{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:SYBOYR67IKQMY3SVNZPMFALI2Z","short_pith_number":"pith:SYBOYR67","canonical_record":{"source":{"id":"1604.08484","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-04-28T16:00:38Z","cross_cats_sorted":["cs.AR","cs.PF"],"title_canon_sha256":"330f5f43b63a12b6f3df0d190ea4e87074db728be5a97503f96765c59bfed1b9","abstract_canon_sha256":"7ba392f44051b77c6fdffa2acbe9babe2f3247f338e42bc55e5c42e0bcf72295"},"schema_version":"1.0"},"canonical_sha256":"9602ec47df42a0cc6e556e5ec28168d6445095fabaf375baa3f407c72459b484","source":{"kind":"arxiv","id":"1604.08484","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.08484","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"arxiv_version","alias_value":"1604.08484v1","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.08484","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"pith_short_12","alias_value":"SYBOYR67IKQM","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"SYBOYR67IKQMY3SV","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"SYBOYR67","created_at":"2026-05-18T12:30:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:SYBOYR67IKQMY3SVNZPMFALI2Z","target":"record","payload":{"canonical_record":{"source":{"id":"1604.08484","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-04-28T16:00:38Z","cross_cats_sorted":["cs.AR","cs.PF"],"title_canon_sha256":"330f5f43b63a12b6f3df0d190ea4e87074db728be5a97503f96765c59bfed1b9","abstract_canon_sha256":"7ba392f44051b77c6fdffa2acbe9babe2f3247f338e42bc55e5c42e0bcf72295"},"schema_version":"1.0"},"canonical_sha256":"9602ec47df42a0cc6e556e5ec28168d6445095fabaf375baa3f407c72459b484","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:16:03.846784Z","signature_b64":"dYJiOF21SA8FUp0ctK66SR1ikhXNMfgvXdauUbeteW23j5/oGpUhBLTYh6Zcc6o/94491aKLPjM27ue8DH0zCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9602ec47df42a0cc6e556e5ec28168d6445095fabaf375baa3f407c72459b484","last_reissued_at":"2026-05-18T01:16:03.845912Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:16:03.845912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1604.08484","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:16:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YaA9tZJbWP3SLP/z50+SPvYOEPXMDLaoanw00Kvyi+yeJZicBk2yc2sqCFSex+gzWLAp++HpStfRvfSLPw28AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:15:11.951446Z"},"content_sha256":"65eba8c51157c6ae7fd9e6a5bf9035fc3c4129e3060b2e02487de3075d2751c6","schema_version":"1.0","event_id":"sha256:65eba8c51157c6ae7fd9e6a5bf9035fc3c4129e3060b2e02487de3075d2751c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:SYBOYR67IKQMY3SVNZPMFALI2Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Architectural Impact on Performance of In-memory Data Analytics: Apache Spark Case Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.PF"],"primary_cat":"cs.DC","authors_text":"Ahsan Javed Awan, Eduard Ayguade, Mats Brorsson, Vladimir Vlassov","submitted_at":"2016-04-28T16:00:38Z","abstract_excerpt":"While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream data processing. However, recent studies on micro-architectural characterization of in-memory data analytics are limited to only batch processing workloads. We compare micro-architectural performance of batch processing and stream processing workloads in Apache Spark using hardware performance counters on a dual socket server. In our evaluation experiments, we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.08484","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:16:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K3yUTeQFMDW7sXzq5cql3r5AHPXDrtA6NyYYnFYJj6q88eKaw7w5uFe4STdsv7bwVeRgI3B02ni+KfBkEQJbAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:15:11.952217Z"},"content_sha256":"f1b86ee5236118ca890fd8cf61149af1f5547180edfe6efe6fffe92107427512","schema_version":"1.0","event_id":"sha256:f1b86ee5236118ca890fd8cf61149af1f5547180edfe6efe6fffe92107427512"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SYBOYR67IKQMY3SVNZPMFALI2Z/bundle.json","state_url