Do We Need an Instance-Based Conceptual Modeling Grammar?

Lukyanenko et al.

Instance-Based Conceptual Modeling Grammar

Do We Need an Instance-Based Conceptual Modeling Grammar? Roman Lukyanenko Florida International University [email protected]

Jeffrey Parsons Memorial University of Newfoundland [email protected] Binny Samuel Western University [email protected]

ABSTRACT

Many conceptual modeling grammars have been developed since 1970s, all sharing a general assumption of representation by abstraction (e.g., representing generalized knowledge about domains rather than concrete domain objects). This neglects the fundamental role instances play in the composition of reality and in human psychology. In this paper we make a case for building grammars that explicitly recognize the primary role of instances. Keywords

Conceptual Modeling, Information Systems Analysis and Design, Database Design, Information Quality, Ontology, Cognition INTRODUCTION

Conceptual modeling is an important component of information systems (IS) development (Mumford and Henshall 1979; Checkland and Holwell 1998; Rossi and Siau 2000; Wand and Weber 2002). Commonly defined as “the activity of formally describing some aspects of the physical and social world around us for the purposes of understanding and communication” (Mylopoulos 1992), conceptual modeling has traditionally been concerned with capturing information requirements at early stages of IS development. Once constructed, a conceptual model guides the design of the database schema, user interface, and programming code, as well facilitates domain understanding and communication by the developers and users (Kung and Solvberg 1986; Mylopoulos 1998; Rossi and Siau 2000; Wand and Weber 2002). Since early 1970s over a hundred different modeling notations have been proposed, some becoming popular and widely used, including the Entity-Relationship (ER) Diagrams (Chen 1976), Unified Modeling Language (UML) (Jacobson et al. 1999; Evermann and Wand 2001), ORM (Halpin 2007), i* (Yu 2002), Telos (Mylopoulos 1992; Borgida 2009) and their numerous extensions (Peckham and Maryanski 1988; Chen 2006). The proliferation of novel modeling grammars and extensions

led many to conclude that progress at the grammar level was minimal, and the focus should shift to comparison and evaluation of different grammars and efforts to find sound theoretical basis (e.g., ontological, cognitive) for various design decisions (Wand et al. 1995; Wand and Weber 2002; Burton-Jones et al. 2009; Moody 2009). Here we make a case that the time is right for a renaissance in novel grammar development. Existing grammars share a critical common principle –that of representation by abstraction, wherein application domain knowledge is captured as generalizations (e.g., classes, entity types) about concrete phenomena (e.g., objects, instances) (Smith and Smith 1977; Peckham and Maryanski 1988; Mylopoulos 1998) – which increasingly limits the usefulness of information systems developed based on such grammars. We argue that another, more fundamental, process – that of recognizing the existence and representation of instances –has been all but ignored in building conceptual modeling grammars. We present below an argument for the consequences of this omission for (1) interacting with conceptual modeling diagrams (e.g., domain understanding, comprehension, verification), (2) design of IS objects such as database schema, user interface, and programming, and (3) quality of data and decisions based on data stored in IS. The Role of Abstraction in Conceptual Modeling A fundamental approach to conveying domain semantics, abstraction enables analysts to deliberately ignore the many individual differences among phenomena and represent only relevant information. Abstraction is core to popular conceptual modeling grammars. For example, a script made using ER or UML grammars depicts classes, attributes of classes, and relationships between classes. Classes (e.g., student, tree, chair) abstract from differences among instances (e.g., the specific characteristics of a particular student, or a specific chair), and capture the perceived equivalence of instances. Indeed, many conceptual modeling grammars consider instances (objects) to necessarily be members of their classes (entity types): “[o]ne principle of conceptual modeling is that domain objects are instances of entity types” (Olivé 2007). Abstraction-based modeling has

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been claimed to be critical to “organize the information base and guide its use, making it easier to update or search it” (Mylopoulos and Borgida 2006). With representation by abstraction as a modeling principle, some contend it is possible to completely and accurately represent relevant domain semantics: “a conceptual schema is the definition of the general domain knowledge that the information system needs to perform its functions; therefore, the conceptual schema must include all the required knowledge” (Olivé 2007). The concept of “accurate and complete” specification has been the cornerstone of conceptual modeling since its inception (Parnas 1972) and persists to this day (Clarke et al. 2012; Clarke et al. 2013). As abstraction does not represent the actual objects, but rather their generalizations, achieving consensus (among users who provide data, decision makers, developers) on what aspects to represent and which to ignore is critical to this mode of representation. Abstraction fails to express the individuality of instances, making it critical to decide in advance the purposes for which data are to be used. These assumptions align with development in organizational settings with existing hierarchies, norms and legal structures.

