logseq/pages/hls__managingDataIntensiveEcosystems-chapter_1668416249546_0.md

file:: managingDataIntensiveEcosystems-chapter_1668416249546_0.pdf file-path:: ../assets/managingDataIntensiveEcosystems-chapter_1668416249546_0.pdf

• the side effects on database design, querying, and maintenance are not well-known. ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 63720458-b3d7-4755-85dd-f269baa9a5f2
• We cover different processes, including automatic database query extraction, bad smell detection, self-admitted technical debt analysis, and evolution history visualization. ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 6372046c-787f-4b27-b745-7500ae97a37d
• If the query is not well-formed or not handled correctly in the program code, it generates extra load on the database side that affects the application’s performance [1] [2]. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63720515-20a3-424e-96ea-dca34bfecbb9 hl-stamp:: 1668416793160
• discuss mining techniques ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63720569-687e-4ca8-a1cc-ae12e194c16c
• static analysis and visualization techniques that exploit the mined information on the storage and manipulation of the ecosystem data ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63720575-b6e6-42d9-a30d-b49ee8de4753
• findings ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6372057b-b341-4277-9ad6-a35da5a8c1aa
• concluding remarks ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63720580-9947-4dbf-b009-ebb2f34c5ad8