A Notation for Knowledge-Intensive Processes Netto, J. M.
França, J.B.S.; Baião, F.A.; Santoro F.M.
Federal Service of Data Processing (SERPRO – Serviço Federal de Processamento de Dados) Rio de Janeiro, Brazil
[email protected]
Research and Practice Group in Information Technology (NP2Tec), Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Brazil {juliana.franca, fernanda.baiao, flavia.santoro}@uniriotec.br
Abstract— Business process modeling has become essential for managing organizational knowledge artifacts. However, this is not an easy task, especially when it comes to the so-called Knowledge-Intensive Processes (KIPs). A KIP comprises activities based on acquisition, sharing, storage, and (re)use of knowledge, as well as collaboration among participants, so that the amount of value added to the organization depends on process agents’ knowledge. The previously developed Knowledge Intensive Process Ontology (KIPO) structures all the concepts (and relationships among them) to make a KIP explicit. Nevertheless, KIPO does not include a graphical notation, which is crucial for KIP stakeholders to reach a common understanding about it. This paper proposes the Knowledge Intensive Process Notation (KIPN), a notation for building knowledge-intensive processes graphical models. Keywords - Knowledge Intensive Process Representation; Knowledge Intensive Process Ontolog; Process Modeling.
I.
INTRODUCTION
Making business processes explicit empowers competitive advantage for organizations. With the dissemination of Business Process Management (BPM) practices, companies have started to raise the value of process modeling and an entire universe of related assets. According to Schreiber et al. [29], process modeling became essential for the systematization and management of organizational knowledge artifacts. However, this is not an easy task, especially when it comes to the so-called Knowledge-Intensive Processes [16]. A Knowledge-Intensive Process (KIP) involves many subjective and complex concepts, typically tacit for from their stakeholders’ minds. Additionally, a KIP usually comprises activities based on acquisition, sharing, storage, (re)use of knowledge, and collaboration among participants, so that the amount of value added to the organization depends on the knowledge of the process agents. They deal with unpredictable decisions, creativity-oriented tasks, and dynamic execution that evolves based on the experience acquired by the agents. All those issues hinder their representation, and make them subject to different interpretations. Papavassiliou et al [27] argue that the representation of processes associated to the knowledge that they generate has become critical for organizations, since knowledge is a crucial concern for a successful business. Yet, Abecker [2] affirms that the representation of knowledge-intensive business activities is required for reaching an adequate understanding to support business management. Research on KIP points out their
essential characteristics, as in [10][13]. However, it is difficult to find out an approach that addresses all or at least most of these characteristics in the representation of their processes, mainly due to the lack of proper modeling strategies, as discussed in [14][15]. Nevertheless, the Object Management Group (OMG) [25] states that, in addition to underlining the concepts inherent to a domain, a notation enhances the clarity of the models and allows the ability of communicating the concepts uniformly. In this context, the literature discusses how a KIP can be better understood and managed, particularly with regard to its representation. KIP representation approaches include BPKM [26], DECOR [1], CommonKADS [29], KMDL [16], Oliveira [24], Case Management [3][22], Wang and Kumar [30], DCR Graphs [18], MailofMine [7], and Donadel and [9]. However, none of them captures all essential KIP characteristics, as shown in [13][15], and most approaches do not provide special attention to the notation applied in KIP representation. The Knowledge Intensive Process Ontology (KIPO) [13] was developed, from an extensive literature review, with the target to comprise all the elements to make a KIP explicit. KIPO explores the elements belonging to tacit knowledge involved in the process, especially the ones related to business rules, decision-making and collaboration. Nevertheless, KIPO does not include a specific graphical notation for representing processes, requiring the instantiation of the ontology to make this process explicit. Based on the potential of KIPO to portray the essential features of KIP, this paper presents a notation for building KIP graphical models: the Knowledge Intensive Process Notation – KIPN. The paper is organized as follows. Section 2 describes current approaches for knowledge intensive processes representation. Section 3 presents the Knowledge Intensive Process Notation (KIPN) and specifies its diagrams and core symbols. Section 4 discusses methodological issues of this research. Section 5 shows a practical example of modeling using KIPN, and Section 6 concludes this paper. II.
