Book chapter on conceptual data modelling for biological data

My invited book chapter, entitled “Ontology-driven formal conceptual data modeling for biological data analysis” [1], recently got accepted for publication in the Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data, edited by Mourad Elloumi and Albert Y. Zomaya, and is scheduled for printing by Wiley early 2012.

All this started off with my BSc(Hons) in IT & Computing thesis back in 2003 and my first paper about the trials and tribulations of conceptual data modelling for bio-databases [2] (which is definitely not well-written, but has some valid points and has been cited a bit). In the meantime, much progress has been made on the topic, and I’ve learned, researched, and published a few things about it, too. So, what is the chapter about?

The main aspect is the ‘conceptual data modelling’ with EER, ORM, and UML Class Diagrams, i.e., concerning implementation-independent representations of the data to be managed for a specific application (hence, not ontologies for application-independence).

The adjective ‘formal’ is to point out that the conceptual modeling is not just about drawing boxes, roundtangles, and lines with some adornments, but there is a formal, logic-based, foundation. This is achieved with the formally defined CMcom conceptual data modeling language, which has the greatest common denominator between ORM, EER, and UML Class Diagrams. CMcom has, on the one hand, a mapping the Description Logic language DLRifd and, on the other hand, mappings to the icons in the diagrammatic languages. The nice aspect of this it that, at least in theory and to some extent in practice as well, one can subject it to automated reasoning to check consistency of the classes, of the whole conceptual data model, and derive implicit constraints (an example) or use it in ontology-based data access (an example and some slides on ‘COMODA’ [COnceptual MOdel-based Data Access], tailored to ORM and the horizontal gene transfer database as example).

Then there is the ‘ontology-driven’ component: Ontology and ontologies can aid in conceptual data modeling by providing solution to recurring modeling problems, an ontology can be used to generate several conceptual data models, and one can integrate (a section of) an ontology into a conceptual data model that is subsequently converted into data in database tables.

Last, but not least, it focuses on ‘biological data analysis’. A non-(biologist or bioinformatician) might be inclined to say that should not matter, but it does. Biological information is not as trivial as the typical database design toy examples like “Student is enrolled in Course”, but one has to dig deeper and figure out how to represent, e.g., catalysis, pathway information, the ecological niche. Moreover, it requires an answer to ‘which language features are ‘essential’ for the conceptual data modeling language?’ and if it isn’t included yet, how do we get it in? Some of such important features are n-aries (n>2) and the temporal dimension. The paper includes a proposal for more precisely representing catalysis, informed by ontology (mainly thanks to making the distinction between the role and its bearer), and shows how certain temporal information can be captured, which is illustrated by enhancing the model for SARS viral infection, among other examples.

The paper is not online yet, but I did put together some slides for the presentation at MAIS’11 reported on earlier, which might serve as a sneak preview of the 25-page book chapter, or you can contact me for the CRC.

References

[1] Keet, C.M. Ontology-driven formal conceptual data modeling for biological data analysis. In: Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data. Mourad Elloumi and Albert Y. Zomaya (Eds.). Wiley (in print).

[2] Keet, C.M. Biological data and conceptual modelling methods. Journal of Conceptual Modeling, Issue 29, October 2003. http://www.inconcept.com/jcm.

Identification and keys in UML Class Diagrams

ER, its extended version EER, ORM, and it extended version ORM2 have several options for identification with keys/reference schemes. UML Class Diagrams, on the other hand, have internal, system-generated, identifiers, with a little-known and underspecified option for user-defined identifiers inspired by ER’s keys, whose description is buried in the standard on pp290-293 [1]. Although UML’s ease of system-generated identifiers relieves the burden of detailed conceptual analysis by the modeller, it is exactly making implicit subject domain semantics explicit that is crucial in the analysis stage; or: less analysis during the modelling stage stores up more problems down the road in terms of software bugs and interoperability. A uniform, or at least a structured and unambiguous approach, can reduce or even avoid inconsistencies in a conceptual data model, especially concerning taxonomies, achieve interoperability through less resource-consuming information integration, and if the identification mechanisms are the same across conceptual data modeling language, then unification among them comes a step closer. The present lack of harmonisation in handling identification hampers all this.

