{"paper":{"title":"Optimizing Index Deployment Order for Evolving OLAP (Extended Version)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Alexander Rasin, Carleton Coffrin, Hideaki Kimura, Stanley B. Zdonik","submitted_at":"2011-07-19T01:52:52Z","abstract_excerpt":"Query workloads and database schemas in OLAP applications are becoming increasingly complex. Moreover, the queries and the schemas have to continually \\textit{evolve} to address business requirements. During such repetitive transitions, the \\textit{order} of index deployment has to be considered while designing the physical schemas such as indexes and MVs.\n  An effective index deployment ordering can produce (1) a prompt query runtime improvement and (2) a reduced total deployment time. Both of these are essential qualities of design tools for quickly evolving databases, but optimizing the pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1107.3606","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}