{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "trace-analysis",
  "label": "Trace Analysis",
  "title": "Execution Trace Analysis Research",
  "description": "Research papers on execution-trace analysis, event modeling, dependency reasoning, and performance diagnosis.",
  "introduction": "This topic focuses on methods that transform execution traces into analyzable representations such as states, graphs, sequences, profiles, or statistical features. The collection includes trace-based diagnosis, event modeling, anomaly detection, dependency reasoning, and analysis pipelines for large or distributed systems.",
  "aliases": [
    "execution trace analysis",
    "system trace analysis",
    "trace-based diagnosis"
  ],
  "search_terms": [
    "execution traces",
    "trace data analysis",
    "trace mining",
    "trace-based performance diagnosis"
  ],
  "related_topics": [
    "kernel-tracing",
    "system-tracing",
    "trace-abstraction",
    "trace-reduction",
    "root-cause-analysis"
  ],
  "canonical_url": "https://threadslab.org/research-publications/topics/trace-analysis.html",
  "paper_count": 30,
  "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": "taaf-trace-abstraction-analysis-framework",
      "title": "TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs",
      "year": 2026,
      "authors": [
        "Alireza Ezaz",
        "Ghazal Khodabandeh",
        "Majid Babaei",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/taaf-trace-abstraction-analysis-framework/",
      "canonical_source_url": "https://doi.org/10.1145/3744916.3787832",
      "core_contribution": "TAAF combines time-indexed knowledge graphs with large language models to turn large execution traces into queryable, actionable explanations.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "knowledge-graphs",
        "llm-assisted-analysis",
        "observability"
      ],
      "keywords": [
        "TAAF",
        "TraceQA-100",
        "time-indexed knowledge graph",
        "natural-language trace queries",
        "multi-hop reasoning",
        "causal reasoning"
      ]
    },
    {
      "paper_id": "tracesynth-kernel-trace-generation",
      "title": "TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models",
      "year": 2026,
      "authors": [
        "Yuvraj Sehgal",
        "Sneh Patel",
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Francois Tetreault"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/tracesynth-kernel-trace-generation/",
      "canonical_source_url": "https://doi.org/10.1145/3803437.3805222",
      "core_contribution": "TraceSynth generates novel structured kernel-trace windows with a Transformer diffusion model and repairs generated events against invariants mined from real LTTng traces.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-analysis",
        "machine-learning"
      ],
      "keywords": [
        "kernel traces",
        "trace generation",
        "diffusion models",
        "constraint-guided generation",
        "LTTng",
        "Parquet",
        "Phoronix Test Suite",
        "DDIM",
        "next-event prediction",
        "synthetic traces"
      ]
    },
    {
      "paper_id": "developing-a-taxonomy-for-advanced-log-parsing-techniques",
      "title": "Developing a Taxonomy for Advanced Log Parsing Techniques",
      "year": 2025,
      "authors": [
        "Issam Sedki",
        "Abdelwahab Hamou-Lhadj",
        "Otmane Ait Mohamed",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/developing-a-taxonomy-for-advanced-log-parsing-techniques/",
      "canonical_source_url": "https://doi.org/10.1109/ICPC66645.2025.00061",
      "core_contribution": "The paper introduces a taxonomy of log-event characteristics that explains why different log parsers fail across systems and parser families.",
      "tags": [
        "observability",
        "trace-analysis",
        "anomaly-detection",
        "performance-analysis"
      ],
      "keywords": [
        "log parsing",
        "log event characteristics",
        "LEC taxonomy",
        "LogHub",
        "Drain",
        "IPLoM",
        "AEL",
        "Spell",
        "LenMa",
        "LogMine",
        "SHISO",
        "ULP",
        "open coding",
        "regex",
        "NER",
        "chi-square",
        "parser errors",
        "token structure"
      ]
    },
    {
      "paper_id": "execution-trace-reconstruction-using-diffusion-based-generative-models",
      "title": "Execution Trace Reconstruction Using Diffusion-Based Generative Models",
      "year": 2025,
      "authors": [
        "Madeline Janecek",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/execution-trace-reconstruction-using-diffusion-based-generative-models/",
      "canonical_source_url": "https://doi.org/10.1109/ICSE55347.2025.00063",
      "core_contribution": "The paper reconstructs missing system-call events in execution traces with diffusion and structured state-space generative models.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-analysis",
        "trace-reduction",
        "performance-analysis"
