“Soft” Trigger Modeling: A Technique for Incorporating Intentionality into Workflow Specifications William L. Kuechler, Jr. University of Texas at San Antonio Division of Accounting and Information Systems
[email protected] 1. Introduction Soft Trigger Modeling (STM) is both a process model and a knowledge explication method by which workflow management systems (WFMS) are reconceptualized from state- or event-based to knowledgebased. When multiple WFMS controlled by autonomous workgroups must interoperate, the utility of event-based WFMS models is limited, since unilateral changes in tasks frequently disrupt coordination between processes. Common business situations exhibiting the problem are outsourcing and virtual corporations. Conventional trigger modeling defines triggers in terms of fixed, predetermined events. STM extends trigger modeling by incorporating common domain knowledge and the intentionality of workgroups into the workflow specification, providing some ability for automated compensation for dynamic changes in task definition. In prior work we have set out the mechanisms by which trigger-based coordination can be automatically maintained following process modification, and the applicability of the Smart Object Model, a logic-object hybrid, for modeling WFMS. The foci of this paper are the knowledge elements of our process model, and an analysis method that populates the model. 2. Problem Definition: scope and assumptions At a technical level the problem is more acute than simply providing for bindings of previously enumerated tasks to functions and goals at run time. Multiple coordinating WFMS constitute an open system, which presents work performers (human or automated) with situations that are in part, truly novel (unknown). Thus the problem includes contextual interpretation of incomplete information, the generation of alternative task sequences based on that interpretation, and finally constrain-based selection from among alternatives, all accomplished at run-time. The problem has many of the aspects of natural language interpretation, and in fact a process grammar is part of our process model.
Vijay Vaishnavi and Candice Patterson Georgia State University Department of Computer Information Systems {vaishna, cpatterson }@gsu.edu The basic tenets of our approach to an adaptive WFMS model are (1) work processes are designed artifacts. They represent methods of fulfilling organizational goals subject to constraints. (2) The design rationale for a process can be reconstructed from contextual analysis of its activities. (3) A set of domain knowledge beliefs underlies the design and is shared by coordinating agents (4) When formally expressed and included in the process definition, design rationale and domain knowledge can be used to reason about changes to the process. In light of the fact that repetitive, operations level workflows constitute the majority of work performed in many organizations, we have scoped our research problem as follows. (1) We address production workflows, which are repetitive and conform to stable patterns when viewed at a level above the details of execution. (2) We address a subcontracting (or client/server) relationship, where except for coordination of processes, the results are equifinal, that is any set of activities that generate output within specification is acceptable. (3) We consider small perturbations to a larger system, rather than radical structural change. 3. The process model There are three key knowledge structures in our process model, which allow intelligent reasoning about partially understood changes in activities or activity sequences: The lexicon: a dictionary of terms assumed to be common across the work domain of the interacting WFMS, and their relationships. The knowledge base (KB): heuristics for scheduling and process interpretation. The KB includes the process grammar: a production rule representation of allowable constructions in the development of alternative activity sequences, an exact analogy to grammar rules for natural and computer languages. The process description (see Figure 1): a tree structure of nodes representing three types of knowledge
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about a process. These are attributes of the process result, that is goals the work is expected to accomplish, generalized functions, which are abstract activities that can be accomplished by a variety of means, and activities, which are mechanism specific instantiations of function. Many conventional process descriptions include only activity information. g1 g2
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Functions f1 Actions
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Figure 1: Knowledge components in the STM work description 4. The knowledge explication method At a high level, STM can be considered the ‘reverse engineering’ of the workflow design rationale, that is, the reconstruction of the reasoning that was followed, tacitly or implicitly in the construction of work process. With reference to Figure 1, the STM modeler begins at the activity level. Generalization operations on activities yield the functional level (WHAT is being done). From the functional level, the modeler asks WHY and proceeds up the attribute hierarchy to the root goal for the entire production process. STM begins with construction of the lexicon (or ontology), the domain knowledge shared by all workgroups. The lexicon serves as a reference dictionary, and all terms used in other knowledge representations must be drawn from this source. To capitalize on an expertise that exists widely in the information systems development community, STM uses linguistic-based domain analysis (LDA). When used specifically for domain knowledge explication, LDA is sometimes termed ontological engineering. Prompted by available information: (1) the set of workflow activities, (2) inputs to and outputs from them (3) the roles that perform them and (4) the resources used by them, extended scenarios of the process are elicited from domain experts. The scenarios are then parsed into entities (nouns) and functional relationships between entities, (verbs and verb phrases). Each entity is then entered into the lexicon. It is common for relationships of the type stored in the lexicon to be encoded as first order logic predicates. In STM, however, this knowledge is represented with ifthen production rules, since rules are more easily
understood and validated by domain experts, as well as being the native knowledge representation of Smart Objects. After lexicon development, analysis of an existing work scenario using generalization and abstraction operators on the concrete elements of the workflow reconstructs the design rationale of the workflow in lexicon terms. Generalized functions (GFs) are established by asking how an activity is performed, what resources or equipment are used, and whether alternatives exist. It is important to elicit alternatives from the domain expert in the most general domain terms, rather than site specific definitions. The relation between GFs and activities is expressed as a set of production rules. These rules, together with the rules describing the relations between attributes and GFs, and the rules describing the relations between attribute nodes at different levels, constitute the process grammar that describes the allowable constructions for a process that enacts the root goal. Each functionality is a design choice for the creation of an attribute of the overall process. The next step in STM captures the rationale implicit in the attribute-tofunction decision, that is, what functions are available in the entire domain to implement this attribute, and preference heuristics for choosing between them. After completion of the work description, a conventional trigger model of the workflow is build. From this document, all triggers that communicate with external WFMS are identified. The final step is to re-conceptualize these triggers as independent, semantically defined communications events rather than derived states. Each trigger becomes an object; triggers and their constraints are semantically described in terms of their significance to the composite WFMS system, using terms from the lexicon. Much of the required knowledge can be determined easily at this point by inspecting the work description goals for the event that constituted the trigger in the original workflow model, i.e. the semantic states of the process that were the preconditions of the communication event. Constraints on temporal relationships within the overall process are also important, that is, the semantics of many triggers are the completion state (% complete) of the overall process, or a sub-process or function. Following semantic definition of triggers, the STM model is complete; as a Smart Object model it is logically executable, capable of inferring reasonable repositioning of coordinating triggers following modification of its activities. An expanded version of this paper is available at http://cobweb.utsa.edu/faculty/kuechler/stm1.html
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