"86_CR96","doi-asserted-by":"publisher","unstructured":"van Keulen M (2012) Managing uncertainty: the road towards better data interoperability. Inf Technol: IT 54(3):138\u2013146. https:\/\/doi.org\/10.1524\/itit.2012.0674","DOI":"10.1524\/itit.2012.0674"},{"key":"86_CR97","unstructured":"Andrews P, Kalro A, Mehanna H, Sidorov A (2016) Productionizing machine learning pipelines at scale. In: Machine learning systems workshop at ICML"},{"key":"86_CR98","doi-asserted-by":"crossref","unstructured":"Sparks ER, Venkataraman S, Kaftan T, Franklin MJ, Recht B (2017) Keystoneml: optimizing pipelines for large-scale advanced analytics. In: 2017 IEEE 33rd international conference on data engineering (ICDE), pp 535\u2013546","DOI":"10.1109\/ICDE.2017.109"},{"issue":"1","key":"86_CR99","first-page":"1235","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S et al (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(1):1235\u20131241","journal-title":"J Mach Learn Res"},{"issue":"12","key":"86_CR100","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.14778\/3137765.3137775","volume":"10","author":"J-H B\u00f6se","year":"2017","unstructured":"B\u00f6se J-H, Flunkert V, Gasthaus J, Januschowski T, Lange D, Salinas D, Schelter S, Seeger M, Wang Y (2017) Probabilistic demand forecasting at scale. Proc VLDB Endow 10(12):1694\u20131705","journal-title":"Proc VLDB Endow"},{"key":"86_CR101","doi-asserted-by":"crossref","unstructured":"Baylor D, Breck E, Cheng H-T, Fiedel N, Foo CY, Haque Z, Haykal S, Ispir M, Jain V, Koc L et\u00a0al (2017) Tfx: a tensorflow-based production-scale machine learning platform. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1387\u20131395","DOI":"10.1145\/3097983.3098021"},{"key":"86_CR102","doi-asserted-by":"crossref","unstructured":"Ardagna C, Ceravolo P, Cota GL, Kiani MM, Damiani E (2017) What are my users looking for when preparing a big data campaign. In: IEEE international congress on Big Data (BigData Congress). IEEE, pp 201\u2013208","DOI":"10.1109\/BigDataCongress.2017.35"},{"key":"86_CR103","unstructured":"Palm\u00e9r C (2017) Modelling eu directive 2016\/680 using enterprise architecture"},{"key":"86_CR104","unstructured":"Atzmueller M, Kluegl P, Puppe F (2008) Rule-based information extraction for structured data acquisition using textmarker. In: Proceedings of LWA, pp 1\u20137"},{"key":"86_CR105","unstructured":"Settles B (2011) Closing the loop: fast, interactive semi-supervised annotation with queries on features and instances. In: Proceedings of EMNLP.ACL, pp 1467\u20131478"},{"key":"86_CR106","first-page":"197","volume":"3","author":"C M\u00fcller","year":"2006","unstructured":"M\u00fcller C, Strube M (2006) Multi-level annotation of linguistic data with MMAX2. Corpus Technol Lang Pedag New Resour New Tools New Methods 3:197\u2013214","journal-title":"Corpus Technol Lang Pedag New Resour New Tools New Methods"},{"key":"86_CR107","unstructured":"Stenetorp P, Pyysalo S, Topi\u0107 G, Ohta T, Ananiadou S, Tsujii J (2012) Brat: a web-based tool for NLP-assisted text annotation. In: Proceedings of the demonstrations at the 13th conference of the European chapter of the association for computational linguistics. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 102\u2013107"},{"key":"86_CR108","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1007\/978-3-540-76298-0_52","volume-title":"The semantic web","author":"S Auer","year":"2007","unstructured":"Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) DBpedia: a nucleus for a web of open data. In: Aberer K, Choi K-S, Noy N, Allemang D, Lee K, Nixon L, Golbeck J, Mika P, Maynard D, Mizoguchi R, Schreiber G, Cudr\u00e9-Mauroux P (eds) The semantic web. Springer, Berlin, Heidelberg, pp 