      ],
      "keywords": [
        "execution trace reconstruction",
        "trace imputation",
        "diffusion models",
        "DiffWave",
        "SSSDS4",
        "SSSDSA",
        "CSDIS4",
        "structured state-space",
        "system-call sequences",
        "LTTng",
        "Phoronix Test Suite",
        "ROUGE-L",
        "perfect rate",
        "missing events",
        "ring-buffer overflow"
      ]
    },
    {
      "paper_id": "decoding-log-parsing-challenges-taxonomy",
      "title": "Decoding Log Parsing Challenges: A Comprehensive Taxonomy for Actionable Solutions",
      "year": 2024,
      "authors": [
        "Issam Sedki",
        "Abdelwahab Hamou-Lhadj",
        "Otmane Ait-Mohamed",
        "Naser Ezzati-Jivan",
        "Mohammed A. Shehab"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/decoding-log-parsing-challenges-taxonomy/",
      "canonical_source_url": "https://doi.org/10.1145/3639478.3643523",
      "core_contribution": "The paper derives a 30-item taxonomy of log event characteristics that induce parsing errors and quantifies the characteristics with the largest impact across eight parsers.",
      "tags": [
        "observability",
        "machine-learning",
        "trace-analysis",
        "benchmark-datasets"
      ],
      "keywords": [
        "log parsing",
        "log event characteristics",
        "LEC taxonomy",
        "LogHub",
        "open coding",
        "Drain",
        "IPLoM",
        "AEL",
        "Spell",
        "LenMa",
        "LogMine",
        "SHISO",
        "ULP",
        "log templates",
        "parsing errors",
        "ICSE 2024"
      ]
    },
    {
      "paper_id": "efficient-unsupervised-latency-culprit-ranking",
      "title": "Efficient Unsupervised Latency Culprit Ranking in Distributed Traces with GNN and Critical Path Analysis",
      "year": 2024,
      "authors": [
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj",
        "James Miller"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-unsupervised-latency-culprit-ranking/",
      "canonical_source_url": "https://doi.org/10.1145/3629527.3651841",
      "core_contribution": "The paper combines an unsupervised GraphSAGE model with critical-path-specific latency profiles to detect anomalous requests and rank likely microservice culprits without labelled training data.",
      "tags": [
        "microservices",
        "graph-neural-networks",
        "latency-analysis",
        "root-cause-analysis",
        "trace-analysis"
      ],
      "keywords": [
        "latency culprit ranking",
        "distributed traces",
        "GraphSAGE",
        "graph neural networks",
        "critical path",
        "FIRM dataset",
        "service invocation graph",
        "unsupervised anomaly detection",
        "Top-k ranking"
      ]
    },
    {
      "paper_id": "towards-a-classification-of-log-parsing-errors",
      "title": "Towards a Classification of Log Parsing Errors",
      "year": 2023,
      "authors": [
        "Issam Sedki",
        "Abdelwahab Hamou-Lhadj",
        "Otmane Ait Mohamed",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/towards-a-classification-of-log-parsing-errors/",
      "canonical_source_url": "https://doi.org/10.1109/ICPC58990.2023.00023",
      "core_contribution": "The paper classifies recurring log-parsing errors and connects them to token and event characteristics that can guide parser design and logging practice.",
      "tags": [
        "observability",
        "trace-analysis",
        "anomaly-detection",
        "performance-analysis"
      ],
      "keywords": [
        "log parsing",
        "parser errors",
        "Drain",
        "AEL",
        "Spell",
        "ULP",
        "LogHub",
        "tokenization",
        "logging practices",
        "dynamic tokens",
        "static tokens",
        "delimiter-aware logging",
        "parser error taxonomy"
      ]
    },
    {
      "paper_id": "uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis",
      "title": "Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis",
      "year": 2023,
      "authors": [
        "Madeline Janecek",
        "Sneh Patel",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis/",
      "canonical_source_url": "https://doi.org/10.1145/3578245.3584689",
      "core_contribution": "The paper uses sampled call graphs and machine learning to distinguish warmup from steady-state executions in Java microbenchmarks.",
      "tags": [
        "performance-analysis",
        "performance-engineering",
        "machine-learning",
        "trace-analysis"
      ],
      "keywords": [
        "Java",
        "JMH",
        "microbenchmarking",
        "steady-state detection",
        "warmup",
        "call-graph analysis",
        "Perf",
        "Graph2Vec",
        "change-point detection",
        "PELT",
        "Random Forest",
        "SVC",
        "flame graphs"
      ]
    },
    {
      "paper_id": "execution-trace-model-verification-multicore",
      "title": "Execution trace-based model verification to analyze multicore and real-time systems",
