Mining SQL Execution Traces for Data Manipulation Behavior Recovery

Résultats de recherche: RechercheArticle dans les actes d'une conférence/un colloque

Résumé

Modern data-intensive software systems manipulate an increasing amount of heterogeneous data in order to support users in various execution contexts. Maintaining and evolving activities of such systems rely on an accurate documentation of their behavior which is often missing or outdated. Unfortunately, standard program analysis techniques are not always suitable for extracting the behavior of dataintensive systems which rely on more and more dynamic data access mechanisms which mainly consist in run-time interactions with a database. This paper proposes a framework to extract behavioral models from dataintensive program executions. The framework makes use of dynamic analysis techniques to capture and analyze SQL execution traces. It applies clustering techniques to identify data manipulation functions from such traces. Process mining techniques are then used to synthesize behavioral models.
langueAnglais
titreProceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014)
Sous-titreCAiSE forum track
EditeurCEUR-WS.org
Pages41-48
Nombre de pages8
Volume1164
étatPublié - 2014

Empreinte digitale

Recovery
Dynamic analysis

mots-clés

    Citer ceci

    Mori, M., Noughi, N., & Cleve, A. (2014). Mining SQL Execution Traces for Data Manipulation Behavior Recovery. Dans Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014): CAiSE forum track (Vol 1164, p. 41-48). CEUR-WS.org.
    Mori, Marco ; Noughi, Nesrine ; Cleve, Anthony. / Mining SQL Execution Traces for Data Manipulation Behavior Recovery. Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014): CAiSE forum track. Vol 1164 CEUR-WS.org, 2014. p. 41-48
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    title = "Mining SQL Execution Traces for Data Manipulation Behavior Recovery",
    abstract = "Modern data-intensive software systems manipulate an increasing amount of heterogeneous data in order to support users in various execution contexts. Maintaining and evolving activities of such systems rely on an accurate documentation of their behavior which is often missing or outdated. Unfortunately, standard program analysis techniques are not always suitable for extracting the behavior of dataintensive systems which rely on more and more dynamic data access mechanisms which mainly consist in run-time interactions with a database. This paper proposes a framework to extract behavioral models from dataintensive program executions. The framework makes use of dynamic analysis techniques to capture and analyze SQL execution traces. It applies clustering techniques to identify data manipulation functions from such traces. Process mining techniques are then used to synthesize behavioral models.",
    keywords = "Data-manipulation behavior recovery, Data-manipulation functions, Data-oriented process mining",
    author = "Marco Mori and Nesrine Noughi and Anthony Cleve",
    year = "2014",
    volume = "1164",
    pages = "41--48",
    booktitle = "Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014)",
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    Mori, M, Noughi, N & Cleve, A 2014, Mining SQL Execution Traces for Data Manipulation Behavior Recovery. Dans Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014): CAiSE forum track. VOL. 1164, CEUR-WS.org, p. 41-48.

    Mining SQL Execution Traces for Data Manipulation Behavior Recovery. / Mori, Marco; Noughi, Nesrine; Cleve, Anthony.

    Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014): CAiSE forum track. Vol 1164 CEUR-WS.org, 2014. p. 41-48.

    Résultats de recherche: RechercheArticle dans les actes d'une conférence/un colloque

    TY - CHAP

    T1 - Mining SQL Execution Traces for Data Manipulation Behavior Recovery

    AU - Mori,Marco

    AU - Noughi,Nesrine

    AU - Cleve,Anthony

    PY - 2014

    Y1 - 2014

    N2 - Modern data-intensive software systems manipulate an increasing amount of heterogeneous data in order to support users in various execution contexts. Maintaining and evolving activities of such systems rely on an accurate documentation of their behavior which is often missing or outdated. Unfortunately, standard program analysis techniques are not always suitable for extracting the behavior of dataintensive systems which rely on more and more dynamic data access mechanisms which mainly consist in run-time interactions with a database. This paper proposes a framework to extract behavioral models from dataintensive program executions. The framework makes use of dynamic analysis techniques to capture and analyze SQL execution traces. It applies clustering techniques to identify data manipulation functions from such traces. Process mining techniques are then used to synthesize behavioral models.

    AB - Modern data-intensive software systems manipulate an increasing amount of heterogeneous data in order to support users in various execution contexts. Maintaining and evolving activities of such systems rely on an accurate documentation of their behavior which is often missing or outdated. Unfortunately, standard program analysis techniques are not always suitable for extracting the behavior of dataintensive systems which rely on more and more dynamic data access mechanisms which mainly consist in run-time interactions with a database. This paper proposes a framework to extract behavioral models from dataintensive program executions. The framework makes use of dynamic analysis techniques to capture and analyze SQL execution traces. It applies clustering techniques to identify data manipulation functions from such traces. Process mining techniques are then used to synthesize behavioral models.

    KW - Data-manipulation behavior recovery

    KW - Data-manipulation functions

    KW - Data-oriented process mining

    M3 - Conference contribution

    VL - 1164

    SP - 41

    EP - 48

    BT - Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014)

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    ER -

    Mori M, Noughi N, Cleve A. Mining SQL Execution Traces for Data Manipulation Behavior Recovery. Dans Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014): CAiSE forum track. Vol 1164. CEUR-WS.org. 2014. p. 41-48.