{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:24OOSVYXTVOXIM5SQZVLHFFQ7A","short_pith_number":"pith:24OOSVYX","schema_version":"1.0","canonical_sha256":"d71ce957179d5d7433b2866ab394b0f82485f992ce4f85c1c405e1754c75abf8","source":{"kind":"arxiv","id":"2209.05358","version":1},"attestation_state":"computed","paper":{"title":"BottleMod: Modeling Data Flows and Tasks for Fast Bottleneck Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.DC","authors_text":"Ansgar L\\\"o{\\ss}er, Bj\\\"orn Scheuermann, Florian Schintke, Joel Witzke","submitted_at":"2022-09-12T16:10:32Z","abstract_excerpt":"In the recent years, scientific workflows gained more and more popularity. In scientific workflows, tasks are typically treated as black boxes. Dealing with their complex interrelations to identify optimization potentials and bottlenecks is therefore inherently hard. The progress of a scientific workflow depends on several factors, including the available input data, the available computational power, and the I/O and network bandwidth. Here, we tackle the problem of predicting the workflow progress with very low overhead. To this end, we look at suitable formalizations for the key parameters a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2209.05358","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2022-09-12T16:10:32Z","cross_cats_sorted":["cs.NI"],"title_canon_sha256":"442f8176051eef5896c0dc93abf2326ae285861232e2b7f22bb58c88365af472","abstract_canon_sha256":"d6d9311c3feae4586e96aaf18b09b2bf5c93aa715efd40a4a37580d1ae166bcf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:29.746905Z","signature_b64":"YUh4MLQmaglssOSOVqWCnjhPOIciQBtkec/UnsR9U60olAI//bNhacl982tq/LODLDzlxO3ps/iYiyhaveLfAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d71ce957179d5d7433b2866ab394b0f82485f992ce4f85c1c405e1754c75abf8","last_reissued_at":"2026-07-05T04:56:29.746394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:29.746394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BottleMod: Modeling Data Flows and Tasks for Fast Bottleneck Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.DC","authors_text":"Ansgar L\\\"o{\\ss}er, Bj\\\"orn Scheuermann, Florian Schintke, Joel Witzke","submitted_at":"2022-09-12T16:10:32Z","abstract_excerpt":"In the recent years, scientific workflows gained more and more popularity. In scientific workflows, tasks are typically treated as black boxes. Dealing with their complex interrelations to identify optimization potentials and bottlenecks is therefore inherently hard. The progress of a scientific workflow depends on several factors, including the available input data, the available computational power, and the I/O and network bandwidth. Here, we tackle the problem of predicting the workflow progress with very low overhead. To this end, we look at suitable formalizations for the key parameters a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.05358","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.05358/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2209.05358","created_at":"2026-07-05T04:56:29.746460+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.05358v1","created_at":"2026-07-05T04:56:29.746460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.05358","created_at":"2026-07-05T04:56:29.746460+00:00"},{"alias_kind":"pith_short_12","alias_value":"24OOSVYXTVOX","created_at":"2026-07-05T04:56:29.746460+00:00"},{"alias_kind":"pith_short_16","alias_value":"24OOSVYXTVOXIM5S","created_at":"2026-07-05T04:56:29.746460+00:00"},{"alias_kind":"pith_short_8","alias_value":"24OOSVYX","created_at":"2026-07-05T04:56:29.746460+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A","json":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A.json","graph_json":"https://pith.science/api/pith-number/24OOSVYXTVOXIM5SQZVLHFFQ7A/graph.json","events_json":"https://pith.science/api/pith-number/24OOSVYXTVOXIM5SQZVLHFFQ7A/events.json","paper":"https://pith.science/paper/24OOSVYX"},"agent_actions":{"view_html":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A","download_json":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A.json","view_paper":"https://pith.science/paper/24OOSVYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.05358&json=true","fetch_graph":"https://pith.science/api/pith-number/24OOSVYXTVOXIM5SQZVLHFFQ7A/graph.json","fetch_events":"https://pith.science/api/pith-number/24OOSVYXTVOXIM5SQZVLHFFQ7A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A/action/storage_attestation","attest_author":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A/action/author_attestation","sign_citation":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A/action/citation_signature","submit_replication":"https://pith.science/pith/24OOSVYXTVOXIM5SQZVLHFFQ7A/action/replication_record"}},"created_at":"2026-07-05T04:56:29.746460+00:00","updated_at":"2026-07-05T04:56:29.746460+00:00"}