":"https://pith.science/pith/SYBOYR67IKQMY3SVNZPMFALI2Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SYBOYR67IKQMY3SVNZPMFALI2Z/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T17:15:11Z","links":{"resolver":"https://pith.science/pith/SYBOYR67IKQMY3SVNZPMFALI2Z","bundle":"https://pith.science/pith/SYBOYR67IKQMY3SVNZPMFALI2Z/bundle.json","state":"https://pith.science/pith/SYBOYR67IKQMY3SVNZPMFALI2Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SYBOYR67IKQMY3SVNZPMFALI2Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:SYBOYR67IKQMY3SVNZPMFALI2Z","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7ba392f44051b77c6fdffa2acbe9babe2f3247f338e42bc55e5c42e0bcf72295","cross_cats_sorted":["cs.AR","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-04-28T16:00:38Z","title_canon_sha256":"330f5f43b63a12b6f3df0d190ea4e87074db728be5a97503f96765c59bfed1b9"},"schema_version":"1.0","source":{"id":"1604.08484","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.08484","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"arxiv_version","alias_value":"1604.08484v1","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.08484","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"pith_short_12","alias_value":"SYBOYR67IKQM","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"SYBOYR67IKQMY3SV","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"SYBOYR67","created_at":"2026-05-18T12:30:44Z"}],"graph_snapshots":[{"event_id":"sha256:f1b86ee5236118ca890fd8cf61149af1f5547180edfe6efe6fffe92107427512","target":"graph","created_at":"2026-05-18T01:16:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream data processing. However, recent studies on micro-architectural characterization of in-memory data analytics are limited to only batch processing workloads. We compare micro-architectural performance of batch processing and stream processing workloads in Apache Spark using hardware performance counters on a dual socket server. In our evaluation experiments, we ","authors_text":"Ahsan Javed Awan, Eduard Ayguade, Mats Brorsson, Vladimir Vlassov","cross_cats":["cs.AR","cs.PF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-04-28T16:00:38Z","title":"Architectural Impact on Performance of In-memory Data Analytics: Apache Spark Case Study"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.08484","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:65eba8c51157c6ae7fd9e6a5bf9035fc3c4129e3060b2e02487de3075d2751c6","target":"record","created_at":"2026-05-18T01:16:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7ba392f44051b77c6fdffa2acbe9babe2f3247f338e42bc55e5c42e0bcf72295","cross_cats_sorted":["cs.AR","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-04-28T16:00:38Z","title_canon_sha256":"330f5f43b63a12b6f3df0d190ea4e87074db728be5a97503f96765c59bfed1b9"},"schema_version":"1.0","source":{"id":"1604.08484","kind":"arxiv","version":1}},"canonical_sha256":"9602ec47df42a0cc6e556e5ec28168d6445095fabaf375baa3f407c72459b484","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9602ec47df42a0cc6e556e5ec28168d6445095fabaf375baa3f407c72459b484","first_computed_at":"2026-05-18T01:16:03.845912Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:16:03.845912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dYJiOF21SA8FUp0ctK66SR1ikhXNMfgvXdauUbeteW23j5/oGpUhBLTYh6Zcc6o/94491aKLPjM27ue8DH0zCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:16:03.846784Z","signed_message":"canonical_sha256_bytes"},"source_id":"1604.08484","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65eba8c51157c6ae7fd9e6a5bf9035fc3c4129e3060b2e02487de3075d2751c6","sha256:f1b86ee5236118ca890fd8cf61149af1f5547180edfe6efe6fffe92107427512"],"state_sha256":"b320335132e5761f75fd359e7d5e2db8365cad36532a95801b52f82db330ab5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kJ2Tq0rdCGxqR4P4pv3ig7EOACtKAwn/0dArb8V3NxCAbBCMzc/c/vevM2bSabBRUeHISGe8K397Cc52AwDgCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:15:11.956395Z","bundle_sha256":"50fc68d1a96f4fbba39ecc5f469ec287cb4ff20a57cd64cf87ef1af72e536434"}}