722\u2013735"},{"issue":"3","key":"86_CR109","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jswis.2009081901","volume":"5","author":"C Bizer","year":"2009","unstructured":"Bizer C, Heath T, Berners-Lee T (2009) Linked data\u2013the story so far. Int J Semant Web Inf Syst: IJSWIS 5(3):1\u201322","journal-title":"Int J Semant Web Inf Syst: IJSWIS"},{"key":"86_CR110","unstructured":"Benikova D, Biemann C (2016) Semreldata ? Multilingual contextual annotation of semantic relations between nominals: dataset and guidelines. In: LREC"},{"key":"86_CR111","doi-asserted-by":"crossref","unstructured":"Lu A, Wang W, Bansal M, Gimpel K, Livescu K (2015) Deep multilingual correlation for improved word embeddings. In: NAACL-HLT","DOI":"10.3115\/v1\/N15-1028"},{"key":"86_CR112","unstructured":"Pecina P, Toral A, Way A, Papavassiliou V, Prokopidis P, Giagkou M (2011) Towards using web-crawled data for domain adaptation in statistical machine translation. In: The 15th conference of the European association for machine translation (EAMT)"},{"key":"86_CR113","first-page":"25","volume-title":"Global Wikipedia: international and cross-cultural issues in online collaboration","author":"T Yasseri","year":"2014","unstructured":"Yasseri T, Spoerri A, Graham M, Kert\u00e9sz J (2014) The most controversial topics in Wikipedia: a multilingual and geographical analysis. In: Fichman P, Hara N (eds) Global Wikipedia: international and cross-cultural issues in online collaboration. Rowman & Littlefield Publishers Inc, Lanham, pp 25\u201348"},{"key":"86_CR114","doi-asserted-by":"crossref","unstructured":"Micher JC (2012) Improving domain-specific machine translation by constraining the language model. Army Research Laboratory, Technical Report of ARL-TN-0492","DOI":"10.21236\/ADA568649"},{"key":"86_CR115","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.dss.2015.12.002","volume":"82","author":"J D\u2019Haen","year":"2016","unstructured":"D\u2019Haen J, den Poel DV, Thorleuchter D, Benoit D (2016) Integrating expert knowledge and multilingual web crawling data in a lead qualification system. Decis Support Syst 82:69\u201378","journal-title":"Decis Support Syst"},{"issue":"1","key":"86_CR116","first-page":"165","volume":"55","author":"MA Helou","year":"2016","unstructured":"Helou MA, Palmonari M, Jarrar M (2016) Effectiveness of automatic translations for cross-lingual ontology mapping. J Artif Int Res 55(1):165\u2013208","journal-title":"J Artif Int Res"},{"key":"86_CR117","doi-asserted-by":"crossref","unstructured":"Furno D, Loia V, Veniero M, Anisetti M, Bellandi V, Ceravolo P, Damiani E (2011) Towards an agent-based architecture for managing uncertainty in situation awareness. In: 2011 IEEE symposium on intelligent agent (IA). IEEE, pp 1\u20136","DOI":"10.1109\/IA.2011.5953605"},{"key":"86_CR118","doi-asserted-by":"publisher","unstructured":"Dalvi N, R\u00e9 C, Suciu D (2009) Probabilistic databases: diamonds in the dirt. Commun ACM 52(7):86\u201394. https:\/\/doi.org\/10.1145\/1538788.1538810","DOI":"10.1145\/1538788.1538810"},{"key":"86_CR119","unstructured":"Ceravolo P, Damiani E, Fugazza C (2007) Trustworthiness-related uncertainty of semantic web-style metadata: a possibilistic approach. In: ISWC workshop on uncertainty reasoning for the semantic web (URSW), vol 327 [Sn], pp 131\u2013132"},{"key":"86_CR120","doi-asserted-by":"publisher","unstructured":"Panse F, van Keulen M, Ritter N (2013) Indeterministic handling of uncertain decisions in deduplication. JDIQ 4(2):91\u2013925. https:\/\/doi.org\/10.1145\/2435221.2435225","DOI":"10.1145\/2435221.2435225"},{"key":"86_CR121","doi-asserted-by":"publisher","unstructured":"Abedjan Z, Golab L, Naumann F (2015) Profiling relational data: a survey. VLDB J 