{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "trace-filtering",
  "label": "Trace Filtering",
  "title": "Trace Filtering Research",
  "description": "Research papers on filtering execution traces, selecting events, and retaining evidence for performance analysis.",
  "introduction": "This topic groups methods for selecting useful events, paths, or entities from execution traces. The goal is to reduce irrelevant data while preserving evidence for diagnosis, visualization, modeling, or anomaly analysis. Each paper page distinguishes the filtering rule, retained evidence, workload, and measured reduction or overhead.",
  "aliases": [
    "execution trace filtering",
    "trace event selection",
    "selective tracing"
  ],
  "search_terms": [
    "trace filtering",
    "event selection",
    "selective tracing",
    "trace volume reduction"
  ],
  "related_topics": [
    "trace-reduction",
    "trace-analysis",
    "kernel-tracing",
    "performance-analysis",
    "observability"
  ],
  "canonical_url": "https://threadslab.org/research-publications/topics/trace-filtering.html",
  "paper_count": 3,
  "papers": [
    {
      "paper_id": "enhanced-trace-abstraction-social-network-analysis",
      "title": "Enhanced Execution Trace Abstraction Approach Using Social Network Analysis Methods",
      "year": 2020,
      "authors": [
        "Ji Wang",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/enhanced-trace-abstraction-social-network-analysis/",
      "canonical_source_url": "https://fb-swt.gi.de/fileadmin/FB/SWT/Softwaretechnik-Trends/Verzeichnis/Band_40_Heft_3/SSP2020_Wang.pdf",
      "core_contribution": "The paper adapts community detection and PageRank from social-network analysis to reduce and prioritize system execution traces.",
      "tags": [
        "kernel-tracing",
        "trace-abstraction",
        "social-network-analysis",
        "trace-filtering",
        "lttng"
      ],
      "keywords": [
        "LTTng",
        "Trace Compass",
        "Louvain community detection",
        "PageRank",
        "thread interaction graph",
        "trace filtering",
        "virtual-machine clustering"
      ]
    },
    {
      "paper_id": "a-flexible-data-driven-approach-for-execution-trace-filtering",
      "title": "A Flexible Data-Driven Approach for Execution Trace Filtering",
      "year": 2015,
      "authors": [
        "Kadjo Gwandy Kouame",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/a-flexible-data-driven-approach-for-execution-trace-filtering/",
      "canonical_source_url": "https://doi.org/10.1109/BigDataCongress.2015.112",
      "core_contribution": "The paper provides a declarative XML/state-machine approach for flexible filtering and synthetic-event generation over large kernel traces.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-filtering",
        "trace-abstraction",
        "trace-analysis"
      ],
      "keywords": [
        "execution trace filtering",
        "LTTng 2.4.0",
        "Trace Compass",
        "finite-state machine",
        "declarative analysis",
        "synthetic events",
        "SYN flood",
        "hping3",
        "state system",
        "XML modeling",
        "kernel tracing"
      ]
    },
    {
      "paper_id": "efficient-analysis-multi-core-system-tracing-data",
      "title": "An Efficient Analysis Approach for Multi-Core System Tracing Data",
      "year": 2012,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-analysis-multi-core-system-tracing-data/",
      "canonical_source_url": "https://doi.org/10.2316/P.2012.790-053",
      "core_contribution": "The paper introduces a stateful trace-abstraction architecture that shares modeled system state and intermediate computation across concurrent patterns to reduce the cost of analyzing large Linux kernel traces.",
      "tags": [
        "kernel-tracing",
        "lttng",
        "trace-analysis",
        "trace-abstraction",
        "trace-filtering",
        "performance-analysis",
        "multicore-systems"
      ],
      "keywords": [
        "multi-core tracing",
        "LTTng",
        "Linux kernel 2.6.38.6",
        "stateful trace analysis",
        "trace abstraction",
        "EventMapper",
        "modeled state",
        "semantic events",
        "synthetic event generation",
        "State Machine Language",
        "pattern library",
        "partial trace abstraction",
        "SYN flood",
        "fork bomb",
        "kernel trace",
        "Java trace analyzer",
        "computation sharing",
        "storage efficiency"
      ]
    }
  ]
}