REPRESENTATION APPROACHES FOR KIP
Despite the fact that KIP manipulates a high degree of knowledge that is critical to the business, this knowledge is frequently lost. A KIP has its own characteristics that distinguish them from traditional processes, such as socialization and collaboration, the dynamics and the influence of intentions and experiences of the agents in decision making. Some traditional process modeling graphic approaches, such as
Event Driven Process Chain (EPC) [20] and Business Process Modeling Notation (BPMN) [21][25] have been adapted to allow the representation of the intrinsic elements of knowledge within business processes, but these methods do not include all the required features to describe a KIP [15]. Besides, the literature shows a set of approaches dedicated to highly-intensive knowledge processes representation. Among the existing proposals, we highlight two, whose primary focus is on the graphical notation. The Knowledge Modeling Description Language (KMDL) [16] represents both tacit and explicit knowledge of the process. Thus, the different possibilities of knowledge conversion can be modeled and the flow of knowledge between actors is depicted. The Oliveira’s methodology [24] is an extension of Ericsson et al. [11] for business process modeling that is composed of diagrams representing a hierarchy of models. It uses constructs adapted from KMDL [16] to model business processes, considering Knowledge Management aspects. The evaluation in [15] concluded that current process modeling representation languages are not adequate for the representation of KIPs, since relevant information about the KIP dynamics is lost. Other approaches are also related to KIP, despite their different terminology. According to Van der Aaslt et al. [3], Case Management (or Case Handling) deals with instances (cases) of processes with little or no structure at all, in which non-atomic activities predominantly take place without a predefined order. In those cases, the interaction among agents is not restricted to data and knowledge exchange, but it is supported by collaborative tools. Man [22] points 2 perspectives of Case Handling representation proposals: Communication-based and Artifact-based process control. He states that these approaches (e.g. Van der Aaslt et al. [3] and Wang and Kumar [30]), despite representing a sequence of activities with relative flexibility, do not include human aspects, such as collaboration and decision-making. The definition provided by Hill et al. [19] to an “Artful” process is also very similar to a KIP, in the sense that an artful process is a dynamic process that depends on agents skills and experience in challenging activities, and requires quick complex decisions among many possible alternatives. Di Ciccio et al [8] discuss two proposals for graphically modeling a KIP, named DCR Graphs [17] and MailofMine [7]. While the first attempts to show the implementation state of the activities and their interdependencies to ease the flow, the second intends to model an artful process by mining elements from emails exchanged during the accomplishment of the knowledge workers' activities. It is noteworthy that such approaches represent the flow and performance of activities dynamically, without considering the characteristics related to collaboration in the process. Although the focus of artful process is on agents and their knowledge, the proposals do not represent specific characteristics of these agents that led to the decision making, knowledge building, improvements in activities, and influence of external events. França et al. [13] designed KIPO, a formal ontology comprising the key concepts and relationships involved in the conceptualization of knowledge-intensive processes. KIPO aims at providing a common, domain-independent
understanding of KIPs and, as such, it may be used as a metamodel for a KIP representation language. The first column of Table 1 lists some of the generic concepts from KIPO. It includes tacit elements linked to the process, such as Belief, Desire, Intention and Perception, which have a role in building the foundation of intentions and objectives to be achieved by the process, as discussed in [6][28]. Usually this type of process is guided by agent’s intentions in achieving the process objective. Thus, the representation of these concepts allows the tracking of what motivated the decision makings and the outcomes achieved through the process execution. KIPO also addresses collaborative aspects, which are essential due to the high degree of tacit knowledge exchanged among agents and to the frequent process evolution along time. The loss of this information decreases the awareness of when and how a collective action is performed, thus compromising a common understanding and collaboration among agents. Our evaluation of the proposals and notations from the literature showed that they do not represent all the characteristics and dimensions proposed in KIPO. However, KIPO does not provide a graphical notation. So far, Moody [23] argues that visual notations are effective because they provide powerful resources for the human visual system and are transmitted in a more concise and precise manner than ordinary text-based language. Although KIPO does not address the problem of representing KIP graphically, it opens a way to explore the potential of a visual notation for KIPs proposal. We address this potential by proposing KIPN – A Knowledge Intensive Process Notation. III.
KIPN – A KIP NOTATION
This section presents KIPN, a graphical notation that promotes the cognitively-effective understanding of a KIP. KIPN covers all characteristics defined by KIPO. According to Moody [23], the "anatomy" of a visual notation is composed by: a set of graphical symbols, forming the graphic vocabulary; a set of compositional rules, forming the visual grammar; and the semantic concepts, forming the visual semantics. In our proposal, the visual semantics is defined by KIPO. The visual syntax (the graphic vocabulary and rules of the visual grammar) comprises a set of diagrams that are related to each other in order to represent the main perspectives within a KIP. Since a KIP is a specific subtype of a business process, our proposed set of diagrams work together as a breakdown of the components of a process diagram. Therefore, some existing approaches were reused from literature to represent certain aspects of the KIP. The diagrams that represent the KIP dimensions are described as follows, and Table 1 shows the symbols proposed for all the diagrams in KIPN. A. KIP Diagram The first and main KIPN diagram is the KIP Diagram. It depicts a comprehensive overview of the processes and activities using BPMN-based elements. However, different from a standard BPMN process representation, the control-flow through the activities is not determined; rather, there is no predefined order of execution for the activities.