24(4):557\u2013581. https:\/\/doi.org\/10.1007\/s00778-015-0389-y","DOI":"10.1007\/s00778-015-0389-y"},{"issue":"10","key":"86_CR122","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.14778\/2794367.2794377","volume":"8","author":"T Papenbrock","year":"2015","unstructured":"Papenbrock T, Ehrlich J, Marten J, Neubert T, Rudolph J-P, Sch\u00f6nberg M, Zwiener J, Naumann F (2015) Functional dependency discovery: an experimental evaluation of seven algorithms. Proc VLDB Endow 8(10):1082\u20131093","journal-title":"Proc VLDB Endow"},{"key":"86_CR123","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.ins.2014.01.015","volume":"275","author":"CLP Chen","year":"2014","unstructured":"Chen CLP, Zhang C (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314\u2013347","journal-title":"Inf Sci"},{"issue":"4","key":"86_CR124","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/2590989.2590995","volume":"42","author":"F Naumann","year":"2014","unstructured":"Naumann F (2014) Data profiling revisited. SIGMOD Rec 42(4):40\u201349","journal-title":"SIGMOD Rec"},{"key":"86_CR125","doi-asserted-by":"crossref","unstructured":"Ahmadov A, Thiele M, Eberius J, Lehner W, Wrembel R (2015) Towards a hybrid imputation approach using web tables. In: IEEE\/ACM international symposium on big data computing (BDC), pp 21\u201330","DOI":"10.1109\/BDC.2015.38"},{"key":"86_CR126","doi-asserted-by":"crossref","unstructured":"Ahmadov A, Thiele M, Lehner W, Wrembel R (2017) Context similarity for retrieval-based imputation. In: International symposium on foundations and applications of big data analytics (FAB) (to appear)","DOI":"10.1145\/3110025.3110161"},{"issue":"5","key":"86_CR127","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1007\/s11280-013-0263-z","volume":"17","author":"Z Li","year":"2014","unstructured":"Li Z, Sharaf MA, Sitbon L, Sadiq S, Indulska M, Zhou X (2014) A web-based approach to data imputation. World Wide Web 17(5):873\u2013897","journal-title":"World Wide Web"},{"key":"86_CR128","doi-asserted-by":"publisher","unstructured":"Miao X, Gao Y, Guo S, Liu W (2018) Incomplete data management: a survey. Front Comput Sci 12(1):4\u201325. https:\/\/doi.org\/10.1007\/s11704-016-6195-x","DOI":"10.1007\/s11704-016-6195-x"},{"issue":"3","key":"86_CR129","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/2.121508","volume":"25","author":"G Wiederhold","year":"1992","unstructured":"Wiederhold G (1992) Mediators in the architecture of future information systems. IEEE Comput 25(3):38\u201349","journal-title":"IEEE Comput"},{"key":"86_CR130","doi-asserted-by":"publisher","unstructured":"Tonon A, Demartini G, Cudr\u00e9-Mauroux P (2012) Combining inverted indices and structured search for ad-hoc object retrieval. In: The 35th international ACM SIGIR conference on research and development in information retrieval, SIGIR \u201912, Portland, OR, USA, August 12-16, pp 125\u2013134 [Online]. https:\/\/doi.org\/10.1145\/2348283.2348304","DOI":"10.1145\/2348283.2348304"},{"key":"86_CR131","doi-asserted-by":"publisher","unstructured":"Catasta M, Tonon A, Demartini G, Ranvier J, Aberer K, Cudr\u00e9-Mauroux P (2014) B-hist: entity-centric search over personal web browsing history. J Web Semant 27:19\u201325 [Online]. https:\/\/doi.org\/10.1016\/j.websem.2014.07.003","DOI":"10.1016\/j.websem.2014.07.003"},{"key":"86_CR132","first-page":"129","volume":"20","author":"M Flood","year":"2016","unstructured":"Flood M, Jagadish HV, Raschid L (2016) Big data challenges and opportunities in financial stability monitoring. Financ Stab Rev 20:129\u2013142","journal-title":"Financ Stab Rev"},{"issue":"6","key":"86_CR133","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1007\/s11704-016-6902-7","volume":"10","author":"LM Ni","year":"2016","unstructured":"Ni LM, Tan H, Xiao J (2016) Rethinking big data in a networked world. Front Comput Sci 10(6):965\u2013967","journal-title":"Front