But which identification mechanism(s) could, or should, be specified more precisely for UML, and how does it rhyme with progress about identity in Ontology? How to incorporate it in UML to foster consistent usage? For instance, should the procedure to find and represent identity, or at least good keys, be a step in the modelling methodology, also be enforced in the CASE tool, or be part of the metamodel and/or in the conceptual modelling language itself?

The distinct implicit assumptions and explicit formalisations of extant identification schemes are elucidated in [2] for ER, EER, ORM, and ORM2. As it appears, not even ER/EER and ORM/ORM2 agree fully on keys and reference schemes, neither from a formal perspective (though the difference is minimal), nor from a methodological or CASE tool perspective. Both, however, do aim at strong or weak identification of entities with so-called natural keys (/semantic identifiers) in particular.

At the other end of the spectrum, Ontology does have something on offer, but the philosophers are only interested in identity of entities. Moreover, it is not just that they don’t agree on the details, but there is a tendency to admit it is nigh on inapplicable (see [3] for a good introduction). Watered-down versions of the notion of identity in philosophy that have been proposed in AI, are to used either only the necessary or only the sufficient conditions, which are at least somewhat applicable, be it as a basis for OntoClean [4], or in Description Logic languages (including OWL 2) with their primitive and defined classes. The details of the definitions, explanations, and pros and cons are presented in a ‘digested’ format in the first part of [2].

This analysis was subsequently used to increase the ontological foundations of UML in the second part of [2] to introduce two language enhancements for UML, being formally defined simple and compound identifiers and the notion of defined class, which also have a corresponding extension of UML’s metamodel. The proposed extensions focus on practical usability in conceptual data modelling, informed by ontology, and are approximations to the qualitative, relative, and synchronic identity, and to the notion of equivalent class (defined concept) in OWL (DL).

For those of you who do not care about the ‘unnecessary philosophizing’ (as one reviewer had put it) and justifications, there is a short (4-page) version [5] with the formal definitions and the UML extensions, which has been accepted at the SAICSIT’11 conference in Cape Town, South Africa. The longer version that provides explanation as to why the proposed extensions are the way they are is available as a SoCS technical report [2].

References

[1] Object Management Group. Ontology definition metamodel v1.0. Technical Report formal/2009-05-01, Object Management Group, 2009.

[2] Keet, C.M. Enhancing identification mechanisms in UML class diagrams with meaningful keys (extended version). Technical Report SoCS11-1, School of Computer Science, University of KwaZulu-Natal, South Africa. August 8, 2011.

[3] H. Noonan. Identity. In E. N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Fall 2008 edition, 2008.

[4] N. Guarino and C. Welty. Identity, unity, and individuality: towards a formal toolkit for ontological analysis. In W. Horn, editor, Proc. of ECAI’00. IOS Press, Amsterdam, 2000.

[5] Keet, C.M. Enhancing Identification Mechanisms in UML Class Diagrams with Meaningful Keys. SAICSIT Annual Research Conference 2011 (SAICSIT’11), Cape Town, October 3-5, 2011. ACM Conference Proceedings (in print).

Recap of the sixth workshop on Fact-Oriented Modelling: ORM’10

The sixth workshop on Fact-Oriented/Object-Role Modelling (ORM’10) in Hersonissou, Crete, Greece, and co-located with the OTM conference just came to a close after a long session on metamodelling to achieve a standard exchange format for the different ORM tools that are in use and under development (such as NORMA, DocTool, and CaseTalk). The other sessions during these three days were filled with paper presentations and several tool demos, reflecting not only the mixed audience of academia and industry, but also the versatility of fact-oriented modelling. I will illustrate some of that in the remainder of the post. (Note: ORM is a conceptual data modelling language that enjoys a formal foundation, and a graphical interface to draw the diagrams and a textual interface to verbalize the domain knowledge so as to facilitate communication with, and validation by, the domain experts.)

An overview of a novel mapping of ORM2 to Datalog^LB was presented by Terry Halpin from LogicBlox and INTI International University [1]. The choice for such a mapping was motivated by the support for rules in Datalog so as to also have a formal foundation and implemented solution for the (derivation) rules one can define in an ORM conceptual data model in the NORMA tool.

Staying with formalisms (but of a different kind and scope), Fazat Nur Azizah from the Bandung Institute of Technology proposed a grammar to specify modelling patterns so that actual patterns can be reused for different conceptual data models—alike software design patterns, but then for the FCO-IM flavour of fact-oriented conceptual data modelling [2].

