Orm 2

Halpin, Terry

doi:10.1007/11575863_87

Cited by 66 publications

(21 citation statements)

References 14 publications

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“…Since that study, LogicBlox developers have been creating ORM2 [11] models using Norma [7], a mature ORM modeling tool that supports the modeler in finding and specifying more complex constraints during conceptual modeling. Consequently, newer models developed at LogicBlox feature ORM2 constraints that are not supported in ORM − .…”

Section: Impact Of Our Extensionsmentioning

confidence: 99%

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Scalable analysis of conceptual data models

McGill

Dillon

Stirewalt

2011

Proceedings of the 2011 International Symposium on Software Testing and Analysis

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Conceptual data models describe information systems without the burden of implementation details, and are increasingly used to generate code. They could also be analyzed for consistency and to generate test data except that the expressive constraints supported by popular modeling notations make such analysis intractable. In an earlier empirical study of conceptual models created at LogicBlox Inc., Smaragdakis, Csallner, and Subramanian found that a restricted subset of ORM, called ORM − , includes the vast majority of constraints used in practice and, moreover, allows scalable analysis. After that study, however, LogicBlox Inc obtained a new ORM modeling tool, which supports discovery and specification of more complex constraints than the previous tool. We report findings of a follow-up study of models constructed using the more powerful tool. Our study finds that LogicBlox developers increasingly rely on a small number of features not in the ORM − subset. We extend ORM − with support for two of them: objectification and a restricted class of external uniqueness constraints. The extensions significantly improve our ability to analyze the ORM models created by developers using the new tool. We also show that a recent change to ORM has rendered the original ORM − algorithms unsound, in general; but that an efficient test suffices to show that these algorithms are in fact sound for the ORM − constraints appearing in any of the models currently in use at LogicBlox.

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Section: Impact Of Our Extensionsmentioning

confidence: 99%

“…Shortly after that study, however, LogicBlox obtained a new modeling tool, called Norma [7], which supports finding and specifying additional constraints not supported by the previous modeling tool. Based on ORM version 2 (ORM2) [11], Norma also incorporates subtle changes in the semantics of some of the constraints from ORM1.…”

Section: Introductionmentioning

confidence: 99%

Scalable analysis of conceptual data models

McGill

Dillon

Stirewalt

2011

Proceedings of the 2011 International Symposium on Software Testing and Analysis

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“…We chose not to include ORM's deontic rules (as well as Dynamic rules) in this chapter as they are not yet implemented by any ORM tool, and they lack formal semantics. More about this extension to ORM can be found in (Halpin, 2005a).…”

Section: Object Role Modeling (Orm)mentioning

confidence: 99%

Graphical Notations for Rule Modeling

Lukichev

Jarrar

2009

Handbook of Research on Emerging Rule-Based Languages and Technologies

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This chapter describes various graphical notations for rule modeling. Rule modeling methodologies, empowered with graphical notations, play an important role in helping business experts and rule engineers to represent business rules formally for further deployment into a rule execution system. Rules, represented graphically, can be easier understood by business people and by technicians without intensive technical learning. In this chapter we mainly focus on three graphical notations for rules: UML/OCL, URML and ORM. UML/OCL is a mainstream modeling technology in software development, which is also accommodated by some business experts when modeling a system at the semi-formal, platform independent level. URML extends UML with additional graphical symbols and the concept of a rule, which allows visualization of different rule types on top of UML class diagrams. ORM is an alternative methodology with a rich graphical notation for modeling a domain at the conceptual level. The methodological power, graphical expressivity, and verbalization capabilities of ORM have made it the most popular language within the business rules community. This chapter introduces each of these graphical notations, explain how it can be used, and compare them against each other.

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“…Fig. 1 shows a functional fact type in ORM 2 notation [11], where role names may be displayed in square brackets and used to verbalize rules in attribute-style [9]. Suppose a dynamic constraint requires that salaries of employees must not decrease.…”

Section: Updating Single-valued Roles In a Functional Fact Typementioning

confidence: 99%

Modeling Dynamic Rules in ORM

Balsters

Carver

Halpin

et al. 2006

On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops

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This paper proposes an extension to the Object-Role Modeling approach to support formal declaration of dynamic rules. Dynamic rules differ from static rules by pertaining to properties of state transitions, rather than to the states themselves. In this paper, application of dynamic rules is restricted to so-called single-step transactions, with an old state (the input of the transaction) and a new state (the direct result of that transaction). Such restricted rules are easier to formulate (and enforce) than a constraint applying historically over all possible states. In our approach, dynamic rules specify an elementary transaction type indicating which kind of object or fact is being added, deleted or updated, and (optionally) pre-conditions relevant to the transaction, followed by a condition stating the properties of the new state, including the relation between the new state and the old state. These dynamic rules are formulated in a syntax designed to be easily validated by non-technical domain experts.

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Orm 2

Cited by 66 publications

References 14 publications

Scalable analysis of conceptual data models

Scalable analysis of conceptual data models

Graphical Notations for Rule Modeling

Modeling Dynamic Rules in ORM

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