Comput Sci"},{"issue":"12","key":"86_CR134","first-page":"1878","volume":"5","author":"L Kolb","year":"2012","unstructured":"Kolb L, Thor A, Rahm E (2012) Dedoop: efficient deduplication with hadoop. PVLDB 5(12):1878\u20131881","journal-title":"PVLDB"},{"key":"86_CR135","doi-asserted-by":"publisher","unstructured":"Ghemawat S, Gobioff H, Leung S (2003) The google file system. In: Proceedings of the 19th ACM symposium on operating systems principles 2003, SOSP 2003, Bolton Landing, NY, USA, October 19\u201322, pp 29\u201343 [Online]. https:\/\/doi.org\/10.1145\/945445.945450","DOI":"10.1145\/945445.945450"},{"key":"86_CR136","doi-asserted-by":"crossref","unstructured":"Dittrich J, Quian\u00e9-Ruiz J, Richter S, Schuh S, Jindal A, Schad J (2012) Only aggressive elephants are fast elephants. PVLDB 5(11):1591\u20131602 [Online]. http:\/\/vldb.org\/pvldb\/vol5\/p1591_jensdittrich_vldb2012.pdf","DOI":"10.14778\/2350229.2350272"},{"key":"86_CR137","unstructured":"Carbone P, Katsifodimos A, Ewen S, Markl V, Haridi S, Tzoumas K (2015) Apache flink\u2122: stream and batch processing in a single engine. IEEE Data Eng Bull 38(4):28\u201338 [Online]. http:\/\/sites.computer.org\/debull\/A15dec\/p28.pdf"},{"key":"86_CR138","unstructured":"Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauly M, Franklin MJ, Shenker S, Stoica I (2012) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX symposium on networked systems design and implementation, NSDI 2012, San Jose, CA, USA, April 25\u201327, pp 15\u201328 [Online]. https:\/\/www.usenix.org\/conference\/nsdi12\/technical-sessions\/presentation\/zaharia . Accessed 20 Feb 2018"},{"key":"86_CR139","doi-asserted-by":"publisher","unstructured":"Armbrust M, Xin RS, Lian C, Huai Y, Liu D, Bradley JK, Meng X, Kaftan T, Franklin MJ, Ghodsi A, Zaharia M (2015) Spark SQL: relational data processing in spark. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, Melbourne, Victoria, Australia, May 31\u2013June 4, pp 1383\u20131394 [Online]. https:\/\/doi.org\/10.1145\/2723372.2742797","DOI":"10.1145\/2723372.2742797"},{"key":"86_CR140","unstructured":"Hagedorn S, G\u00f6tze P, Sattler K (2017) The STARK framework for spatio-temporal data analytics on spark. In: Datenbanksysteme f\u00fcr Business, Technologie und Web (BTW, 17. Fachtagung des GI-Fachbereichs, Datenbanken und Informationssysteme\" (DBIS), 6.-10. M\u00e4rz 2017. Stuttgart, Germany, Proceedings, pp 123\u2013142"},{"key":"86_CR141","unstructured":"Meng X, Bradley JK, Yavuz B, Sparks ER, Venkataraman S, Liu D, Freeman J, Tsai D B, Amde M, Owen S, Xin D, Xin R, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17:34:1\u201334:7 [Online]. http:\/\/jmlr.org\/papers\/v17\/15-237.html"},{"key":"86_CR142","doi-asserted-by":"crossref","unstructured":"Abouzeid A, Bajda-Pawlikowski K, Abadi DJ, Rasin A, Silberschatz A (2009) Hadoopdb: an architectural hybrid of mapreduce and DBMS technologies for analytical workloads. PVLDB 2(1):922\u2013933 [Online]. http:\/\/www.vldb.org\/pvldb\/2\/vldb09-861.pdf","DOI":"10.14778\/1687627.1687731"},{"key":"86_CR143","doi-asserted-by":"publisher","unstructured":"Du J, Wang H, Ni Y, Yu Y (2012) Hadooprdf: a scalable semantic data analytical engine. In: Intelligent computing theories and applications\u20148th international conference, ICIC 2012, Huangshan, China, July 25\u201329. Proceedings, pp 633\u2013641 [Online]. https:\/\/doi.org\/10.1007\/978-3-642-31576-3_80","DOI":"10.1007\/978-3-642-31576-3_80"},{"key":"86_CR144","doi-asserted-by":"publisher","unstructured":"Sch\u00e4tzle A, Przyjaciel-Zablocki M, Neu A, Lausen G (2014) Sempala: interactive SPARQL query processing on hadoop. In: The semantic Web\u2014ISWC 2014\u201413th international semantic web conference, Riva del Garda, Italy, October 