TABLE I.
KIPN SYMBOLS
Association
Message Flow
Question
Desire
External Agent
Criterion
Knowledge-Intensive Business Activity
Experience
Assertion
Evidence
Decision
Speciality
Business Rule
Goal
Risk
Feeling
Mental Image
Choosen Alternative
Fact
Message
Data Object
Advantage
Discarded Alternative
Informal Exchange
Belief
Disadvantage
Contingency
Impact Agent
Innovation Agent
Intention
Innovation
It is possible to indicate constrains (a.k.a. business rules) under which the flow is subject to. Decisions, innovations and contingent events related to an activity may be also represented. Moreover, processes can be modeled in different levels of abstraction, which means building a hierarchy of models. In this approach, a model in one level abstracts details from the models in the next level anytime they get too complex to be understood (due to the number of activities or to the relationships with other processes/sub-processes, for example). B. Socialization Diagram The Socialization diagram is the heart of the KIP, where swimlanes highlight how communication happens between the agents, the messages exchanged, acquisition and sharing of knowledge within knowledge-intensive activities. This diagram shows the socialization and contingency events that influence the elements produced or handled and decisions made. Knowledge structures may result from the socialization among agents. These structures can be very formal in the case of Data Object and Assertion, or informal in the case of a Mental Image. To highlight these interactions between agents, the swimlanes represent the Socialization activities , including the informal knowledge exchanges, innovations proposals, external events firing (contingencies) and decision activities. In particular, decision activities are represented only when there was a judgment at a certain point of the process. The decision itself made from this judgment is detailed in the Decision Diagram. C. Decision Diagram The Decision Diagram aims at detailing the decisionmaking that occur during the KIP, with its corresponding result. The elements involved are facts and evidences about
issues related to the domain, the proposed alternatives and their criteria, advantages and disadvantages, risks and restrictions, and all those that add information to the representation of contributions or socialization activities for decision making. We may note how a decision or a business rule influences the process, as well as the experience applied and expertise produced while solving a problem. The Decision Diagram follows the Mind Mapping notation, focusing on the Chosen Alternative. Mind Mapping [5] is used to represent semantic or other connections among portions of learned material in a creative and seamless manner [10], making it easier to explicit decision making processes. The aspects that influence a decision-making process are associated in a radial way to the centered icon of the chosen alternative, giving an objective and concise view about the process. D. Agent Diagram The socialization and decision activities ultimately promote new experiences, and even expertise gain to agents. To map the expertise and experience of those agents, we suggest the Agent Diagram. An (generic) agent keeps previous experiences on its work; and an innovation agent also has specific expertise that contributes to innovation and decision-making. In this diagram, it is possible to associate the expertise and experience with their corresponding agent in order to illustrate a competence matrix that maps the skills of those involved. E. Goal Diagram The intrinsic characteristics of agents that mainly influence the activities and goals of the process are shown in the Goal Diagram. The desires, feelings and beliefs that motivate an agent to participate in an activity, as well as a decision or socialization are represented. In this diagram, we adopt the principles of goal mapping proposed by the i * approach [31]. The SR - Strategic Intentions Model - represents interests and concerns through modeling the behaviors of actors [31]. Agents’ intentions are represented as soft goals and resources are the elements that assist the agent (actor) to achieve the proposed objectives (goals), e.g., decisions, expertise and experience. Personal characteristics (desire, belief and feeling) contribute to the intention. IV.
