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There is a need for new architectural, software, and hardware solutions that are capable of providing dynamic data integration, assuring high data quality, and offering safety and security mechanisms, while facilitating online data analysis. In this context, we propose<jats:italic>moduli<\/jats:italic>, a novel disaggregated data management reference architecture for data-intensive applications that organizes data processing in various<jats:italic>zones.<\/jats:italic>Working on<jats:italic>moduli<\/jats:italic>allowed us also to identify open research and technological challenges.<\/jats:p>","DOI":"10.1145\/3643603.3643607","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T23:14:46Z","timestamp":1708470886000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["moduli: A Disaggregated Data Management Architecture for Data-Intensive Workflows"],"prefix":"10.1145","volume":"2024","author":[{"given":"Paolo","family":"Ceravolo","sequence":"first","affiliation":[{"name":"Universit\u00e0 degli Studi di Milano"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiziana","family":"Catarci","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Console","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Cudr\u00e9-Mauroux","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Fribourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sven","family":"Groppe","sequence":"additional","affiliation":[{"name":"University of L\u00fcbeck"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katja","family":"Hose","sequence":"additional","affiliation":[{"name":"TU Wien and Aalborg University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaroslav","family":"Pokorn\u00fd","sequence":"additional","affiliation":[{"name":"Charles University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oscar","family":"Romero","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Catalunya"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Wrembel","sequence":"additional","affiliation":[{"name":"Poznan University of Technology and Artificial Intelligence and Cybersecurity Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"volume-title":"IEEE\/ACM Int. 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Yeung Eds."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331166"},{"volume-title":"Int. Conf. on Field Programmable Logic and Applications (FPL). IEEE, 1--4.","author":"Nurvitadhi E.","key":"e_1_2_1_69_1","unstructured":"Nurvitadhi, E., Sim, J., Sheffield, D., Mishra, A., Krishnan, S., and Marr, D. 2016. Accelerating recurrent neural networks in analytics servers: Comparison of FPGA, CPU, GPU, and ASIC. In Int. Conf. on Field Programmable Logic and Applications (FPL). IEEE, 1--4."},{"key":"e_1_2_1_70_1","doi-asserted-by":"crossref","unstructured":"Paggi H. Soriano J. Lara J. A. and Damiani E. 2021. Towards the definition of an information quality metric for information fusion models. Comput. Electr. Eng. 89.","DOI":"10.1016\/j.compeleceng.2020.106907"},{"volume-title":"Int. Conf. on Management of Data (SIGMOD). ACM.","author":"Park K.","key":"e_1_2_1_71_1","unstructured":"Park, K., Saur, K., Banda, D., Sen, R., Interlandi, M., and Karanasos, K. 2022. End-to-end optimization of machine learning prediction queries. In Int. Conf. on Management of Data (SIGMOD). ACM."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3552490.3552496"},{"volume-title":"Annual Conf. on Neural Information Processing Systems (NIPS), D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, and R. Garnett, Eds. 3567--3575","author":"Ratner A. J.","key":"e_1_2_1_73_1","unstructured":"Ratner, A. J., Sa, C. D., Wu, S., Selsam, D., and R\u00e9, C. 2016. Data programming: Creating large training sets, quickly. In Annual Conf. on Neural Information Processing Systems (NIPS), D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, and R. Garnett, Eds. 3567--3575."},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.113.130503"},{"key":"e_1_2_1_75_1","volume-title":"Int. Conf. Big Data Analytics and Knowledge Discovery (DAWAK). LNCS","volume":"12393","author":"Romero O.","unstructured":"Romero, O. and Wrembel, R. 2020. Data engineering for data science: Two sides of the same coin. In Int. Conf. Big Data Analytics and Knowledge Discovery (DAWAK). LNCS, vol. 12393. Springer, 157--166."},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3434642"},{"volume-title":"Data Quality Dimensions","author":"Sattler K.-U.","key":"e_1_2_1_77_1","unstructured":"Sattler, K.-U. 2009. Data Quality Dimensions. Springer, 612--615."