Terry A. Halpin

Object-Role Modeling (ORM/NIAM)

Object-Role Modeling (ORM) is a method for modeling and querying an information system at the conceptual level, and mapping between conceptual and logical (e.g. relational) levels. ORM comes in various flavors, including NIAM (Natural language Information Analysis Method). This article provides an overview of ORM, and notes its advantages over Entity Relationship and traditional Object-Oriented modeling.

Conceptual Queries Using ConQuer-II

Formulating non-trivial queries in relational languages such as SQL and QBE can prove daunting to end users. ConQuer is a conceptual query language that allows users to formulate queries naturally in terms of elementary relationships, operators such as “and”, “or”, “not” and “maybe”, contextual for clauses and object-correlation, thus avoiding the need to deal explicitly with implementation details such as relational tables, null values, outer joins, group by clauses and correlated subqueries. While most conceptual query languages are based on the Entity-Relationship approach, ConQuer is based on Object-Role Modeling (ORM), which exposes semantic domains as conceptual object types, allowing queries to be formulated via paths through the information space. As a result of experience with the first implementation of ConQuer, the language has been substantially revised and extended to become ConQuer-II, and a new tool, ActiveQuery, has been developed with an improved interface. ConQuer-IIs new features such as arbitrary correlation and subtyping enable it to be used for a wide range of advanced conceptual queries.

ConQuer: A Conceptual Query Language

Relational query languages such as SQL and QBE are less than ideal for end user queries since they require users to work explicitly with structures at the relational level, rather than at the conceptual level where they naturally communicate. ConQuer is a new conceptual query language that allows users to formulate queries naturally in terms of elementary relationships, and operators such as “and”, “not” and “maybe”, thus avoiding the need to deal explicitly with implementation details such as relational tables, null values, and outer joins. While most conceptual query languages are based on the Entity-Relationship approach, ConQuer is based on Object-Role Modeling (ORM), which exposes semantic domains as conceptual object types, thus allowing queries to be formulated in terms of paths through the information space. This paper provides an overview of the ConQuer language.

Data modeling in UML and ORM: a comparison

The Unified Modeling Language (UML) is becoming widely used for software and database modeling, and has been accepted by the Object Management Group as a standard language for object-oriented analysis and design. For data modeling purposes, UML includes class diagrams, that may be annotated with expressions in a textual constraint language. Although facilitating the transition to object-oriented code, UML’s implementation concerns render it less suitable for developing and validating a conceptual model with domain experts. This defect can be remedied by using a fact-oriented approach for the conceptual modeling, from which UML class diagrams may be derived. ObjectRole Modeling (ORM) is currently the most popular fact-oriented approach to data modeling. This paper examines the relative strengths and weaknesses of ORM and UML for data modeling, and indicates how models in one notation can be translated into the other.

ORM 2

Object-role Modeling (ORM) is a fact-oriented modeling approach for specifying, transforming, and querying information at a conceptual level. Unlike Entity-Relationship modeling and Unified Modeling Language class diagrams, fact-oriented modeling is attribute-free, treating all elementary facts as relationships. For information modeling, fact-oriented graphical notations are typically far more expressive than other notations. Introduced 30 years ago, ORM has evolved into closely related dialects, and is supported by industrial and academic tools. Industrial experience has identified ways to improve current ORM languages (graphical and textual) and associated tools. A project is now under way to provide tool support for a second generation ORM (called ORM 2), that has significant advances over current ORM technology. This paper provides an overview of, and motivation for, the enhancements introduced by ORM 2, and discusses an open-source ORM 2 tool under development.

Conceptual schemas with abstractions making flat conceptual schemas more comprehensible

Abstract Flat graphical, conceptual modeling techniques are widely accepted as visually effective ways in which to specify and communicate the conceptual data requirements of an information system. Conceptual schema diagrams provide modelers with a picture of the salient structures underlying the modeled universe of discourse, in a form that can readily be understood by and communicated to users, programmers and managers. When complexity and size of applications increase, however, the success of these techniques in terms of comprehensibility and communicability deteriorates rapidly. This paper proposes a method to offset this deterioration, by adding abstraction layers to flat conceptual schemas. We present an algorithm to recursively derive higher levels of abstraction from a given (flat) conceptual schema. The driving force of this algorithm is a hierarchy of conceptual importance among the elements of the universe of discourse.

Database Schema Transformation and Optimization

An application structure is best modeled first as a conceptual schema, and then mapped to an internal schema for the target DBMS. Different but equivalent conceptual schemas often map to different internal schemas, so performance may be improved by applying conceptual transformations prior to the standard mapping. This paper discusses recent advances in the theory of schema transformation and optimization within the framework of ORM (Object-Role Modeling). New aspects include object relativity, complex types, a high level transformation language and update distributivity.

Fact‐oriented modelling for data analysis

Abstract. Although entity‐relationship modelling is the most popular means of specifying the conceptual schema for the data perspective of an information system, an alternative design approach known as fact‐oriented modelling appears to offer advantages. This paper provides an overview of fact‐oriented modelling, including some recent revisions and an evaluation of how the method has coped with large‐scale practical applications. One distinguishing feature of the approach is its emphasis on natural verbalization of information examples in terms of elementary facts. This notion of elementarity is clarified to cater for a problematic case involving compositely identified object types. Finally, the algorithm for mapping a conceptual to a relational schema is revised to cater for lazy object types.

Fact-Oriented Modeling: Past, Present and Future

Fact-oriented modeling is a conceptual approach that enables one to model and query business domains in terms of the underlying facts of interest, where all facts and rules may be verbalized in language readily understandable by non-technical users of those business domains. Unlike Entity-Relationship modeling and object-oriented modeling, fact-oriented modeling treats all facts as relationships (unary, binary, ternary etc.). Grouping of facts into attribute-based structures (e.g. ER entities, UML objects, database relations, XML elements) is considered a lower level, implementation issue that is irrelevant to capturing the essential business semantics. This chapter provides a brief history of the fact-oriented modeling approach, illustrates its main concepts and benefits via a case study, reviews the current state of the art in terms of methodology and tooling perspectives, and identifies several topics for future research.

Automated verbalization for ORM 2

In the analysis phase of information systems development, it is important to have the conceptual schema validated by the business domain expert, to ensure that the schema accurately models the relevant aspects of the business domain An effective way to facilitate this validation is to verbalize the schema in language that is both unambiguous and easily understood by the domain expert, who may be non-technical Such verbalization has long been a major aspect of the Object-Role Modeling (ORM) approach, and basic support for verbalization exists in some ORM tools Second generation ORM (ORM 2) significantly extends the expressibility of ORM models (e.g deontic modalities, role value constraints, etc.) This paper discusses the automated support for verbalization of ORM 2 models provided by NORMA (Neumont ORM Architect), an open-source software tool that facilitates entry, validation, and mapping of ORM 2 models NORMA supports verbalization patterns that go well beyond previous verbalization work The verbalization for individual elements in the core ORM model is generated using an XSLT transform applied to an XML file that succinctly identifies different verbalization patterns and describes how phrases are combined to produce a readable verbalization This paper discusses the XML patterns used to describe ORM constraints and the tightly coupled facilities that enable end-users to easily adapt the verbalization phrases to cater for different domain experts and native languages.