19\u201323. Proceedings, Part I, pp 164\u2013179 [Online]. https:\/\/doi.org\/10.1007\/978-3-319-11964-9_11","DOI":"10.1007\/978-3-319-11964-9_11"},{"key":"86_CR145","unstructured":"Ladwig G, Harth A (2011) Cumulusrdf: linked data management on nested key-value stores. In: Proceedings of the 7th international workshop on scalable semantic web knowledge base systems (SSWS2011) at the 10th international semantic web conference (ISWC2011). Oktober 2011, Inproceedings"},{"key":"86_CR146","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2016.07.009","volume":"63","author":"A Corbellini","year":"2017","unstructured":"Corbellini A, Mateos C, Zunino A, Godoy D, Schiaffino S (2017) Persisting big-data: the NoSQL landscape. Inf Syst 63:1\u201323","journal-title":"Inf Syst"},{"key":"86_CR147","doi-asserted-by":"publisher","unstructured":"Barbar\u00e1 D (2002) Requirements for clustering data streams. SIGKDD Explor Newsl 3(2):23\u201327. https:\/\/doi.org\/10.1145\/507515.507519","DOI":"10.1145\/507515.507519"},{"issue":"1","key":"86_CR148","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/IDA-2007-11101","volume":"11","author":"J Gama","year":"2007","unstructured":"Gama J, Aguilar-Ruiz J (2007) Knowledge discovery from data streams. Intell Data Anal 11(1):1\u20132","journal-title":"Intell Data Anal"},{"key":"86_CR149","volume-title":"Big data in Austria","author":"M Meir-Huber","year":"2014","unstructured":"Meir-Huber M, K\u00f6hler M (2014) Big data in Austria. Austrian Ministry for Transport, Innovation and Technology (BMVIT), Technical report"},{"key":"86_CR150","doi-asserted-by":"crossref","unstructured":"Nural MV, Peng H, Miller JA (2017) Using meta-learning for model type selection in predictive big data analytics. In: 2017 IEEE international conference on Big Data (Big Data). IEEE, pp 2027\u20132036","DOI":"10.1109\/BigData.2017.8258149"},{"key":"86_CR151","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.ins.2017.09.050","volume":"423","author":"T Cunha","year":"2018","unstructured":"Cunha T, Soares C, de Carvalho AC (2018) Metalearning and recommender systems: a literature review and empirical study on the algorithm selection problem for collaborative filtering. Inf Sci 423:128\u2013144","journal-title":"Inf Sci"},{"issue":"10","key":"86_CR152","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MC.2009.326","volume":"42","author":"G Blair","year":"2009","unstructured":"Blair G, Bencomo N, France R (2009) Models@ run.time. Computer 42(10):22\u201327","journal-title":"Computer"},{"key":"86_CR153","doi-asserted-by":"crossref","unstructured":"Schmid S, Gerostathopoulos I, Prehofer C, Bures T (2017) Self-adaptation based on big data analytics: a model problem and tool. In: IEEE\/ACM 12th international symposium on software engineering for adaptive and self-managing systems (SEAMS). IEEE, pp 102\u2013108","DOI":"10.1109\/SEAMS.2017.20"},{"key":"86_CR154","doi-asserted-by":"crossref","unstructured":"Hartmann T, Moawad A, Fouquet F, Nain G, Klein J, Traon YL (2015) Stream my models: reactive peer-to-peer distributed models@run.time. In: Proceedings of the 18th international conference on model driven engineering languages and systems (MoDELS). ACM\/IEEE","DOI":"10.1109\/MODELS.2015.7338238"},{"issue":"6","key":"86_CR155","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1109\/TSC.2015.2493732","volume":"8","author":"W Aalst van der","year":"2015","unstructured":"van der Aalst W, Damiani E (2015) Processes meet big data: connecting data science with process science. IEEE Trans Serv Comput 8(6):810\u2013819","journal-title":"IEEE Trans Serv Comput"},{"key":"86_CR156","volume-title":"The power of events: an introduction to complex event processing in distributed enterprise systems","author":"DC Luckham","year":"2001","unstructured":"Luckham