{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "trace-visualization",
  "label": "Trace Visualization",
  "title": "Trace Visualization Research",
  "description": "Research papers on visualizing execution traces, system states, dependencies, and multiscale trace data.",
  "introduction": "This topic covers visual interfaces and visual encodings for navigating execution traces and understanding system behavior. The collection includes multiscale timelines, state views, dependency and interaction graphs, critical paths, labels, and visual support for performance diagnosis.",
  "aliases": [
    "execution trace visualization",
    "large trace visualization",
    "trace navigation"
  ],
  "search_terms": [
    "trace visualization",
    "trace navigation",
    "multiscale timelines",
    "execution-state visualization"
  ],
  "related_topics": [
    "trace-analysis",
    "trace-abstraction",
    "trace-reduction",
    "performance-analysis",
    "system-tracing"
  ],
  "canonical_url": "https://threadslab.org/research-publications/topics/trace-visualization.html",
  "paper_count": 7,
  "papers": [
    {
      "paper_id": "dtracomp-distributed-trace-comparison",
      "title": "DTraComp: Comparing distributed execution traces for understanding intermittent latency sources",
      "year": 2026,
      "authors": [
        "Maryam Ekhlasi",
        "Fatemeh Faraji Daneshgar",
        "Michel Dagenais",
        "Maxime Lamothe",
        "Naser Ezzati-Jivan",
        "Matthew Khouzam"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/dtracomp-distributed-trace-comparison/",
      "canonical_source_url": "https://doi.org/10.1016/j.jss.2026.112990",
      "core_contribution": "DTraComp is an open-source Eclipse Trace Compass framework that compares groups of distributed requests and attributes span time to user-space, kernel, thread-state, and system-call evidence.",
      "tags": [
        "system-tracing",
        "kernel-tracing",
        "trace-analysis",
        "trace-visualization",
        "latency-analysis",
        "root-cause-analysis",
        "microservices",
        "lttng"
      ],
      "keywords": [
        "DTraComp",
        "distributed trace comparison",
        "OpenTracing",
        "LTTng",
        "LTTng-UST",
        "Eclipse Trace Compass",
        "differential flame graph",
        "span-state attribution",
        "system-call attribution",
        "Waited CPU",
        "Waited Blocked",
        "HotROD",
        "TiDB",
        "Apache Cassandra",
        "Eclipse Theia",
        "JFreeChart",
        "Jaeger",
        "microservice performance"
      ]
    },
    {
      "paper_id": "integrated-modeling-tool-state-machine-trace",
      "title": "Integrated Modeling Tool for Indexing and Analyzing State Machine Trace",
      "year": 2021,
      "authors": [
        "Simon Delisle",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/integrated-modeling-tool-state-machine-trace/",
      "canonical_source_url": "https://doi.org/10.1109/ISNCC52172.2021.9615814",
      "core_contribution": "The paper provides a graphical state-machine modeling tool that generates trace analyses and integrates them into Trace Compass, reducing the need to hand-code state providers or XML analyses.",
      "tags": [
        "trace-analysis",
        "trace-visualization",
        "trace-abstraction",
        "performance-analysis"
      ],
      "keywords": [
        "state-machine traces",
        "trace indexing",
        "special-purpose database",
        "Gantt chart",
        "XY view",
        "trace filtering"
      ]
    },
    {
      "paper_id": "declarative-framework-stateful-trace-analysis",
      "title": "A declarative framework for stateful analysis of execution traces",
      "year": 2017,
      "authors": [
        "Florian Wininger",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/declarative-framework-stateful-trace-analysis/",
      "canonical_source_url": "https://doi.org/10.1007/s11219-016-9311-0",
      "core_contribution": "The framework lets analysts define stateful trace analyses declaratively, using a generic state model and XML specifications that can drive storage, filtering, and visualization across trace formats.",
      "tags": [
        "trace-analysis",
        "trace-abstraction",
        "performance-analysis",
        "system-tracing",
        "trace-visualization"
      ],
      "keywords": [
        "declarative trace analysis",
        "stateful analysis",
        "execution traces",
        "LTTng",
        "ETW",
        "State History Tree",
        "XML",
        "trace filtering",
        "Gantt chart",
        "critical-path analysis"
      ]
    },
    {
      "paper_id": "multi-scale-navigation-of-large-trace-data-a-survey",
      "title": "Multi-scale navigation of large trace data: A survey",
      "year": 2017,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multi-scale-navigation-of-large-trace-data-a-survey/",
      "canonical_source_url": "https://doi.org/10.1002/cpe.4068",
      "core_contribution": "The survey provides a taxonomy and requirements-oriented comparison of techniques for collecting, abstracting, analyzing, visualizing, and navigating large execution traces.",
      "tags": [
        "trace-visualization",
        "trace-abstraction",
        "kernel-tracing",
        "performance-analysis",
        "system-tracing"
      ],
      "keywords": [
        "trace navigation",
        "multi-scale analysis",
        "trace visualization",
        "content abstraction",
        "metric abstraction",
        "visual abstraction",
        "resource abstraction",
        "semantic zoom",
        "focus-plus-context",
        "Trace Compass",
        "Vampir",
        "Jumpshot",
        "SLOG",
        "R-tree",
        "quadtree",
        "State History Tree"
      ]
    },
    {
      "paper_id": "multiscale-navigation-large-trace-data",
      "title": "Multiscale Navigation in Large Trace Data",
      "year": 2014,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multiscale-navigation-large-trace-data/",
      "canonical_source_url": "https://doi.org/10.1109/CCECE.2014.6901019",
      "core_contribution": "The paper presents multiscale trace navigation that links raw kernel events to system-call, synthetic, and fault/alert abstractions in a zoomable timeline.",
      "tags": [
        "trace-visualization",
        "trace-abstraction",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "large trace data",
        "multiscale navigation",
        "zoomable timeline",
        "semantic zoom",
        "physical zoom",
        "trace visualization"
      ]
    },
    {
      "paper_id": "efficient-model-query-visualize-system-states-traces",
      "title": "Efficient Model to Query and Visualize the System States Extracted from Trace Data",
      "year": 2013,
      "authors": [
        "Alexandre Montplaisir",
        "Naser Ezzati-Jivan",
        "Florian Wininger",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-model-query-visualize-system-states-traces/",
      "canonical_source_url": "https://doi.org/10.1007/978-3-642-40787-1_13",
      "core_contribution": "The paper introduces a disk-backed state-history model that incrementally converts trace events into queryable state intervals, enabling scalable timestamp/state queries and visualization without replaying the entire trace.",
      "tags": [
        "trace-abstraction",
        "trace-visualization",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "system states",
        "trace queries",
        "trace visualization",
        "interval data",
        "tree-based storage",
        "online analysis",
        "offline analysis"
      ]
    },
    {
      "paper_id": "multilevel-label-placement-execution-trace-events",
      "title": "Multilevel Label Placement for Execution Trace Events",
      "year": 2013,
      "authors": [
        "Naser Ezzati-Jivan",
        "Alireza Shameli-Sendi",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multilevel-label-placement-execution-trace-events/",
      "canonical_source_url": "https://doi.org/10.1109/CCECE.2013.6567826",
      "core_contribution": "The paper develops a multilevel trace-label placement algorithm that preserves readable, semantically meaningful labels while users zoom and pan across dense execution-trace views.",
      "tags": [
        "trace-visualization",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "execution trace events",
        "label placement",
        "trace visualization",
        "overlap avoidance",
        "event labels",
        "CCECE 2013"
      ]
    }
  ]
}