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and evolving activities of such systems rely on an accurate documentation of their behavior which is often missing or outdated.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.
Mining SQL Execution Traces for Data Manipulation Behavior Recovery / M. Mori;N. Noughi;A. Cleve. - STAMPA. - (2014), pp. 41-48. (Intervento presentato al convegno Joint 26th International Conference on Advanced Information Systems Engineering Forum and Doctoral Consortium, CAiSE-Forum-DC 2014 Thessaloniki 18 June 2014 through 20 June 2014 nel 2014).
Mining SQL Execution Traces for Data Manipulation Behavior Recovery
MORI, MARCO;
2014
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and evolving activities of such systems rely on an accurate documentation of their behavior which is often missing or outdated.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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.