DC (2001) The power of events: an introduction to complex event processing in distributed enterprise systems. Addison-Wesley, Boston"},{"issue":"8","key":"86_CR157","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1145\/2240236.2240257","volume":"55","author":"WMP Aalst van der","year":"2012","unstructured":"van der Aalst WMP (2012) Process mining. Commun ACM 55(8):76\u201383","journal-title":"Commun ACM"},{"key":"86_CR158","unstructured":"van der Aalst WMP, Adriansyah A, de Medeiros AKA, Arcieri F, Baier T, Blickle T, Bose RPJC, van den Brand P, Brandtjen R, Buijs JCAM, Burattin A, Carmona J, Castellanos M, Claes J, Cook J, Costantini N, Curbera F, Damiani E, de Leoni M, Delias P, van Dongen BF, Dumas M, Dustdar S, Fahland D, Ferreira DR, Gaaloul W, van Geffen F, Goel S, G\u00fcnther CW, Guzzo A, Harmon P, ter Hofstede AHM, Hoogland J, Ingvaldsen JE, Kato K, Kuhn R, Kumar A, Rosa ML, Maggi FM, Malerba D, Mans RS, Manuel A, McCreesh M, Mello P, Mendling J, Montali M, Nezhad H R M, zur Muehlen M, Munoz-Gama J, Pontieri L, Ribeiro J, Rozinat A, P\u00e9rez HS, P\u00e9rez RS, Sep\u00falveda M, Sinur J, Soffer P, Song M, Sperduti A, Stilo G, Stoel C, Swenson KD, Talamo M, Tan W, Turner C, Vanthienen J, Varvaressos G, Verbeek E, Verdonk M, Vigo R, Wang J, Weber B, Weidlich M, Weijters T, Wen L, Westergaard M, Wynn MT (2011) Process mining manifesto. In: Proceedings of the business process management workshops (BPM). Springer, pp 169\u2013194"},{"key":"86_CR159","doi-asserted-by":"publisher","DOI":"10.1002\/0471741442","volume-title":"Process-aware information systems: bridging people and software through process technology","author":"M Dumas","year":"2005","unstructured":"Dumas M, van der Aalst WMP, ter Hofstede AHM (2005) Process-aware information systems: bridging people and software through process technology. Wiley, Hoboken"},{"key":"86_CR160","unstructured":"van Dongen BF, van der Aalst WMP (2005) A meta model for process mining data. In: Proceedings of the international workshop on enterprise modelling and ontologies for interoperability (EMOI) co-located with the 17th conference on advanced information systems engineering (CAiSE)"},{"key":"86_CR161","unstructured":"Al-Ali H, Damiani E, Al-Qutayri M, Abu-Matar M, Mizouni R (2016) Translating bpmn to business rules. In: International symposium on data-driven process discovery and analysis. Springer, pp 22\u201336"},{"issue":"3","key":"86_CR162","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1197\/jamia.M1733","volume":"12","author":"G Hripcsak","year":"2005","unstructured":"Hripcsak G, Rothschild AS (2005) Agreement, the f-measure, and reliability in information retrieval. J Am Med Inform Assoc 12(3):296\u2013298","journal-title":"J Am Med Inform Assoc"},{"key":"86_CR163","doi-asserted-by":"publisher","unstructured":"Gilson O, Silva N, Grant PW, Chen M (2008) From web data to visualization via ontology mapping. Coput Graph Forum 27(3):959\u2013966. https:\/\/doi.org\/10.1111\/j.1467-8659.2008.01230.x","DOI":"10.1111\/j.1467-8659.2008.01230.x"},{"key":"86_CR164","unstructured":"Nazemi K, Burkhardt D, Breyer M, Stab C, Fellner DW (2010) Semantic visualization cockpit: adaptable composition of semantics-visualization techniques for knowledge-exploration. In: International association of online engineering (IAOE): international conference interactive computer aided learning, pp 163\u2013173"},{"key":"86_CR165","doi-asserted-by":"crossref","unstructured":"Nazemi K, Breyer M, Forster J, Burkhardt D, Kuijper A (2011) Interacting with semantics: a user-centered visualization adaptation based on semantics data. In: Smith MJ, Salvendy G (eds) Human interface and the management of information. Interacting with information. Springer, Berlin, Heidelberg pp 