At the other end of the spectrum were two papers that proposed and assessed the use and benefits of ORM in the setting of understanding natural language text documents. Ron McFadyen from the University of Winnipeg introduced document literacy and ORM [3]. Peter Bollen from Maastricht University showed how ORM can improve the completeness and maintenance of specifications like the Business Process Model and Notation [4], which is in analogy with the WSML-documentation-in-ORM [5] and thereby thus strengthening the case that one indeed can be both more precise and communicative with one’s specification if accompanied by a representation in ORM.

There was a session on Master Data Management (MDM), presented by Baba Piprani from MetaGlobal Systems and Patricia Schiefelbein from Boston Scientific. However, I got a bit sidetracked when Baba Piprani had an interesting quote called the “Helsinki principle”, being

Any meaningful exchange of utterances depends upon the prior existence of an agreed set of semantic and syntactic rules. The recipients of the utterances must use only these rules to interpret the received utterances, if it is to mean the same as that which was meant by the utterer. (ISO TR9007)

whereas I was associating the term “Helsinki principle” with a wholly different story, being the right to self-determination described in the Helsinki accords on security and cooperation in Europe. Now, it happens to be the case that proper MDM contributes to solving semantic mismatches.

Last, there was a session on extensions. Tony Morgan from INTI International University [6] had a go at folding and zooming, presenting an alternative approach to abstraction for large ORM diagram (that is, alternative to [7,8] and the many other proposals outside ORM); it introduced new notations, the code-folding idea for but then for ORM diagrams, and a lightweight algorithm. Yan Tang from STARLab at the Free University of Brussels elaborated on the interaction between semantic decision tables and DOGMA [9] (DOGMA is an approach and tool that reuses ORM notation for ontology engineering). Last, but not least, I presented the paper by Alessandro Artale and myself about the basic constraints for relation migration [10], about which I wrote in an earlier blog post.

To wrap up, the workgroup on the common exchange format for fact-oriented modelling tools—chaired by Serge Valera from the European Space Agency—will continue their work toward standardization, the slides of the presentations will be made available on the ORM Foundation website in these days, and else it is on heading towards the 7^th ORM workshop next year somewhere in the Mediterranean.

References

(Unfortunately, at the time of writing, most of the papers are still in the proceedings behind Springer’s paywall)

[1] Terry Halpin, Matthew Curland, Kurt Stirewalt, Navin Viswanath, Matthew McGill, and Steven Beck. Mapping ORM to Datalog: An Overview.
 International Workshop on Fact-Oriented Modeling (ORM’10), Hersonissou, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer, LNCS 6428, 504-513.

[2] Fazat Nur Azizah, Guido P. Bakema, Benhard Sitohang, and Oerip S. Santoso. Information Grammar for Patterns (IGP) for Pattern Language of Data Model Patterns Based on Fully Communication Oriented Information Modeling (FCO-IM). International Workshop on Fact-Oriented Modeling (ORM’10), Hersonissou, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer, LNCS 6428, 522-531.

[3] Ron McFadyen and Susan Birdwise. Literacy and Data Modeling. International Workshop on Fact-Oriented Modeling (ORM’10), Hersonissou, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer,
LNCS 6428, p. 532-540.

[4] Peter Bollen. A Fact-Based Meta Model for Standardization Documents. International Workshop on Fact-Oriented Modeling (ORM’10), Hersonissou, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer,
LNCS 6428, p. 464-473.

[5] Tziviskou, C. and Keet, C.M. A Meta-Model for Ontologies with ORM2. Third International Workshop on Object-Role Modelling (ORM’07), Algarve, Portugal, Nov 28-30, 2007. Meersman, R., Tari, Z., Herrero., P. et al. (Eds.), Springer, LNCS 4805, 624-633.

[6] Tony Morgan. A Proposal for Folding in ORM Diagrams. International Workshop on Fact-Oriented Modeling (ORM’10), Hersonissou, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer, LNCS 6428, 474-483.

[7] Keet, C.M. Using abstractions to facilitate management of large ORM models and ontologies. International Workshop on Object-Role Modeling (ORM’05). Cyprus, 3-4 November 2005. In: OTM Workshops 2005. Halpin, T., Meersman, R. (eds.), Lecture Notes in Computer Science LNCS 3762. Berlin: Springer-Verlag, 2005. pp603-612.