      "year": 2022,
      "authors": [
        "Raphaël Beamonte",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/execution-trace-model-verification-multicore/",
      "canonical_source_url": "https://doi.org/10.1002/cpe.6974",
      "core_contribution": "The paper combines model-based quantitative constraints with user-space and kernel execution traces to automatically identify likely causes of real-time and multicore constraint violations.",
      "tags": [
        "system-tracing",
        "kernel-tracing",
        "multicore-systems",
        "trace-analysis",
        "root-cause-analysis",
        "lttng"
      ],
      "keywords": [
        "model-based constraints",
        "execution tracing",
        "user-space traces",
        "kernel traces",
        "LTTng",
        "LTTng-UST",
        "Common Trace Format",
        "Trace Compass",
        "reservoir sampling",
        "critical-path analysis",
        "JACK2",
        "cyclictest",
        "priority inversion",
        "CPUTop"
      ]
    },
    {
      "paper_id": "performance-anomaly-detection-sequence-alignment",
      "title": "Performance Anomaly Detection through Sequence Alignment of System-Level Traces",
      "year": 2022,
      "authors": [
        "Madeline Janecek",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/performance-anomaly-detection-sequence-alignment/",
      "canonical_source_url": "https://doi.org/10.1145/3524610.3527898",
      "core_contribution": "The framework combines critical-path extraction, coarse anomaly checks, OPTICS execution clustering, and MUSCLE multiple sequence alignment to localize performance differences in system-level traces.",
      "tags": [
        "system-tracing",
        "anomaly-detection",
        "performance-analysis",
        "trace-analysis"
      ],
      "keywords": [
        "sequence alignment",
        "MUSCLE",
        "OPTICS",
        "critical path",
        "system-level traces",
        "performance anomalies",
        "LTTng",
        "Docker",
        "execution clustering",
        "trace overhead"
      ]
    },
    {
      "paper_id": "performance-evaluation-multithread-execution-paths",
      "title": "Performance Evaluation of Complex Multi-Thread Applications Through Execution Path Analysis",
      "year": 2022,
      "authors": [
        "Majid Rezazadeh",
        "Naser Ezzati-Jivan",
        "Seyed Vahid Azhari",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/performance-evaluation-multithread-execution-paths/",
      "canonical_source_url": "https://doi.org/10.1016/j.peva.2022.102289",
      "core_contribution": "The paper unifies Chromium user-space events with Linux kernel scheduling and resource events to analyze execution paths and localize sporadic latency in a complex multithreaded application.",
      "tags": [
        "performance-analysis",
        "trace-analysis",
        "multicore-systems",
        "performance-engineering"
      ],
      "keywords": [
        "multi-thread applications",
        "execution paths",
        "performance evaluation",
        "thread behavior"
      ]
    },
    {
      "paper_id": "model-based-constraints-execution-tracing",
      "title": "Automated Generation of Model-Based Constraints for Common Multi-core and Real-Time Applications Using Execution Tracing",
      "year": 2021,
      "authors": [
        "Raphael Beamonte",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/model-based-constraints-execution-tracing/",
      "canonical_source_url": "https://doi.org/10.1007/s10766-020-00689-5",
      "core_contribution": "The paper automatically builds an approximate workflow model and adaptive quantitative constraints from user-space and kernel execution traces, reducing manual model construction for real-time and multicore diagnosis.",
      "tags": [
        "system-tracing",
        "multicore-systems",
        "trace-analysis",
        "performance-analysis",
        "lttng"
      ],
      "keywords": [
        "model-based constraints",
        "execution tracing",
        "multi-core systems",
        "real-time applications",
        "constraint generation",
        "LTTng",
        "Trace Compass",
        "cyclictest",
        "JACK2",
        "wakelock"
      ]
    },
    {
      "paper_id": "debugging-performance-degradation-distributed-requests",
      "title": "Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis",
      "year": 2021,
      "authors": [
        "Naser Ezzati-Jivan",
        "Houssem Daoud",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/debugging-performance-degradation-distributed-requests/",
      "canonical_source_url": "https://doi.org/10.1155/2021/8478076",
      "core_contribution": "The paper correlates LTTng traces from user space through kernel, storage, network, and multiple hosts in a disk-backed state model, enabling top-down diagnosis of distributed request latency.",
      "tags": [
        "system-tracing",
        "microservices",
        "performance-analysis",
        "root-cause-analysis",
        "trace-analysis"
