An MDA‐based approach for database re‐engineering

Polo, Macario; García-Rodríguez, Ignacio; Piattini, Mario

doi:10.1002/smr.353

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…The latter seems to suggest the use of "transformations" as means to explicitly state the mappings rather than being hidden in an algorithm. Finally, [36] proposes an MDA approach to data re-engineering.…”

Section: Discussionmentioning

confidence: 99%

Harvesting models from web 2.0 databases

et al. 2011

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International audienceData rather than functionality are the sources of competitive advantage for Web2.0 applications such as wikis, blogs and social networking websites. This valuable information might need to be capitalized by third-party applications or be subject to migration or data analysis. Model-Driven Engineering (MDE) can be used for these purposes. However, MDE ﬁrst requires obtaining models from the wiki/blog/website database (a.k.a. model harvesting). This can be achieved through SQL scripts embedded in a program. However, this approach leads to laborious code that exposes the iterations and table joins that serve to build the model. By contrast, a Domain-Speciﬁc Language (DSL) can hide these "how" concerns, leaving the designer to focus on the "what", i.e. the mapping of database schemas to model classes. This paper introduces Schemol, a DSL tailored for extracting models out of databases which considers Web2.0 specifics. Web2.0 applications are often built on top of general frameworks (a.k.a. engines) that set the database schema (e.g.,MediaWiki, Blojsom). Hence, table names offer little help in automating the extraction process. In addition, Web2.0 data tend to be annotated. User-provided data (e.g., wiki articles, blog entries) might contain semantic markups which provide helpful hints for model extraction. Unfortunately, these data end up being stored as opaque strings. Therefore, there exists a considerable conceptual gap between the source database and the target metamodel. Schemol offers extractive functions and view-like mechanisms to confront these issues. Examples using Blojsom as the blog engine are available for download

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Section: Discussionmentioning

confidence: 99%

Harvesting models from web 2.0 databases

et al. 2011

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“…Thus, as said earlier, the approach may be worth considered as pertaining to IS reengineering [15,16], rather than to data or software reengineering as separate views on it. [27] 2005 Methods/tools Semi-auto · · × · Batini et al [28] 2005 Method/tool Semi-auto × · · × Cleve et al [23] 2006 Method/tool Semi-auto × · × · Batini et al [29] 2006 Method Semi-auto × · · × Polo et al [21] 2007 Method/tool Semi-auto × · · · Henrard et al [6] 2007 Method/tool Semi-auto × · · × Marinescu [30] 2007 Method/tool Semi-auto × · · · Mian et al [18] 2008 Tools Semi-auto × · · · Mens [26] 2008 Methods/tools Semi-auto · · × · Arcelli et al [31] 2010 Method Semi-auto × × × × Canfora et al [25] 2011 Methods/tools Semi-auto · · × · Viscusi et al [5] 2010 Method Manual · · · × Mian et al [20] 2013…”

Section: Software and Data Reverse Engineering: A Unified Approachmentioning

confidence: 99%

“…We now provide a discussion focused on aspects and contributions relevant for the aims and purpose of this paper. Considering data reverse engineering, different strategies, methodologies, and tools have been proposed and discussed in the literature . Furthermore, data reverse engineering aims to address specific problems, such as (i) the weakness of the DBMS technical models that cannot express all the constructs and constraints represented in conceptual schemas, (ii) the weakness of databases designed by designers without database theory and methodology background, (iii) the obsolescence of constructs, and (iv) the lack of design documentation .…”

Section: Related Workmentioning

confidence: 99%

Pattern detection for conceptual schema recovery in data-intensive systems

Zanoni

Perin

Fontana

et al. 2014

J. Softw. Evol. and Proc.

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In this paper, an approach for information systems reverse engineering is proposed and applied. The aim is to support a unified perspective to the reverse engineering process of both data and software. At the state of the art, indeed, many methods, techniques, and tools for software reverse engineering have been proposed to support program comprehension, software maintenance, and software evolution. Other approaches and tools have been proposed for data reverse engineering, with the aim, for example, to provide complete and up-to-date documentation of legacy databases. However, the two engineering communities often worked independently, and very few approaches addressed the reverse engineering of both data and software as information system's constituencies. Hence, a higher integration is needed to support a better co-evolution of databases and programs, in an environment often characterized by high availability of data and volatility of information flows. Accordingly, the approach we propose leverages the detection of object-relational mapping design patterns to build a conceptual schema of the software under analysis. Then, the conceptual schema is mapped to the domain model of the system, to support the design of the evolution of the information system itself. The approach is evaluated on two large-scale open-source enterprise applications.

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“…Moreover, Pérez-Castillo et al (Pérez-Castillo et al, 2009) propose a wrapping technique to extract Web services from relational databases that manage the data access. Finally, Polo et al (Polo et al, 2007) have studied building database-driven applications.…”

Section: Related Workmentioning

confidence: 99%

Database Schema Elicitation to Modernize Relational Databases

Pérez‐Castillo¹,

Guzmán²,

Caivano³

et al. 2012

Proceedings of the 14th International Conference on Enterprise Information Systems

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Legacy enterprise systems mainly consist of two kinds of artefacts: source code and databases. Typically, the maintenance of those artefacts is carried out through re-engineering processes in isolated manners. However, for a more effective maintenance of the whole system both should be analysed and evolved jointly according to ADM (Architecture-Driven Modernization) approach. Thus, the ROI and the lifespan of the legacy system are expected to improve. In this sense, this paper proposes the schema elicitation technique for recovering the relational database schema that is minimally used by the source code. For this purpose, the technique analyses database queries embedded in the legacy source code in order to remove the dead parts of the database schema. Also, this proposal has been validated throughout a real-life case study.

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An MDA‐based approach for database re‐engineering

Cited by 11 publications

References 17 publications

Harvesting models from web 2.0 databases

Harvesting models from web 2.0 databases

Pattern detection for conceptual schema recovery in data-intensive systems

Database Schema Elicitation to Modernize Relational Databases

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