METHODOLOGICAL ASPECTS FOR DEVELOPING KIPN
The objective of this proposal is to represent the concepts defined by KIPO in a clear, intuitive and easy to understand manner, so that the resulting graphical model of the process is precisely understood by users of the domain. These requirements suggest the concept of Cognitive Effectiveness, defined by Moody [23] as the speed, ease and accuracy with which a representation can be processed by the human mind. Moody [23] defined nine principles that should be followed when proposing a graphical notation for conceptual modeling. These principles supported the creation of KIPN, since the proposed representation currently does not intend to control execution flow-level data from a system, but rather represent a conceptual definition of a KIP. He suggests the principle of Cognitive Integration on the integration of multiple diagrams to
represent the conceptual model. We applied this principle in KIPN by reusing existing approaches for representing each of the KIP perspectives (collaboration, decisions, and goals). We adopted KIPO as a common conceptual metamodel for all diagrams, and integrated approaches with similar concepts, reusing their grammars and proposing graphic symbols to represent their concepts. The socialization diagram is kind of a summary diagram, which provides a view of the process as a whole. The other aspects can be instantiated from this diagram, as the decisions and intentions to achieve the proposed goals, which are represented through reused approaches. But, according to Figl et al [12], the perception of the cognitive effectiveness of a modeling language may influence how users perceive a language as useful and become interested in using it. The authors highlight that this kind of users’ decision is highly relevant in fields where there is no de-facto standard modeling language, such as in KIP. To evaluate the impact of the cognitive effectiveness of several visual conceptual modeling languages, they applied the principles defined by Moody [23] and concluded that four of these principles influence on the perceived usefulness of modeling languages: (i) Perceptual Discriminability: the easy discrimination between different visual symbols; (ii) Graphic Economy: the balance between high expressiveness and limited number of symbols; (iii) Semiotic Clarity: the absence of construct deficit; and (iv) Dual Coding: balanced combination of textual and symbolic representations. The proposal of KIPN, therefore, followed the criteria proposed by Figl et al [12], so as to pursue a high cognitive effectiveness in users' perception. With regard to the Perceptual Discriminability principle, we adopted more than one variable to differentiate symbols, such as color, shape and texture, which are some of the variables suggested for this purpose [23 apud 4]. We also tried to choose icons that are quite similar to the concepts they represent, in order to help differentiate symbols and increase their cognitive effectiveness. Graphic Economy limits the number of symbols in a notation to a number that can be managed by its user, and with no redundant symbol for the same concept. It suggests that some concepts are chosen not to be graphed. Moody [23] points out that the limitation of the graphical representation of semantic concepts should be done when the goal is to reduce the complexity. Semiotic Clarity principle, on the other hand, suggests a 1:1 match of semantic concepts for graphic symbols. Graphic Economy and Semiotic Clarity principles may be conflicting in some scenarios, since the first may lead to the deficit symbols anomaly, which decreases Semiotic Clarity. We addressed Graphic Economy by restricting the graphic symbols in KIPN only to the most specific concepts from KIPO. Therefore, superclasses (such as Agent, Collaborative Session, Communicative Interaction, Alternative, Knowledge Structure and Business Rule) are not graphically represented in KIPN. Notice that this decision does not impact on the Semiotic Clarity of the resulting model, since such concepts are never instantiated. Additionally, we argue that Receiver and Sender roles may be inferred during a message exchange, and chose not to represent them with a graphical symbol. Rather, senders and receivers may be distinguished in a KIPN-based model through the agents’ representation and direction arrows.
The Dual Coding principle suggests the use of textual features to assist in understanding the diagrams. Note that they should not replace or act as symbols and differentiation between them, since they are not effective in these cases [23]. In KIPN, comments are associated to the model and symbols to show their descriptions. V.
A PRACTICAL EXAMPLE SCENARIO
This section presents a practical example of modeling a KIP using KIPN. The scenario is the Data Management process in a real software development company. When a system is going to be updated or created, the Data Management area has to analyze the specification and integrate new data requirements to the corporate data model. The process is highly dynamic and some of its activities are very dependent on the participants experience, expertise and creativity. This process was previously used to evaluate existing KIP notations in [15]. Hence, we decided to work in the same scenario so as to enable a comparison of our proposal to the results already obtained, with regard to the quantity of available information in diagrams. The process starts when the manager of the data modeling department receives a request of a data model for a system or module. Then, the manager allocates a Data Analyst (DA) for the activity and he/she starts the interaction with the client area. DA verifies information and data sources and analyzes the current corporate data model. If needed, the DA negotiates with other systems/business data modelers to perform integration, which may require additional meetings with stakeholders to assess how the integration will take place, that is, which objects should be integrated, reused or created. When the integration specification is finished, the Business Analyst, Project Leader, DA and Database Analyst conduct an internal evaluation. If there are restrictions or corrections to be made, the document keeps being refined until it correctly addresses the original request. A validation meeting is conducted with stakeholders and, finally, the entire process is documented following standards for analysis of its performance indicators. The information that supports the process is implicit to participants. Some documents (such as current legislation or performance indicators definitions and reports) are used to support technical decisions. However, the data conceptual modeling task (which comprises understanding and specifying the concepts and relationships that are required by the system, and integrating this specification into the corporate business model) depends fundamentally on the analyst experience. Thus, capturing how the concepts are abstracted and contextualized in the model is relevant to the management, support and improvement of this process. One reason for that is the high turnover of stakeholders responsible for the system, which generates various misinterpretations about the same subject. The interaction between these participants promotes, for example, the exchange of experiences and opinions, resulting in more knowledgeable decisions, besides a high quality model, which is the final product of this process The first KIPN diagram generated is the KIP Diagram (Fig. 1) showing the process activities (“Allocate Data Analyst”, “Attend meeting specification” and “Verify documentation”,
among others). The dependencies of business rules, decisions and other elements, such as the activity conclusion, are made explicit so as to clarify the conditions of the activities flow.