      ],
      "keywords": [
        "distributed requests",
        "multilevel trace analysis",
        "LTTng",
        "Apache",
        "PHP",
        "MariaDB",
        "State History Tree",
        "cache hit ratio",
        "disk contention",
        "ApacheBench",
        "Trace Compass"
      ]
    },
    {
      "paper_id": "distributed-architecture-for-an-integrated-development-environment-large-trace-analysis-and-visu",
      "title": "Distributed Architecture for an Integrated Development Environment, Large Trace Analysis, and Visualization",
      "year": 2021,
      "authors": [
        "Yonni Chen Kuang Piao",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/distributed-architecture-for-an-integrated-development-environment-large-trace-analysis-and-visu/",
      "canonical_source_url": "https://doi.org/10.3390/s21165560",
      "core_contribution": "The paper presents a modular client-server architecture for large-trace storage, analysis, and visualization inside an integrated development environment.",
      "tags": [
        "system-tracing",
        "trace-analysis",
        "observability",
        "performance-optimization",
        "multicore-systems"
      ],
      "keywords": [
        "distributed architecture",
        "Trace Compass",
        "TraceScape",
        "LTTng 2.10.3",
        "Trace Analysis Server Protocol",
        "TASP",
        "Protobuf",
        "GZip JSON",
        "large traces",
        "state models",
        "trace visualization",
        "PixiJS",
        "Chart.js",
        "HighCharts"
      ]
    },
    {
      "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": "heterogeneous-debugging-dynamic-tracing",
      "title": "Efficient Large-Scale Heterogeneous Debugging Using Dynamic Tracing",
      "year": 2019,
      "authors": [
        "Didier Nadeau",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/heterogeneous-debugging-dynamic-tracing/",
      "canonical_source_url": "https://doi.org/10.1016/j.sysarc.2019.02.016",
      "core_contribution": "The paper redesigns GDB dynamic tracing around LTTng-UST and scalable trace views so multi-threaded CPU/GPU debugging remains usable on heterogeneous many-core systems.",
      "tags": [
        "system-tracing",
        "trace-analysis",
        "performance-analysis",
        "root-cause-analysis"
      ],
      "keywords": [
        "heterogeneous systems",
        "dynamic tracing",
        "large-scale debugging",
        "debugging efficiency",
        "GDB",
        "LTTng-UST",
        "Trace Compass",
        "ROCm-GDB",
        "GPU debugging",
        "pbzip2"
      ]
    },
    {
      "paper_id": "efficient-trace-analysis-parallelization",
      "title": "Efficient Methods for Trace Analysis Parallelization",
      "year": 2019,
      "authors": [
        "Fabien Reumont-Locke",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-trace-analysis-parallelization/",
      "canonical_source_url": "https://doi.org/10.1007/s10766-019-00631-4",
      "core_contribution": "The paper partitions Common Trace Format streams into balanced workloads, resolves cross-chunk state dependencies, and parallelizes stateful trace analyses with low synchronization cost.",
      "tags": [
        "trace-analysis",
        "performance-engineering",
        "multicore-systems",
        "performance-optimization"
      ],
      "keywords": [
        "trace analysis",
        "parallelization",
        "analysis scalability",
        "multicore processing",
        "CTF",
        "Babeltrace",
        "LTTng",
        "parallel efficiency",
        "State History Tree"
      ]
    },
    {
      "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": "hardware-assisted-software-event-tracing",
      "title": "Hardware-assisted software event tracing",
      "year": 2017,
      "authors": [
        "Adrien Vergé",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/hardware-assisted-software-event-tracing/",
      "canonical_source_url": "https://doi.org/10.1002/cpe.4069",
      "core_contribution": "The paper designs and evaluates hardware-assisted alternatives to LTTng-UST for software-event and execution-path tracing, including STM/ETB, ETM/ETB, and a modified Perf/BTS path.",
      "tags": [
        "system-tracing",
        "kernel-tracing",
        "trace-analysis",
        "performance-engineering"
      ],
      "keywords": [
        "hardware-assisted tracing",
        "software events",
        "event collection",
        "trace overhead",
        "ARM CoreSight",
        "STM",
        "ETM",
        "ETB",
        "Intel BTS",
        "Perf",
        "LTTng-UST"
      ]
    },
    {
      "paper_id": "enhanced-state-history-tree-esht-a-stateful-data-structure-for-analysis-of-highly-parallel-syste",
      "title": "Enhanced State History Tree (eSHT): A Stateful Data Structure for Analysis of Highly Parallel System Traces",
      "year": 2016,
      "authors": [
        "Loic Prieur-Drevon",
        "Raphael Beamonte",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/enhanced-state-history-tree-esht-a-stateful-data-structure-for-analysis-of-highly-parallel-syste/",