Instance-Based Conceptual Modeling Grammar

resulting grammar should represent particular instances via attributes as perceived by particular users at certain moments in time. In contrast to representation by abstraction, the result is representational uniqueness each representation of the same instance may be different (i.e., expressed using different attributes and classes), including representations by the same user at different times.1 A consequence of representational uniqueness is that capturing class-based abstractions a priori no longer becomes necessary. This deviates fundamentally from traditional conceptual modeling that guides analysis toward discovery and representation of domain-specific class-based abstractions that capture commonalities among instances. Beyond these initial steps, however, little is understood about instance-based conceptual modeling and no formal notation exists. In the next section, we discuss potential uses of instance-based representations that motivate the value of developing a formal instance-based conceptual modeling grammar. Motivating Grammar

Instance-based

Conceptual

Modeling

At the same time, challenges and limitations of this traditional assumption have been widely recognized. One challenge is effectively engaging subject-matter experts to identify and record all relevant information as accurately and completely as possible (Appan and Browne 2010; Appan and Browne 2012). Wand and Wang (Wand and Wang 1996) note inherent limitations of traditional modeling in capturing unanticipated information. The notion of “complete and correct set of requirements” that “sweeps away the multiple perspectives and ambiguities of organizational life” has been criticized (Walsham 1993). The challenges of view integration arising from having to collapse different perspectives have been explored (Parsons 2003). Parsons and Wand (Parsons and Wand 2000) examined the negative consequences of inherent classification (a major form of abstraction) on conceptual modeling and database operations. Samuel (Samuel 2012) argues that abstraction-driven grammars impose cognitive effort by forcing users to identify instances that fit the predefined abstractions. Lukyanenko et al. (2014) provide evidence that abstraction-based grammars result in lower quality of stored information in settings with high view heterogeneity (e.g., in social media, crowdsourcing).

We now turn to the theoretical and practical case for instance-based conceptual modeling grammars. We ground the case for focusing on instances in ontological and cognitive foundations. We then illustrate the kinds of applications for which focusing on instances instead of (or in addition to) classes can be useful.

In contrast to abstraction, instance-based representations are new and poorly understood. Original work on instance-based information modeling was undertaken by Parsons and Wand (1996; 2000) but focused on the logical representation, resulting in an instance-based data model. Lukyanenko and Parsons (2012; 2014) applied instance-based data model to citizen science crowdsourcing. Their research argues that instance-based conceptual modeling grammars should emphasize things (individually identifiable), rather than classes and the

As Bunge’s ontology is primarily focused on the material world, some researchers turned to ontologies with

Theoretical Rationale for Instance-based Conceptual Modeling Grammars Ontological foundations. Arguments in conceptual modeling research for instance-based representations (Parsons 1996; Parsons and Wand 2000; Lukyanenko et al. 2014) frequently draw on Bunge’s ontology (Bunge 1977; Wand and Weber 1990) in which material and social instances (e.g., specific planets, birds, trees) are the primary constituents of reality. According to Bunge, every instance possesses properties. Properties are always attached to instances and cannot exist without them: materiality of properties directly derives from materiality of things. Every instance is unique in some way as different instances fail to share some of their properties. People use classes to group instances with common attributes (properties accessible to human perception and imagination). In Bunge’s view, instances are observerindependent ontological primitives.

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At the same time, representational uniqueness does not imply that every stored representation be unique, as two different users may independently provide the same set of attributes and classes for the same or even a different instance.

