{ "schema_version": "0.6.0", "paper_id": "state-history-tree-an-incremental-disk-based-data-structure-for-very-large-interval-data", "page_url": "https://naser.github.io/research-publications/papers/state-history-tree-an-incremental-disk-based-data-structure-for-very-large-interval-data/", "title": "State History Tree: An Incremental Disk-Based Data Structure for Very Large Interval Data", "title_variants": [], "authors": [ "Alexandre Montplaisir-Goncalves", "Naser Ezzati-Jivan", "Florian Wininger", "Michel R. Dagenais" ], "author_details": [ { "name": "Alexandre Montplaisir-Goncalves", "orcid": null, "profile_url": null }, { "name": "Naser Ezzati-Jivan", "orcid": "https://orcid.org/0000-0003-1435-6297", "profile_url": "https://naser.github.io/" }, { "name": "Florian Wininger", "orcid": null, "profile_url": "https://dblp.org/pid/134/9574.html" }, { "name": "Michel R. Dagenais", "orcid": null, "profile_url": "https://dblp.org/pid/60/309.html" } ], "publication": { "year": 2013, "venue": "IEEE International Conference on Social Computing", "type": "conference paper", "publication_date": "2013-09", "online_date": null, "print_date": "2013-09", "volume": null, "issue": null, "pages": "716-724", "article_number": null, "publisher": "IEEE", "issn": [], "isbn": [], "crossref_type": "proceedings-article" }, "publication_type": "conference paper", "status": "published_with_public_full_text", "canonical_source_url": "https://doi.org/10.1109/SocialCom.2013.107", "source_record_id": "state-history-tree-an-incremental-disk-based-data-structure-for-very-large-interval-data-35a2d470ae", "identifiers": { "doi": "10.1109/SocialCom.2013.107" }, "abstract": "The State History Tree is a disk-based structure for incrementally arriving, time-ordered interval data derived from LTTng system traces, designed to support very large histories.", "abstract_source": "Public institutional accepted-version PDF reviewed; abstract paraphrased for this catalog.", "abstract_available": true, "scholar_eligibility": { "eligible": false, "basis": "not-eligible", "note": "The page is a discovery record; it does not claim Google Scholar article-host eligibility." }, "description": "The State History Tree is a disk-based structure for incrementally arriving, time-ordered interval data derived from LTTng system traces, designed to support very large histories.", "evidence_level": "full-text-reviewed", "evidence": { "source_basis": "full-text-reviewed", "coverage": "material paper sections", "summary_origin": "AI-assisted catalog editorial summary", "review_status": "catalog-reviewed; paper-author approval pending", "verified_on": "2026-08-09", "sources": [ { "note": "State History Tree PDF: LTTng-derived intervals, Java/TMF implementation, disk blocks, and query design" }, { "note": "State History Tree PDF: Intel Core i7-920/6 GB platform, block-size study, 10-550 GB scale, query times, and comparisons" }, { "note": "State History Tree PDF: chronological-insertion limitation and conclusion boundary" }, { "note": "Local PDF hash verified in pdf-evidence/extraction-manifest.json" } ] }, "summary": { "core_contribution": "The paper presents a disk-oriented State History Tree that stores very large, time-ordered trace intervals without repeated node rebalancing.", "problem": "Trace-derived, time-ordered interval histories can exceed memory capacity; generic R-trees rebalance repeatedly during chronological construction, while B-trees do not directly model intervals (pp. 2-5).", "method": "The State History Tree stores key/value intervals in fixed-size disk blocks organized as a tree. Nodes are closed and committed without rebalancing when chronological end-time insertion fills them; sequence numbers and child time boundaries guide navigation. Full and key-specific ad-hoc queries traverse relevant branches (pp. 5-7).", "findings": "The Java implementation integrates with Eclipse TMF/Linux Tools and manages state intervals extracted from LTTng traces. Tested block sizes are 16 KB, 64 KB, 256 KB, 1 MB, and 4 MB; 16-256 KB perform best on the tested traces, while 1 MB is slightly better above 10 GB. For 10-550 GB traces, the paper reports about 150 ms full-query and 2 ms ad-hoc-query times (pp. 7-9). R-tree queries are faster in memory, but construction is slower and capacity-limited; PostGIS has higher reported storage/query overhead.", "limitations": "The design assumes intervals arrive in ascending end-time order and short intervals dominate. Frequent insertion into the past can increase imbalance and, in the worst case, degenerate toward a linear list (pp. 5-6). The conclusion states no separate author future-work agenda.", "future_work": "Unknown / not explicitly stated by the authors. Do not convert generic modern-storage or modern-tracing ideas into author-attributed future work." }, "tags": [ "kernel-tracing", "trace-analysis", "performance-engineering", "resource-analysis" ], "keywords": [ "State History Tree", "disk-based interval data", "streaming interval data", "time-ordered intervals", "LTTng", "Eclipse Tracing and Monitoring Framework", "Linux Tools Project", "Java", "R-tree", "PostgreSQL/PostGIS", "full query", "ad-hoc query" ], "versions": [ { "id": "published-version", "label": "Published version", "relation": "version-of-record", "title": "State History Tree: An Incremental Disk-Based Data Structure for Very Large Interval Data", "url": "https://doi.org/10.1109/SocialCom.2013.107", "pdf_url": null, "status": "published", "canonical_for_citation": true }, { "id": "public-accepted-full-text", "label": "Public accepted full text", "relation": "source-record", "title": "State History Tree: An Incremental Disk-Based Data Structure for Very Large Interval Data", "url": "https://publications.polymtl.ca/2983/1/2013_Montplaisir-Gon%C3%A7alves_State_history_tree_incremental_disk-based.pdf", "pdf_url": "https://publications.polymtl.ca/2983/1/2013_Montplaisir-Gon%C3%A7alves_State_history_tree_incremental_disk-based.pdf", "status": "public_full_text", "canonical_for_citation": false } ], "access": { "status": "published_with_public_full_text", "note": "The DOI is the canonical citation target; the public full-text link is external and the PDF is not redistributed here.", "license": null }, "resources": { "code": null, "data": null, "slides": null, "demo": null }, "citation_guidance": { "when_to_cite": "Cite this paper when your work uses or compares incremental disk-based interval storage that avoids rebalancing under time-ordered insertion.", "points": [ "Incremental disk-based interval storage that avoids rebalancing under time-ordered insertion.", "The State History Tree's full versus key-specific ad-hoc query distinction.", "LTTng-derived system-state interval storage integrated into Eclipse TMF/Linux Tools.", "The block-size and 10-550 GB scalability results, including the reported 150 ms full and 2 ms ad-hoc query values." ], "canonical_version_id": "published-version" }, "provenance": { "metadata_verified_on": "2026-08-09", "metadata_source": [ "State History Tree PDF: LTTng-derived intervals, Java/TMF implementation, disk blocks, and query design", "State History Tree PDF: Intel Core i7-920/6 GB platform, block-size study, 10-550 GB scale, query times, and comparisons", "State History Tree PDF: chronological-insertion limitation and conclusion boundary", "Local PDF hash verified in pdf-evidence/extraction-manifest.json" ], "summary_written_by": "AI-assisted", "summary_verified_by": "full-text-grounded catalog review; author approval pending", "linked_preprint_record": null, "author_order_note": null }, "batch": { "phase": 2, "batch_label": "expanded forty-paper release", "status": "included_in_expanded_catalog", "selected_at": "2026-08-09" } }