      "canonical_source_url": "https://doi.org/10.1109/BigDataCongress.2016.19",
      "core_contribution": "eSHT redesigns the State History Tree to store and query highly parallel trace intervals more compactly and with shallower trees.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "performance-optimization",
        "multicore-systems",
        "resource-analysis"
      ],
      "keywords": [
        "Enhanced State History Tree",
        "eSHT",
        "State History Tree",
        "interval data",
        "external-memory indexing",
        "LTTng 2.7.0",
        "Trace Compass",
        "Linux kernel 4.3.0",
        "parallel traces",
        "node fill",
        "tree depth",
        "query optimization"
      ]
    },
    {
      "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": "cube-data-model-for-multilevel-statistics-computation-of-live-execution-traces",
      "title": "Cube data model for multilevel statistics computation of live execution traces",
      "year": 2015,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/cube-data-model-for-multilevel-statistics-computation-of-live-execution-traces/",
      "canonical_source_url": "https://doi.org/10.1002/cpe.3272",
      "core_contribution": "The paper proposes a multilevel data-cube model for computing statistics over live execution traces across several time scales.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "performance-analysis",
        "resource-analysis",
        "performance-engineering"
      ],
      "keywords": [
        "data cube",
        "stream cube",
        "live trace statistics",
        "LTTng",
        "State History Tree",
        "tilted time frame",
        "multilevel analysis",
        "roll-up",
        "drill-down",
        "range query",
        "sliding window",
        "cuboid materialization"
      ]
    },
    {
      "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": "a-framework-to-compute-statistics-of-system-parameters-from-very-large-trace-files",
      "title": "A framework to compute statistics of system parameters from very large trace files",
      "year": 2013,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/a-framework-to-compute-statistics-of-system-parameters-from-very-large-trace-files/",
      "canonical_source_url": "https://doi.org/10.1145/2433140.2433151",
      "core_contribution": "The framework computes system-parameter statistics for arbitrary intervals and resource hierarchies from very large traces using a disk-resident history structure and controlled granularity.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "performance-analysis",
        "resource-analysis",
        "performance-engineering"
      ],
      "keywords": [
        "trace statistics",
        "LTTng",
        "Linux kernel 2.6.38.6",
        "disk-resident interval tree",
        "history tree",
        "granularity degree",
        "linear interpolation",
        "hierarchical query",
        "large-scale tracing",
        "online analysis",
        "CPU usage",
        "I/O throughput"
      ]
    },
    {
      "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"
      ]
    },
    {
      "paper_id": "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",
      "year": 2013,
      "authors": [
        "Alexandre Montplaisir-Goncalves",
        "Naser Ezzati-Jivan",
        "Florian Wininger",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/state-history-tree-an-incremental-disk-based-data-structure-for-very-large-interval-data/",
      "canonical_source_url": "https://doi.org/10.1109/SocialCom.2013.107",
      "core_contribution": "The paper presents a disk-oriented State History Tree that stores very large, time-ordered trace intervals without repeated node rebalancing.",
      "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"
      ]
    },
    {
      "paper_id": "a-stateful-approach-to-generate-synthetic-events-from-kernel-traces",
      "title": "A Stateful Approach to Generate Synthetic Events from Kernel Traces",
      "year": 2012,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/a-stateful-approach-to-generate-synthetic-events-from-kernel-traces/",
      "canonical_source_url": "https://doi.org/10.1155/2012/140368",
      "core_contribution": "The paper introduces a stateful trace-abstraction method that converts low-level kernel events into semantic and synthetic events using modeled resource state and finite-state patterns.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-abstraction",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "stateful trace abstraction",
        "synthetic events",
        "semantic events",
        "modeled state",
        "finite-state machine",
        "LTTng",
        "Linux kernel 2.6.38.6",
        "XML pattern library",
        "partial trace abstraction",
        "fork bomb",
        "SYN flood",
        "port scanning"
      ]
    },
    {
      "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"
      ]
    }
  ]
}