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  "page_url": "https://naser.github.io/research-publications/papers/cube-data-model-for-multilevel-statistics-computation-of-live-execution-traces/",
  "title": "Cube Data Model for Multilevel Statistics Computation of Live Execution Traces",
  "title_variants": [],
  "authors": [
    "Naser Ezzati-Jivan",
    "Michel R. Dagenais"
  ],
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      "name": "Naser Ezzati-Jivan",
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  "publication": {
    "year": 2015,
    "venue": "Concurrency and Computation: Practice and Experience",
    "type": "journal article",
    "publication_date": "2014-04-16",
    "online_date": "2014-04-16",
    "print_date": "2015-04-10",
    "volume": "27",
    "issue": "5",
    "pages": "1069-1091",
    "article_number": null,
    "publisher": "Wiley",
    "issn": [
      "1532-0626",
      "1532-0634"
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    "doi": "10.1002/cpe.3272"
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  "description": "The paper proposes a multilevel data-cube model for computing statistics over live execution traces across several time scales.",
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    "method": "The architecture combines a trace reader, circular-buffer processing, dimension/metric trees, interval-form history storage, separate cubes per tilted time frame, and a query engine. It supports sum/count/average, point/stabbing, range, sliding-window, top-k, drill-down, roll-up, slice, and dice queries. Minimal, partial, and full cubing trade memory for response time (pp. 4-12).",
    "findings": "Java/Eclipse experiments use LTTng on Linux kernel 2.6.38.6, a Core i7 2.80 GHz/6 GB machine, generated activity from recursive grep -r, wget -r -l, and ls -R, 1,000 measures, and three time levels: last 5 minutes, 24 hours, and 12 days. A one-day trace is used for the three-level memory study; maximum memory is approximately 35 MB. Partial cubing with selected non-leaf measures gives the best reported response-time/memory trade-off, and range-query time is largely independent of interval length in the tested cases (pp. 12-16).",
    "limitations": "Materialization choices control memory/latency; the partial-cubing selection is static in the evaluation; memory grows with metric count/frequency; and the workload is a selected generated LTTng trace rather than a broad production benchmark. Very busy streams may require buffering, delayed processing, or dropping events (pp. 5, 12-16).",
    "future_work": "Dynamically choose non-leaf cuboids or switch between minimal/partial materialization using user feedback/query history, and extend the system to problem detection and data-mining analyses (p. 16, conclusion)."
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  "tags": [
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      "title": "Cube Data Model for Multilevel Statistics Computation of Live Execution Traces",
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