Nagy, Csaba Lanza, Michele Cleve, Anthony 2023 Managing data-intensive software ecosystems has long been considered an expensive and error-prone process. This is mainly due to the often implicit consistency relationships between applications and their database(s). In addition, as new technologies emerged for specialized purposes (e.g., key-value stores, document stores, graph databases), the common use of multiple database models within the same software (eco)system has also become more popular. There are undeniable benefits of such multi-database models where developers use and combine technologies. However, the side effects on database design, querying, and maintenance are not well-known. This chapter elaborates on the recent research effort devoted to mining, analyzing, and evolving data-intensive software ecosystems. It focuses on methods, techniques, and tools providing developers with automated support. It covers different processes, including automatic database query extraction, bad smell detection, self-admitted technical debt analysis, and evolution history visualization. application/pdf https://n2t.net/ark:/12658/srd1329625 https://susi.usi.ch/global/documents/329625 https://susi.usi.ch/documents/329625/files/Nagy_Lanza_2023_Springer_Software Ecosystems_Book chapter.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-36060-2_11 info:eu-repo/semantics/altIdentifier/ark/12658/srd1329625 info:eu-repo/semantics/openAccess License undefined Software ecosystems / Tom Mens ; Coen De Roover ; Anthony Cleve . - Cham : Springer International Publishing. - 2023, p. 281–314 info:eu-repo/classification/udc/004 Mining, analyzing, and evolving data-intensive software ecosystems http://purl.org/coar/resource_type/c_3248