[8] Campbell, L.J., Halpin, T.A. and Proper, H.A.: Conceptual Schemas with Abstractions: Making flat conceptual schemas more comprehensible. Data & Knowledge Engineering (1996) 20(1): 39-85

[9] Yan Tang. Towards Using Semantic Decision Tables for Organizing Data Semantics. International Workshop on Fact-Oriented Modeling (ORM’10), Hersonissou, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer, LNCS 6428, 494-503.

[10] Keet, C.M. and Artale, A. A basic characterization of relation migration. International Workshop on Fact-Oriented Modeling (ORM’10), Hersonissou, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer, LNCS. 6428, 484-493.

Constraints for migrating relations over time

Migrating objects in a database is nothing new—employees become managers, MSc students PhD students and so forth—and this has been investigated and implemented widely in temporal databases. But imagine a scenario for an airline company’s passenger RDBMS and a passenger who books a flight, hence we have a relation ⟨John, AZ123⟩ ∈ Booking with John ∈ Passenger and AZ123 ∈ Flight, which is normally followed by the events that John also checks in and boards the plane afterward, i.e., ⟨John, AZ123⟩ ∈ CheckIn and then ⟨John, AZ123⟩ ∈ Boarding. While the booking relation holds even after the tuple extended to the check-in relation, i.e., ⟨John, AZ123⟩ is a member of both Booking and CheckIn relations, this is not the case for the step from check-in to boarding which causes the tuple ⟨John, AZ123⟩ to be moved from one table to another in the operational database. In addition, for any tuple that is member of the Boarding relation, we know that it must have been a member of CheckIn relation sometime earlier. Clearly, airline companies implement some code to keep track of such changes, but how to represent this in a conceptual data model?

Or take a simple change in relation between two objects can be caused by the fact that a is structurally a part of b but a gets loose so after that a becomes spatially contained in b; e.g., a component in a medical device breaks loose due to wear and tear. Then it would be nice if a fault detection system can send such a message back to control, compared to the imprecise “there’s something wrong over there”.

A related issue is keeping track of the status of the same relation. Take, for instance, the issues with subquantities with, say, a bottle of wine and pouring a subquantity of the wine into a wineglass so that this subquantity in the glass used to be—but not is—a subquantity of the wine in the bottle and one wants to maintain traceability of quantities over time. This is important especially in the food industry for food safety in the food processing chain, hence, the data management has to be able to deal with such cases adequately and transparently.

Perhaps surprisingly, there is no conceptual data modelling language that lets you model the business knowledge where relations migrate during the conceptual analysis stage.  By relation migration, I mean the change of membership of a tuple from one relation to another. Thus, relation migration is distinct from state transition diagrams that concern states of single objects, from activity diagrams that concern processes but do not explicitly consider the participating entities, and from interaction diagrams for modelling use cases. Here we focus explicitly on the migration of facts/tuples/relation instances and the corresponding temporal behaviour of fact types/relations. So, in analogy to object migration, there is a usefulness of a similar set of constraints for relations that can be called relation migration.

Alessandro Artale and I specified the basic constraints that hold for relation migration, which recently got accepted for the ORM’10 workshop co-located with the OTM conference. The paper’s [1] abstract is as follows:

Representing and reasoning over evolving objects has been investigated widely. Less attention has been devoted to the similar notion of relation migration, i.e., how tuples of a relation (ORM facts) can evolve along time. We identify different ways how a relation can change over time and give a logic-based semantics to the notion of relation migration to capture its behaviour. We also introduce the notion of lifespan of a relation and clarify the interactions between object migration and relation migration. Its use in graphical conceptual data modelling is illustrated with a minor extension to ORM2 so as to more easily communicate such constraints with domain experts.

We distinguish between evolution constraints—specifying how elements of a relation can possibly migrate to another relation—persistence constraints—specifying persistent states for a relation—and quantitative evolution constraints—specifying the exact amount of time for the relation migration to happen. In addition, one has to consider the lifespan of relations. Together, they result in 15 axioms for the evolution and persistence constraints, and 3 propositions concerning the logical implications with respect to subsumption and relation migration, relation migration vs. lifespan, and objects vs. a relation’s lifespan.

