{"paper":{"title":"SOFA: An Extensible Logical Optimizer for UDF-heavy Dataflows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Arvid Heise, Astrid Rheinl\\\"ander, Fabian Hueske, Felix Naumann, Ulf Leser","submitted_at":"2013-11-25T15:26:49Z","abstract_excerpt":"Recent years have seen an increased interest in large-scale analytical dataflows on non-relational data. These dataflows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant building blocks of such dataflows are user-defined predicates or functions (UDFs). However, the heavy use of UDFs is not well taken into account for dataflow optimization in current systems.\n  SOFA is a novel and extensible optimizer for UDF-heavy dataflows. It builds on a concise set of properties for describing the semantics of Map/Reduce-style UDFs and a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.6335","kind":"arxiv","version":1},"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"}