Constraints Checking in UML Class Diagrams: SQL vs OCL

Berrabah, Djamel; Boufarès, Faouzi

doi:10.1007/978-3-540-74469-6_58

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…We identified these criteria on the basis of the features typically used to compare two formal languages such as OCL and B [10] and OCL and SQL [11].…”

Section: Criteria For Comparisonmentioning

confidence: 99%

SBVR vs OCL: A comparative analysis of standards

Bajwa

Bordbar

Lee

2011

2011 IEEE 14th International Multitopic Conference

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Abstract-In software modelling, the designers have to produce UML visual models with software constraints. Similarly, in business modelling, designers have to model business processes using business constraints (business rules). Constraints are the key components in the skeleton of business or software models. A designer has to write constraints to semantically compliment business models or UML models and finally implementing the constraints into business processes or source code. Business constraints/rules can be written using SBVR (Semantics of Business Vocabulary and Rules) while OCL (Object Constraint Language) is the well-known medium for writing software constraints. SBVR and OCL are two significant standards from OMG. Both standards are principally different as SBVR is typically used in business domains and OCL is employed to compliment software models. However, we have identified a few similarities in both standards that are interesting to study. In this paper, we have performed a comparative analysis of both standards as we are looking for a mechanism for automatic transformation of SBVR to OCL. The major emphasis of the study is to highlight principal features of SBVR and OCL such as similarities, differences and key parameters on which these both standards can work together.

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“…We identified these criteria on the basis of the features typically used to compare two formal languages such as OCL and B [10] and OCL and SQL [11].…”

Section: Criteria For Comparisonmentioning

confidence: 99%

SBVR vs OCL: A comparative analysis of standards

Bajwa

Bordbar

Lee

2011

2011 IEEE 14th International Multitopic Conference

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“…7. 13 shows that an association end with rolename opposite in the reflexive binary relationship type between two instances of Property is derived. Therefore, the user may be interested in understanding the semantics of the derivation rule of opposite.…”

Section: Filtering Scenariomentioning

confidence: 97%

“…There are assistants to generate most of the final code of a software system from its UML information model, or even interpret this model and make it executable [76]. It is possible to check the consistency of UML models in order to solve many issues in the software development process [13]. We can translate a UML model into a narrative description that must enable non-experts or business people to understand its semantics [23].…”

Section: Improving the Hl7 V3 Standardmentioning

confidence: 99%

A filtering engine for large conceptual schemas

Villegas Niño

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Nowadays, the need for representation and conceptualization of real world information has dramatically increased. Organizations evolution and diversification require the management and maintenance of large amounts of knowledge from their domains of interest. That growth also has an impact in the size of conceptual schemas of information systems, making them larger. The sheer size of those schemas transforms them into very useful artifacts for the communities and organizations for which they are developed. However, the size of the schemas and their overall structure and organization make it difficult to manually extract knowledge from them, to understand their characteristics, and to change them.There are many information system development activities in which people needs to get a piece of the knowledge contained in the conceptual schema. For example, a conceptual modeler needs to check with a domain expert that the knowledge is correct, a database designer needs to implement that knowledge into a relational database, a software tester needs to write tests checking that the knowledge has been correctly implemented in the system components, or a member of the maintenance team needs to change that knowledge.Dealing with large conceptual schemas is one of the most challenging and long-standing goals in conceptual modeling. The purpose of this thesis is to formally define a new information filtering methodology to help users of very large conceptual schemas to understand the characteristics and knowledge these schemas contain. This thesis analyzes and describes the different phases of an information filtering engine, identifies and studies several properties of relevance for elements of large conceptual schemas, provides a catalog of specific filtering requests to explore a schema in several filtering scenarios, and implements and evaluates the efficiency and effectiveness of a filtering engine prototype with several real case studies.The filtering methodology studies the characteristics of the knowledge contained within a large conceptual schema, and proposes ways to select and represent the user interest in order to specialize the results of a filtering engine. Consequently, a user focus on a fragment of the large schema of interest to her and our methodology automatically obtains a reduced conceptual schema extracted from the large schema and focused on the knowledge that has a closer relation with the focus of the user. Such filtered conceptual schema is a subset of the original one, and because of its reduced size it is more comprehensible to the user.The filtering approach provides knowledge extraction techniques aligned with the user interest representation, and presents such knowledge in an appropriate way to simplify its understanding. Furthermore, using this filtering approach the large conceptual schemas are navigated more quickly, increasing their usability and reducing the user effort. LIST OF FIGURES xiiAnyone who has never made a mistake has never tried something new.

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SQL-PL4OCL: an automatic code generator from OCL to SQL procedural language

Egea

Dania

2017

Softw Syst Model

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Constraints Checking in UML Class Diagrams: SQL vs OCL

Cited by 7 publications

References 4 publications

SBVR vs OCL: A comparative analysis of standards

SBVR vs OCL: A comparative analysis of standards

A filtering engine for large conceptual schemas

SQL-PL4OCL: an automatic code generator from OCL to SQL procedural language

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