Concerning the interaction object and relation migration, we found two types, one where an object migration forces a relation to migrate, and one where a relation migration forces an object migration. For instance, take a company where, at some point in time, each employee will be promoted within the same department he or she works for (for simplicity: the employee works for exactly one department) and such that demotion does not occur. This means an object migration of type PEX (Persistent Extension) between Employee and Manager (see figure, below). This forces a relation migration of type RPEX between worksFor and manages in order to maintain consistency of the conceptual data model (see figure below).

Example of an object migration (dashed purple arrow) that forces a relation migration (dashed green arrow)

The last word has not been said yet about incorporating rigidity fully in this framework, nor tractable reasoning with relation migration, but at least the foundational aspects of relation migration have been identified and characterized formally, which already can be added to conceptual data modelling languages, as illustrated for the Object-Role Modeling language in [1].

References

[1] Keet, C.M. and Artale, A. A basic characterization of relation migration. International Workshop on Fact-Oriented Modeling (ORM’10), Crete, Greece, October 27-29, 2010. Meersman, R., Herrero, P. (Eds.), OTM Workshops, Springer, Lecture Notes in Computer Science LNCS. (to appear)

A note on improving the quality of conceptual data models with a reasoner

Moving back to work-related topics, let us have a look at quality of conceptual data models; for the ontologies-person: a conceptual data model is, roughly, a so-called “application ontology”, with data types, a relatively close resemblance to the database or application it was developed for, and generally without the heavy logical apparatus behind it. Some of the well-known conceptual data modeling languages are UML, ER/EER, and ORM/ORM2.

What exactly a “good” and a “bad” conceptual model is, is not clearly specified, but experienced modellers know when they see one. However, there are few experienced modellers compared to the databases and application software around, they are not flawless (no one is), and when the conceptual model becomes large, errors creep in anyway due to the so-called “cognitive overload”. Much effort has gone into improving the methodology of, what is called in information systems development, the conceptual analysis stage of the whole software development process as well as the, mostly graphical, conceptual modelling languages; both topics seem to be tremendous sources of turf wars. In addition, the contribution that computers and specialised software can make beyond the standard CASE tools—that, at best, can validate the conceptual model (i.e., that the model is syntactically correct, but not semantically)—is barely known, and largely an ignored aspect of the whole modeling process.

Now, I will try to talk together three seemingly, but not quite, independent events leading to the real point. (note to the reader: they give a context, but could be skipped)

First, a few researchers in conceptual data modeling have taking notice of what is happening in the ontologies arena and, not surprisingly, taken up the idea recently that, perhaps, something like that could well be used to improve conceptual data models, too [1-3]. The basic idea to reason over conceptual data models, however, was conceived at least as far back as the early ‘90s, when it was both intended to improve the quality of the models and for schema-based database integration, although it has not entertained wide-spread user-adoption (nor those reported in refs [1-3], for that matter). See the works by the DIS group of Lenzerini at “la Sapienza” University in Rome (Calvanese, Di Giacomo, Lenzerini, Nardi, and cs.).

Second, having had to visit the KSG at Meraka for 6 weeks as part of the Italy-South Africa cooperation on “Conceptual modeling and intelligent query formulation” with as aim to find some common interest to work on within the project’s scope (which was obviously not the PsyOps ontology development to ultimately streamline torture), the notion of ‘intelligent conceptual modelling’ came up again (see also the extended EMMSAD’08 presentation).

Third, last year a distinguished ex-Microsoft Visio senior programmer, Matthew Curland, visited us to explain the machinery behind the NORMA CASE tool (a free MS Visio plugin, available from sourceforge), which automatically generates a range of types application code (C#, SQL, etc.) based on an ORM2 conceptual data model. He, as well as other modellers, however, did not see an advantage to enhancing the quality of conceptual models by using reasoners compared to a validator that both NORMA and its predecessor, VisioModeler, already have. Admitted, we did not have many clear examples readily at hand back then.

