{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:T7YFLG3UT7YPBE4RRBY5QJY3UH","short_pith_number":"pith:T7YFLG3U","schema_version":"1.0","canonical_sha256":"9ff0559b749ff0f093918871d8271ba1df102f6a92b04ceaefbd021db1a74ce0","source":{"kind":"arxiv","id":"2506.22267","version":2},"attestation_state":"computed","paper":{"title":"From Data Center IoT Telemetry to Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Andrea Bartolini, Andrea Proia, Hiari Pizzini Cavagna, Junaid Ahmed Khan","submitted_at":"2025-06-27T14:36:39Z","abstract_excerpt":"Industry 5.0 demands IoT systems that support seamless human-machine collaboration, yet current IoT data analysis requires deep domain, deployment, and query expertise. We show that combining Large Language Models (LLMs) with Knowledge Graphs (KGs) enables natural language access to heterogeneous IoT data. Focusing on data center IoT telemetry, we introduce a rule-based Virtual Knowledge Graph (VKG) construction process and an on-premise LLM inference service to create an end-to-end Data Analytics (DA) chatbot. Our system dynamically generates VKGs per query and translates user input into SPAR"},"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":"2506.22267","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-06-27T14:36:39Z","cross_cats_sorted":[],"title_canon_sha256":"c3c0860187b4c965ab759e71db4c93edd1e51fc585ebc837ff3d8d1fa156db29","abstract_canon_sha256":"4857c06ac4c0ccb2d04d9df9baa3a428b9cbdc87c410af7432059cccb9913d61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:48.258786Z","signature_b64":"M/Llcp9gFCvHw4sFlqcKACN0yM8GqfeMOhHZBAqClHR6d7eU60Os5vA3/FJclbUUz0YFd2r1/xXJFOTnRsorCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ff0559b749ff0f093918871d8271ba1df102f6a92b04ceaefbd021db1a74ce0","last_reissued_at":"2026-07-05T11:53:48.258253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:48.258253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Data Center IoT Telemetry to Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Andrea Bartolini, Andrea Proia, Hiari Pizzini Cavagna, Junaid Ahmed Khan","submitted_at":"2025-06-27T14:36:39Z","abstract_excerpt":"Industry 5.0 demands IoT systems that support seamless human-machine collaboration, yet current IoT data analysis requires deep domain, deployment, and query expertise. We show that combining Large Language Models (LLMs) with Knowledge Graphs (KGs) enables natural language access to heterogeneous IoT data. Focusing on data center IoT telemetry, we introduce a rule-based Virtual Knowledge Graph (VKG) construction process and an on-premise LLM inference service to create an end-to-end Data Analytics (DA) chatbot. Our system dynamically generates VKGs per query and translates user input into SPAR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.22267","kind":"arxiv","version":2},"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/2506.22267/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":"2506.22267","created_at":"2026-07-05T11:53:48.258321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.22267v2","created_at":"2026-07-05T11:53:48.258321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.22267","created_at":"2026-07-05T11:53:48.258321+00:00"},{"alias_kind":"pith_short_12","alias_value":"T7YFLG3UT7YP","created_at":"2026-07-05T11:53:48.258321+00:00"},{"alias_kind":"pith_short_16","alias_value":"T7YFLG3UT7YPBE4R","created_at":"2026-07-05T11:53:48.258321+00:00"},{"alias_kind":"pith_short_8","alias_value":"T7YFLG3U","created_at":"2026-07-05T11:53:48.258321+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.06107","citing_title":"A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH","json":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH.json","graph_json":"https://pith.science/api/pith-number/T7YFLG3UT7YPBE4RRBY5QJY3UH/graph.json","events_json":"https://pith.science/api/pith-number/T7YFLG3UT7YPBE4RRBY5QJY3UH/events.json","paper":"https://pith.science/paper/T7YFLG3U"},"agent_actions":{"view_html":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH","download_json":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH.json","view_paper":"https://pith.science/paper/T7YFLG3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.22267&json=true","fetch_graph":"https://pith.science/api/pith-number/T7YFLG3UT7YPBE4RRBY5QJY3UH/graph.json","fetch_events":"https://pith.science/api/pith-number/T7YFLG3UT7YPBE4RRBY5QJY3UH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH/action/storage_attestation","attest_author":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH/action/author_attestation","sign_citation":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH/action/citation_signature","submit_replication":"https://pith.science/pith/T7YFLG3UT7YPBE4RRBY5QJY3UH/action/replication_record"}},"created_at":"2026-07-05T11:53:48.258321+00:00","updated_at":"2026-07-05T11:53:48.258321+00:00"}