An approach for mining services in database oriented applications

Grosso, C. Del; Penta, Massimiliano Di; Guzmán, Ignacio García Rodriguez de

doi:10.1109/csmr.2007.11

Cited by 15 publications

(7 citation statements)

References 19 publications

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“…This method is used in the context of runtime system monitoring. Del Grosso et al [8] propose an approach to identify distinct features within data-intensive systems, with the ultimate goal of exporting these features as services. The method consists of grouping together all the SQL queries collected from the interactions of programs with their database.…”

Section: Related Workmentioning

confidence: 99%

Conceptual interpretation of SQL execution traces for program comprehension

Noughi

Cleve

2015

2015 IEEE 6th International Workshop on Program Comprehension Through Dynamic Analysis (PCODA)

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Modern data-intensive software systems manipulate an increasing amount of heterogeneous data usually stored in a database. Maintaining such systems became a crucial and complex task, which is especially true due to the lack of sufficient documentation. In this context, program comprehension became a primary and an important step in this task. Unfortunately, the highly dynamic nature of interactions between a system and its database makes it hard to analyze these interactions with static analysis techniques. To this end, we propose a novel approach that combines dynamic analysis techniques and visualization to ease understanding data-intensive systems, by focusing on their database manipulation behavior. The approach consists of defining the conceptual interpretation of SQL execution traces in terms of a domain-specific, platform-independent model.

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Section: Related Workmentioning

confidence: 99%

Conceptual interpretation of SQL execution traces for program comprehension

Noughi

Cleve

2015

2015 IEEE 6th International Workshop on Program Comprehension Through Dynamic Analysis (PCODA)

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“…Debusmann et al [7] present a dynamic analysis method in the context of system performance monitoring, with the aim of measuring the response time of queries sent to a remote database server. Del Grosso et al [8] propose an approach to identify the data-intensive functionalities of an application with the aim of identifying services. Yang et al [13] support the recovery of a feature model by means of the aspect-based tracing technique introduced in [2].…”

Section: Related Workmentioning

confidence: 99%

Understanding the database manipulation behavior of programs

Noughi

Mori

Meurice

et al. 2014

Proceedings of the 22nd International Conference on Program Comprehension

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Due to the lack of (up-do-date) documentation, software maintenance and evolution processes often necessitate the recovery of a sufficient understanding of the software system, before the latter can be adapted to new or changing requirements. To address this problem, several program comprehension techniques have been proposed to support this preliminary phase of software maintenance and evolution. Nevertheless, those techniques generally fail in gaining a complete and accurate understanding in the case of modern data-intensive systems, which are characterized by complex, dynamic and continuous interactions between the application programs and their database. In particular, understanding the database manipulation behavior of a given program involves different levels of comprehension ranging from identifying to relating and interpreting the successive database access operations. In this paper, we present our early research achievements in the development of a tool-supported framework aiming to extract and understand the database manipulation behavior of data-intensive programs.

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“…We plan to model data-related artifacts with DB-MAIN tool 2 while we plan to adopt MySQL 3 DBMS for data instances. As far as the migration problem is concerned, we plan to adopt query parsers (e.g., JSqlParser 4 ) for extracting semantic information of data-related events to be applied to a Formal Concept Analysis technique [13], (e.g., colibri-java 5 ) with the aim of clustering queries which implement the same high-level data-manipulation function [15]. Once high-level data accesses have been identified, we plan to adopt the de-facto standard for process mining, i.e., ProM tool 6 to extract data-oriented processes of legacy data-intensive systems.…”

Section: Techniques For Implementationmentioning

confidence: 99%

Towards Highly Adaptive Data-Intensive Systems: A Research Agenda

Mori

Cleve

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Data-intensive software systems work in different contexts for different users with the aim of supporting heterogeneous tasks in heterogeneous environments. Most of the operations carried out by dataintensive systems are interactions with data. Managing these complex systems means focusing the attention to the huge amount of data that have to be managed despite limited capacity devices where data are accessed. This rises the need of introducing adaptivity in accessing data as the key element for data-intensive systems to become reality. Currently, these systems are not supported during their lifecycle by a complete process starting from design to implementation and execution while taking into account the variability of accessing data. In this paper, we introduce the notion of data-intensive self-adaptive (DISA) systems as dataintensive systems able to perform context-dependent data accesses. We define a classification framework for adaptation and we identify the key challenges for managing the complete lifecycle of DISA systems. For each problem we envisage a possible solution and we present the technological support for an integrated implementation.

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An approach for mining services in database oriented applications

Abstract: Abstract

Cited by 15 publications

References 19 publications

Conceptual interpretation of SQL execution traces for program comprehension

Conceptual interpretation of SQL execution traces for program comprehension

Understanding the database manipulation behavior of programs

Towards Highly Adaptive Data-Intensive Systems: A Research Agenda

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