Given these three events, and a recent ORM Foundation forum digression on solving problems vs. inventing them, I’ve tried to put my layout skills, preference for figures, and sense of colour-coding to, hopefully, good use to unambiguously demonstrate the differences between mere validation of a conceptual model and, among others, satisfiability checking. The automated reasoning over the conceptual data model fishes out semantic errors and, equally useful, derives additional constraints that were not explicitly modelled in the conceptual model (well, missed by the modeler). The examples were done with the reasoner-enhanced modeling tool, icom [5], and compared to the NORMA CASE tool.

Considering the demonstrated differences in the pdf we can go back to the notion of quality of conceptual data models: clearly one that is (i) consistent and (ii) as inclusive with the constraints as necessary is a better one. Regarding the former, timely detecting inconsistent, unsatisfiable, classes prevents the error(s) from propagating down to the implementation, where it otherwise results in, e.g., a class that is never instantiated or a table that remains empty, which is normally not the intention. Regarding the latter, having implicit constraints explicit at the modeling stage can ensure their correct implementation in the software or undesirable consequences can be fixed before implementation as opposed to find out during testing or operation and having to back-track the issue.

A reasoner, be it special purpose one as in [2,3] or DL-based [1,4], thus, does contribute to the goal of improving the quality of the conceptual model and, hence, the software. Or, to rephrase it in terms of solving problems: there is a lot of buggy software, and good conceptual modeling is a well-known, comparatively cheap, way of preventing such problems compared to the costly and time-consuming bug-fixing and laborious maintenance. The reasoner-enhanced conceptual modelling, then, is another feature in the conceptual modeller’s ‘toolbox’ to prevent such problems that hitherto still fell through the cracks with traditional conceptual modeling.

But someone’s interests may not lie in obtaining good conceptual data models—after all, testers and programmers want to keep their job, and so do consultants for database and application software reengineering, or researchers who focus on how to deal with inconsistent databases, methods for elaborate maintenance strategies, and whatnot. There are other advantages, though, than just good conceptual data models or application ontologies, such as relegating the graphical syntax to being “syntactic sugar” by unifying the modeling languages (see [5] and references therein), which then enables HCI researchers to have a look at what would be the best set of icons for graphical modeling or natural language experts to enhance the textual interfaces for conceptual modelling to make the modelling a more fruitful process for the modeller and domain expert alike, or to accommodate ardent supporters of, say, UML to constructively collaborate with modellers who fancy ORM in a way so that they all can keep their preferred diagrams yet work on one common conceptual data model. But more about that another time.

—————-

[1] M. Balaban, A. Maraee, A UML-based method for deciding finite satisfiability in description logics, in: F. Baader, C. Lutz, B. Motik (eds.), Proceedings of the 21st International Workshop on Description Logics (DL’08), vol. 353 of CEUR-WS, 2008, Dresden, Germany, May 13-16, 2008.

[2] K. Kaneiwa, K. Satoh, Consistency checking algorithms for restricted UML class diagrams, in: Proceedings of FoIKS ’06, Springer Verlag, 2006.

[3] Y. Smaragdakis, C. Csallner, R. Subramanian, Scalable automatic test data generations from modeling diagrams, in: Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE’07), 2007, Nov. 5-9, Atlanta, Georgia, USA

[4] E. Franconi, G. Ng, The ICOM tool for intelligent conceptual modelling, in: 7th Workshop on Knowledge Representation meets Databases (KRDB’00), 2000, Berlin, Germany, 2000.

[5] C.M. Keet, Unifying industry-grade class-based conceptual data modeling languages with CMcom. 21st International Workshop on Description Logics (DL’08), 13-16 May 2008, Dresden, Germany. CEUR-WS, Vol-353.

Two days of “ORMing” at the International Workshop on Object-Role Modelling 2006

The second international workshop on Object-Role Modelling was held last week in Montpellier in conjunction with the OTM conferences. The joy of being with likeminded people who also think ORM is the best conceptual modeling language around for the moment works stimulating. Of course, there exist workshops which thank their whole existence to the fact that the organizers couldn’t get their papers published anywhere else. But not so with the ORM workshop, presenters, and participants who included a.o. Sjir Nijssen, Terry Halpin, Erik Proper, Necito de la Cruz, and Ken Evans. The mix of academia and industry seems to have found a common ground and even managed to communicate with each other (if only this would be the case with researchers and bioinformaticians…).