},{"key":"e_1_2_1_78_1","article-title":"Towards a method for data accuracy assessment utilizing a bayesian network learning algorithm","volume":"1","author":"Sessions V.","year":"2009","unstructured":"Sessions, V. and Valtorta, M. 2009. Towards a method for data accuracy assessment utilizing a bayesian network learning algorithm. ACM J. Data Inf. Qual. 1, 3, 14:1--14:34.","journal-title":"ACM J. Data Inf. Qual."},{"key":"e_1_2_1_79_1","unstructured":"Stedman C. 2022. What is data governance and why does it matter? https:\/\/www.techtarget.com\/searchdatamanagement\/definition\/data-governance."},{"key":"e_1_2_1_80_1","unstructured":"Stein D. 2022. Open sourcing feathr - linkedin's feature store for productive machine learning."},{"key":"e_1_2_1_81_1","first-page":"3","article-title":"Data Integration: The Current Status and the Way Forward","volume":"41","author":"Stonebraker M.","year":"2018","unstructured":"Stonebraker, M. and Ilyas, I. F. 2018. Data Integration: The Current Status and the Way Forward. IEEE Data Eng. Bull. 41, 2, 3--9.","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_82_1","volume-title":"LNCS","volume":"9672","author":"Suriarachchi I.","unstructured":"Suriarachchi, I. and Plale, B. 2016. Provenance as essential infrastructure for data lakes. In Int. Provenance and Annotation Worksh. (IPAW). LNCS, vol. 9672. Springer, 178--182."},{"key":"e_1_2_1_83_1","volume-title":"Workshops at the Int. Conf. on Very Large Data Bases VLDB. CEUR Workshop Proceedings","volume":"3462","author":"Tagliabue J.","unstructured":"Tagliabue, J., Greco, C., and Bigon, L. 2023. Building a serverless data lakehouse from spare parts. In Workshops at the Int. Conf. on Very Large Data Bases VLDB. CEUR Workshop Proceedings, vol. 3462. CEUR-WS.org."},{"volume-title":"Biennial Conf. on Innovative Data Systems Research (CIDR).","author":"Terrizzano I. G.","key":"e_1_2_1_84_1","unstructured":"Terrizzano, I. G., Schwarz, P. M., Roth, M., and Colino, J. E. 2015. Data wrangling: The challenging yourney from the wild to the lake. In Biennial Conf. on Innovative Data Systems Research (CIDR)."},{"key":"e_1_2_1_85_1","volume-title":"Conf. Artificial Intelligence Applications and Innovations AIAI. IFIP Advances in Information and Communication Technology","volume":"675","author":"Villamizar N.","unstructured":"Villamizar, N., Wahrman, J., and Villasana, M. 2023. Comparing vectorization techniques, supervised and unsupervised classification methods for scientific publication categorization in the UNESCO taxonomy. In IFIP WG 12.5 Int. Conf. Artificial Intelligence Applications and Innovations AIAI. IFIP Advances in Information and Communication Technology, vol. 675. Springer, 356--368."},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/240455.240479"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.404034"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.1996.11518099"},{"key":"e_1_2_1_89_1","unstructured":"Ward J. S. and Barker A. 2013. Undefined by data: A survey of big data definitions. CoRR abs\/1309.5821."},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/2.121508"},{"volume-title":"Int. Conf. on Management of Data (SIGMOD).","author":"Winker T.","key":"e_1_2_1_91_1","unstructured":"Winker, T., Groppe, S., Uotila, V., Yan, Z., Lu, J., Franz, M., and Mauerer, W. 2023. Quantum machine learning: Foundation, new techniques, and opportunities for database research. In Int. Conf. on Management of Data (SIGMOD)."},{"volume-title":"Proceedings of the International Workshop on Big Data in Emergent Distributed Environments (BiDEDE)","author":"Winker T.","key":"e_1_2_1_92_1","unstructured":"Winker, T., \u00c7alikyilmaz, U., Gruenwald, L., and Groppe, S. 2023. Quantum machine learning for join order optimization using variational quantum circuits. In Proceedings of the International Workshop on Big Data in Emergent Distributed Environments (BiDEDE), Seattle, WA, USA."},{"key":"e_1_2_1_93_1","series-title":"Lecture Notes in Computer Science","volume-title":"Int. Conf. Database and Expert Systems Applications DEXA","author":"Wrembel R.","unstructured":"Wrembel, R. 2023. Data integration revitalized: From data warehouse through data lake to data mesh. In Int. Conf. Database and Expert Systems Applications DEXA. Lecture Notes in Computer Science, vol. 14146. Springer, 3--18."},{"volume-title":"Int. Conf. on Management of Data (SIGMOD).","author":"Xu L.","key":"e_1_2_1_94_1","unstructured":"Xu, L., Qiu, S., Yuan, B., Jiang, J., Renggli, C., Gan, S., Kara, K., Li, G., Liu, J., Wu, W., Ye, J., and Zhang, C. 2022. In-database machine learning with CorgiPile: Stochastic gradient descent without full data shuffle. In Int. 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