Figure 4. Decision Diagram of Data Management process.
Figure 1. KIP diagram of Data Management process.
The second diagram (Fig. 2) details two socialization activities from the KIP Diagram. “Allocate Data Analyst” is a socialization between Department Manager and Data Analyst. There is a decision activity that is detailed in a Decision Diagram. “Attend Specification Meeting” is a meeting to define the data model specification, when the Project Manager and the Stakeholder informally exchange knowledge about the project schedule. The end of this meeting fires a ‘contingency event’ that influences the scope of the specification. There is no previously specified flow of activities.
The Goal Diagram (Fig. 5) shows interests and concerns of the manager when allocating a Data Analyst. It represents issues that are personal to an agent during the execution of a KIP instance. Since it is difficult to predict feelings or desires, the Goal Diagram should not be taken as a “model”, or template, to be followed in future executions of the KIP but, rather, as a documentation of the feelings and desires of this agent at the moment of the execution.
Figure 5. Goal Diagram of Data Management process.
VI.
Figure 2. Socialization Diagram of Data Management process.
The Agent Diagram (Fig. 3) is represented by a competency map showing all experiences and specialties of the Innovation and Impact Agents that are relevant to the process. The Decision Diagram shows the information involved in the decision activity, as showed in Fig. 4. This example depicts a simple decision about which Data Analyst to allocate in the project. All pieces of information that have to be considered to make the decision (such as the expertise of the agents, advantages/disadvantages of the alternatives) are represented, and graphically associated to the central subject– in this case, the choice of the Analyst responsible for the project.
Figure 3. Agent Diagram of Data Management process.
CONCLUSIONS
Following the evolution of acquisition and information processing, this paper presented KIPN, a notation for modeling knowledge-intensive processes. This notation represents concepts defined in knowledge-intensive processes ontology (KIPO). The KIPN proposes the integration of different approaches to represent concepts of one single semantic metamodel. This is perhaps the biggest challenge of our proposal, making the resulting model an "organized mix of concepts" that can be understood and managed by domain experts. The cognitive aspect was assured by applying principles that strive for the effectiveness of "communication" among process stakeholders, including business analysts, internal and external participants. As the sequence of activities is influenced by several factors, we represented activities in an independent-flow manner, adding restricting elements when specifically required. In KIPN, activities are detailed through socializations. The agents interact and collaborate, contributing to the creation and acquisition of knowledge. The notation is able represent tacit knowledge through informal exchange and mental image elements, but it still does not capture explicitly the knowledge conversion. The relevance of this issue should be further discussed and addressed by creating a diagram reusing elements of KMDL approach [16]. Agents’ contribution is represented by innovation, intention, belief, desire, feeling, experience and mental image
elements. These elements will probably work as documentation of particular instances of the process, because they are difficult to be predicted and then modeled. However, their importance lies in adding important explications to enable the understanding of how and why a situation occurred. This raises an issue about when, and in which abstraction level, a KIP model should be developed. It is important to open a discussion about to what extent is it possible to instantiate and manage an element that is not defined in the model of the process, but might exist in its instances. The rules represented in the decision diagram should be detailed and analyzed in business rule diagram. Besides the KIPO provides the concepts for that, this aspect was revealed as very important especially for understanding the constraints on the process modeled. KIPN diagrams suggest a reflection on the relevance of information that was "hidden" within the process activities, events and actors, among others. The models built for the application scenario evidenced the significance of this information, and showed that it is possible to detail aspects that were not considered by conventional notations. Future work includes extending KIPN with business rules details, and the conduction of a case study. Finally, the development of a modeling tool that supports KIPN, as well as a methodology for mapping, analyzing and representing KIP, is required. REFERENCES [1]
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