Making ORM models is not as easy as putting together an ER model or drawing a UML diagram, but it is more expressive, easier to maintain, really design- and implementation-independent, etc. etc., and in the end, the resulting software has a better quality (thus: happier customers). Among other advantages, it forces one to do the thinking during the analysis phase of development instead of going over the deadline in the testing stage. So, it’s not particularly suitable for life scientists who want a one-off tool by tomorrow and that works only tomorrow, but it is for those who want a tool that also works the day after tomorrow and that is easier to maintain when biologists want more features.

A nice thing with ORM is its versatility: it’s not just a conceptual modeling language, but comprises aspects such as methodology, usability, and a variety of usage possibilities, such as database development, data warehouses, other types of applications, requirements engineering, and, to my surprise, even for assessing textbook complexity.

Some of the topics that passed the revue this year were language extensions and expressivity of the language, e.g. adding dynamic rules [1], representing part-whole relations [2], and from the perspective of designing programming languages [3]. A second set of papers looked into architecture and conceptual modeling methodologies [4] [5] [6]. A paper on Dogma [7] was, like last year, good for starting a discussion on realists versus, well, non-realists, and on conceptual models vs ontologies. I’ll leave both topics for another time, but the interested reader may want to have a look at Ingvar Johansson’s introduction for non-philosophers [8].

A full list of accepted papers is available online. An ORM foundation is in the process of being set up (for now, quite a lot of information can be found on http://www.orm.net).

[1] Balsters, H., Carver, A., Halpin, T., Morgan, T. Modeling Dynamic Rules in ORM. 2nd International Workshop on Object-Role Modelling (ORM 2006), Montpellier, France, Nov 2-3, 2006. In: OTM Workshops 2005. Meersman, R., Tari, Z., Herrero., P. et al. (Eds.), Lecture Notes in Computer Science 4278. Berlin: Springer-Verlag, 2006. pp1201-1201.

[2] Keet, C.M. Part-whole relations in Object-Role Models. 2nd International Workshop on Object-Role Modelling (ORM 2006), Montpellier, France, Nov 2-3, 2006. In: OTM Workshops 2005. Meersman, R., Tari, Z., Herrero., P. et al. (Eds.), Lecture Notes in Computer Science 4278. Berlin: Springer-Verlag, 2006. pp1116-1127.

[3] Betsy Pepels, Rinus Plasmeijer, and H.A. (Erik) Proper. Fact-oriented modeling from a programming language designer’s perspective. 2nd International Workshop on Object-Role Modelling (ORM 2006), Montpellier, France, Nov 2-3, 2006. In: OTM Workshops 2005. Meersman, R., Tari, Z., Herrero., P. et al. (Eds.), Lecture Notes in Computer Science 4278. Berlin: Springer-Verlag, 2006. pp1170-1180.

[4] P. van Bommel, S.J.B.A. Hoppenbrouwers, H.A. (Erik) Proper and Th.P. van der Weide. Giving Meaning to Enterprise Architectures: Architecture Principles with ORM and ORC. 2nd International Workshop on Object-Role Modelling (ORM 2006), Montpellier, France, Nov 2-3, 2006. In: OTM Workshops 2005. Meersman, R., Tari, Z., Herrero., P. et al. (Eds.), Lecture Notes in Computer Science 4278. Berlin: Springer-Verlag, 2006. pp1138-1147.

[5] P. van Bommel, S.J.B.A. Hoppenbrouwers, H.A. (Erik) Proper and Th.P. van der Weide Exploring modelling strategies in a meta-modelling context. 2nd International Workshop on Object-Role Modelling (ORM 2006), Montpellier, France, Nov 2-3, 2006. In: OTM Workshops 2005. Meersman, R., Tari, Z., Herrero., P. et al. (Eds.), Lecture Notes in Computer Science 4278. Berlin: Springer-Verlag, 2006. pp1128-1137.

[6] S.J.B.A. (Stijn) Hoppenbrouwers, L. (Leonie) Lindeman and H.A. (Erik) Proper. Capturing Modeling Processes – Towards the MoDial Modeling Laboratory. 2nd International Workshop on Object-Role Modelling (ORM 2006), Montpellier, France, Nov 2-3, 2006. In: OTM Workshops 2005. Meersman, R., Tari, Z., Herrero., P. et al. (Eds.), Lecture Notes in Computer Science 4278. Berlin: Springer-Verlag, 2006. pp1242-1252.

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