239\u2013248","DOI":"10.1007\/978-3-642-21793-7_28"},{"key":"86_CR166","doi-asserted-by":"crossref","unstructured":"Melo C, Mikheev A, Le-Grand B, Aufaure M-A (2012) Cubix: a visual analytics tool for conceptual and semantic data. In: IEEE 12th international conference on data mining workshops (ICDMW). IEEE, pp 894\u2013897","DOI":"10.1109\/ICDMW.2012.41"},{"key":"86_CR167","doi-asserted-by":"publisher","unstructured":"Fluit C, Sabou M, Van Harmelen F (2006) Ontology-Based information visualization: toward semantic web applications. In: Geroimenko V, Chen C (eds) Visualizing the semantic Web: XML-Based internet and information visualization. Springer, London, pp 45\u201358. https:\/\/doi.org\/10.1007\/1-84628-290-X_3","DOI":"10.1007\/1-84628-290-X_3"},{"issue":"2","key":"86_CR168","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.websem.2007.03.005","volume":"5","author":"S Krivov","year":"2007","unstructured":"Krivov S, Williams R, Villa F (2007) Growl: a tool for visualization and editing of owl ontologies. Web Semant Sci Serv Agents World Wide Web 5(2):54\u201357","journal-title":"Web Semant Sci Serv Agents World Wide Web"},{"key":"86_CR169","unstructured":"Chu D, Sheets DA, Zhao Y, Wu Y, Yang J, Zheng M, Chen G (2014) Visualizing hidden themes of taxi movement with semantic transformation. In: Visualization symposium (PacificVis), IEEE pacific. IEEE, pp 137\u2013144"},{"key":"86_CR170","doi-asserted-by":"publisher","unstructured":"Catarci T, Scannapieco M, Console M, Demetrescu C (2017) My (fair) big data. In: 2017 IEEE international conference on Big Data, BigData 2017, Boston, MA, USA, December 11\u201314, pp 2974\u20132979 [Online]. https:\/\/doi.org\/10.1109\/BigData.2017.8258267","DOI":"10.1109\/BigData.2017.8258267"},{"key":"86_CR171","unstructured":"Oracle (2015) The five most common big data integration mistakes to avoid, white paper. http:\/\/er2016.cs.titech.ac.jp\/program\/panel.html . Accessed 20 June 2017 [Online]"},{"key":"86_CR172","doi-asserted-by":"publisher","unstructured":"Ali SMF, Wrembel R (2017) From conceptual design to performance optimization of ETL workflows: current state of research and open problems. VLDB J. [Online]. https:\/\/doi.org\/10.1007\/s00778-017-0477-2","DOI":"10.1007\/s00778-017-0477-2"},{"key":"86_CR173","doi-asserted-by":"publisher","unstructured":"Olston C, Reed B, Srivastava U, Kumar R, Tomkins A (2008) Pig latin: a not-so-foreign language for data processing. In: Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD, Vancouver, BC, Canada, June 10\u201312, pp 1099\u20131110 [Online]. https:\/\/doi.org\/10.1145\/1376616.1376726","DOI":"10.1145\/1376616.1376726"},{"key":"86_CR174","doi-asserted-by":"publisher","unstructured":"Venkataraman S, Yang Z, Liu D, Liang E, Falaki H, Meng X, Xin R, Ghodsi A, Franklin MJ, Stoica I, Zaharia M (2016) Sparkr: scaling R programs with spark. In: Proceedings of the 2016 international conference on management of data, SIGMOD conference 2016, San Francisco, CA, USA, June 26\u2013July 01, pp 1099\u20131104 [Online]. https:\/\/doi.org\/10.1145\/2882903.2903740","DOI":"10.1145\/2882903.2903740"},{"key":"86_CR175","unstructured":"Dinter B, Gluchowski P, Schieder C (2015) A stakeholder lens on metadata management in business intelligence and big data-results of an empirical investigation"},{"key":"86_CR176","unstructured":"Yazici A, George R (1999) Fuzzy database modeling, ser. Studies in fuzziness and soft computing. Physica Verlag, vol 26. iSBN 978-3-7908-1171-1"},{"key":"86_CR177","doi-asserted-by":"crossref","DOI":"10.1515\/9780691214696","volume-title":"A mathematical theory of evidence","author":"G Shafer","year":"1976","unstructured":"Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton"},{"key":"86_CR178","doi-asserted-by":"publisher","unstructured":"Wanders