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prominent focus on social reality such as social ontology of Searle (Wyssusek 2006; March and Allen 2012). Consistent with Bunge, Searle recognizes the importance of instances, but notes that some are “social objects” (e.g., European Union, specific bank account) in that they are constructed by humans and are thus observer-dependent (Searle 1995) (for comparison of the two ontologies, see, (Mattessich 2013)). Searle notes that while the world is made of physical particles (e.g., protons, neutrinos) and forces (e.g., electromagnetic force, gravity) that act on them, the level that is naturally accessible to humans is that of mid-sized objects (i.e., Bunge’s material instances) that humans use as prototypes (metaphors) for reasoning and thinking about social reality (Searle 1983; Searle 1995). This level is known as the “middle world” in evolutionary biology - the world that humans readily understand because of the millennia of alignment between mid-sized objects and human biology through natural selection (Dawkins 2000; Shermer 2009). Thus, Bunge and Searle’s both consider instances (objects) as fundamental blocks of social and material world. Cognitive foundations. The fundamental role of instances is found in psychology. While people experience a continuous sensory input (e.g., light falling on retina, sound waves) they transform it into discrete representations (Harnad 1990). Instances are also believed to be units of attention: humans perceive sensory fields (e.g., visual space) to be made of discriminable objects and an undifferentiated perceptual background (Kahneman 1992; Carey 2009). Ultimately, classes (concepts, categories) are formed based on generalizations made about similarly of instances (Rosch 1978). An influential exemplar models of concept formation in psychology makes an even stronger claim that categories are in fact collections of stored exemplars (instances) (Nosofsky 1986; Falkowski and Feret 1990). A review of philosophy and psychology offers a compelling and consistent argument for fundamental role of instances in the organization of reality and human interaction with it. Following these theories one might conclude that neglecting instances when representing domains is contrary both to the way the world is, as well as the way humans are used to thinking about reality. Requirements of Emerging IS Applications Class-based conceptual modeling assumes the abstractions needed for a domain are relatively welldefined and stable. This assumption may not be problematic for traditional IS applications within the boundaries of an organization, where legal requirements, organizational norms, and business processes impose structure and discipline on information requirements needed for standardized organizational activities. Indeed, the regularity and predictability of well-defined, stable classes is consistent with a view of the organization in which repeatable activities involving agreed-on classes of entities form the basis of transactions that need to be recorded.

Instance-Based Conceptual Modeling Grammar

While such applications remain critical to organizational success, we believe an emergent class of applications based on connecting the organization with its external environment will play an increasingly important role in the future, as the desire to monitor and respond to changes in the environment grows. For example, increasingly a new breed of open IS has emerged, systems for which at least some users are permanently located outside the organizational context in which the systems are created and maintained. Parsons and Wand (2014) termed these settings open information environments (OIEs) in which organizations “have access to sources over which they may have no control; new sources of data may emerge; applications of data might change radically over time; and new uses of data might emerge” (p. 2). A major source of information in OIEs is user-generated content (UGC) where the general public produces information in the form of tags, tweets, product reviews, forum posts that organizations can leverage in its decision making, operations and analysis (Susarla et al. 2012; Brabham 2013; Levina and Arriaga 2014; Lukyanenko and Parsons 2015). UGC is growing at an unprecedented rate, resulting in an overall larger amount of UGC than information produced within organizations (Vallente 2014). UGC dominates the Internet: six of the ten most popular websites (e.g., Facebook, Twitter, Wikipedia) produce large quantities of UGC (Brynjolfsson and McAfee 2014). In such cases, the possibility of determining in advance the relevant abstractions is negligible. Moreover, organizations cannot control the external environment. What is relevant will change. In this setting, the requirement to model information needs in terms of a fixed, class-based conceptual schema impedes an organization’s ability to acquire external information and use it effectively. Discussion and Conclusion Despite the long history of conceptual modeling research, the many conceptual modeling grammars that have been proposed and adopted in practice contain an overwhelming bias toward representation by abstraction. In this paper we raised the possibility of having a more balanced kind of representation that accounts for both abstraction and instantiation. We considered theoretical foundations in philosophy and psychology that uphold the central if not primary role of instances as building blocks of reality and human cognition. Instance-based representation further promises to improve user-developer communication and domain understanding based on conceptual modeling scripts. This kind of grammar is also quite timely. The rapid growth of flexible database technologies (i.e., noSQL databases) and flexible approaches to IS development (e.g., personalization) appear to be less consistent with the traditional abstraction-based conceptual modeling and seem to be more consistent with the representational uniqueness paradigm promised by an instance-based representation. Finally, the rise of OIEs motivates the development of an instance-based grammar to support inherently