B, van Keulen M, van der Vet P (2015) Uncertain groupings: probabilistic combination of grouping data. In: Proceedings of DEXA, ser. LNCS, vol 9261. Springer, pp 236\u2013250. https:\/\/doi.org\/10.1007\/978-3-319-22849-5_17","DOI":"10.1007\/978-3-319-22849-5_17"},{"key":"86_CR179","doi-asserted-by":"publisher","unstructured":"Huang J, Antova L, Koch C, Olteanu D (2009) MayBMS: a probabilistic database management system. In: Proceedings of SIGMOD. ACM, pp 1071\u20131074. https:\/\/doi.org\/10.1145\/1559845.1559984","DOI":"10.1145\/1559845.1559984"},{"issue":"4\u20135","key":"86_CR180","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.is.2008.06.001","volume":"34","author":"M Thiele","year":"2009","unstructured":"Thiele M, Fischer U, Lehner W (2009) Partition-based workload scheduling in living data warehouse environments. Inf Syst 34(4\u20135):382\u2013399","journal-title":"Inf Syst"},{"key":"86_CR181","unstructured":"Angelini M, Santucci G (2013) Modeling incremental visualizations. In: Proceedings of the EuroVis workshop on visual analytics (EuroVA13), pp 13\u201317"},{"issue":"7","key":"86_CR182","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1109\/TVCG.2015.2462356","volume":"22","author":"H-J Schulz","year":"2016","unstructured":"Schulz H-J, Angelini M, Santucci G, Schumann H (2016) An enhanced visualization process model for incremental visualization. IEEE Trans Vis Comput Graph 22(7):1830\u20131842","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"12","key":"86_CR183","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1109\/TVCG.2014.2346574","volume":"20","author":"CD Stolper","year":"2014","unstructured":"Stolper CD, Perer A, Gotz D (2014) Progressive visual analytics: user-driven visual exploration of in-progress analytics. IEEE Trans Vis Comput Graph 20(12):1653\u20131662","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"86_CR184","unstructured":"Fekete J-D, Primet R (2016) Progressive analytics: a computation paradigm for exploratory data analysis. arXiv preprint arXiv:1607.05162"},{"issue":"5","key":"86_CR185","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1109\/TVCG.2006.166","volume":"12","author":"B Shneiderman","year":"2006","unstructured":"Shneiderman B, Aris A (2006) Network visualization by semantic substrates. IEEE Trans Vis Comput Graph 12(5):733\u2013740","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"4","key":"86_CR186","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.jmsy.2013.04.008","volume":"32","author":"D Wu","year":"2013","unstructured":"Wu D, Greer MJ, Rosen DW, Schaefer D (2013) Cloud manufacturing: strategic vision and state-of-the-art. J Manuf Syst 32(4):564\u2013579","journal-title":"J Manuf Syst"},{"key":"86_CR187","first-page":"2","volume":"14","author":"KE Martin","year":"2015","unstructured":"Martin KE (2015) Ethical issues in the big data industry. MIS Q Exec 14:2","journal-title":"MIS Q Exec"}],"container-title":["Journal on Data Semantics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13740-018-0086-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13740-018-0086-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13740-018-0086-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T01:14:19Z","timestamp":1661303659000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13740-018-0086-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,23]]},"references-count":187,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["86"],"URL":"https:\/\/doi.org\/10.1007\/s13740-018-0086-2","relation":{},"ISSN":["1861-2032","1861-2040"],"issn-type":[{"value":"1861-2032","type":"print"},{"value":"1861-2040","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,23]]},"assertion":[{"value":"17 April 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}