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heterogeneous and variable user input and avoid potentially negative consequences of abstraction-based modeling. Following these arguments, we hope to begin addressing the inherent limitations of abstraction-based modeling through the introduction of formalisms that capture concrete and individual instances. We thus identify developing an instance-based conceptual modeling grammar as an important direction for conceptual modeling research. Before an instance-based grammar can be developed, there are a number of fundamental questions that need to be addressed. An open question is whether an instancebased grammar should focus solely representation of instances or should also represent classes in a hybrid fashion. This appears to reflect many real-world domains well, as within each domain there are some relatively fixed, stable and agreed-upon classes, attributes and relationships; hence making these domain aspects more amenable to abstraction-driven modeling. For example legal, security and reporting norms follow fixed convention rather than left open to judgment of individual users. Similarly, a requirement to exchange data with legacy systems may suggest pre-specifying some structures in advance. Finally, classes afford domain understanding and representation that would be challenging to represent using instances alone. Classification allows humans to abstract from differences among instances, thereby gaining cognitive economy and ability to infer unobservable properties of things (Rosch 1978; Parsons and Wand 2008). For example, by stating something is a bird speakers can save the effort to communicate attributes they assume are true of birds (e.g., has heart, has feathers, probably can fly). Using classes improves communication efficiency and lessens the effort of having to provide an exhaustive list of attributes per instance. Classes are also intuitive when reasoning about instances. It is unnatural for users to refer to instance x in terms of its attributes alone. It is likely that users refer to x using some class (e.g., dog, employee, bank, account). Finally, knowing what classes users assign to instances reveals any biases in the kinds of attributes users attach to instances. The classes known to a person influence human perception, as illustrated by stereotype effects (Jussim et al. 1995) and categorical perception (Harnad 2005); knowing the classes users attach to instances, therefore, illuminates gaps and biases in the provided attributes. In summary, classes become a convenient and natural mechanism by which users can reason about instances and describe their properties of interest. They also help to understand the attributes provided. Finally, when given freedom to classify in an open-ended manner, non-expert users tend to provide classes (generally generic, "basic" classes) with high accuracy (Lukyanenko et al. 2014). Therefore it appears that integration of the two modes of representation should yield a more balanced and natural grammar. This, for course, raises questions about how to

Instance-Based Conceptual Modeling Grammar

integrate instance-based with traditional abstractiondriven modeling (Lukyanenko and Parsons 2013a). At the same time, we do not wish to definitively argue that instance-based grammar is necessary. Much of IS development has been conducted using abstraction-based grammars and some even eschewed formal modeling (Recker 2015). Likewise, even though OIEs are proliferating and increasingly play a central role in organizations, alternative approaches to developing these systems exist. Thus, Lukyanenko and Parsons proposed (Lukyanenko and Parsons 2013a) and implemented (Lukyanenko and Parsons 2013b) a “no conceptual modeling” approach in the context in an OIE project where developed skipped modeling stage and relied on a flexible data model to capture and store information. Similar “no modeling” appears to be present in noSQLtype of IS development (Kaur and Rani 2013). Another approaches include building simple “agile” schemas, allow for schema expansion and evolution (Chen 2006; Liddle and Embley 2007; Roussopoulos and Karagiannis 2009). Classes used in an abstraction-based model can also be chosen to maximize consensus (e.g., basic-level categories) (McGinnes 2011; Lukyanenko et al. 2014). Thus, while we advocate for the construction of an instance-based grammar, it is important to note that alternatives should also be considered and the resulting grammar should be gauged against these alternatives as well as the traditional abstraction-based modeling. Finally, a fundamental question is how to design the instance-based grammar. Conceptual modeling research and its reference disciplines developed an extensive body of knowledge on advantages and shortcomings of various design choices. In building a new grammar, a challenge is in synthesizing and reconciling prior knowledge and developing effective theory-grounded design solutions. Instance-based engineering offers an opportunity to build a novel grammar that is based on many years of theoretical research. The possibility of an instance-based conceptual modeling grammar opens a new and exciting page in conceptual modeling research and practice. But before this page can be written, there are serious questions that remain to be answered. We thus call upon the community of conceptual modeling research to contribute and support this work. References 1.

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