Data Perspectives of Workflow Schema Evolution : Cases of Task Deletion and Insertion

Dynamic changes in the business environment requires their business process to be up-to-date. The Workflow Management Systems supporting these business processes need to adapt to these changes rapidly. The Work Flow Management Systems however lacks the ability to dynamically propagate the process changes to their process model schemas (Workflow templates). The literature on workflow schema evolution emphasizes the impact of changes in control flow with very ittle attention to other aspects of a workflow schema. This thesis studies the data aspect (data flow and data model) of workflow schema during its evolution. Workflow schema changes can lead to inconsistencies between the underlying database model and the workflow. A rather straight forward approach to the problem would be to abandon the existing database model and start afresh. However this introduces data persistence issues. Also there could be significant system downtimes involved in the process of migrating data from the old database model to the current one. In this research we develop an approach to address this problem. The business changes demand various types of control flow changes to its business process model (workflow schema). The control flow changes include task insertion, deletion, swapping, movement, replacement, extraction, in-lining, Parallelizing etc. Many of the control flow changes to the workflow can be made by using the combination of a simple task insertion and deletion, while some like embedding task in loop/ conditional branch and Parallelizing tasks also requires the addition/removal of control dependency between the tasks. Since many of the control flow change patterns involves task insertion and deletion at its core, in this thesis we study its impact on the underlying data model. We propose algorithms to dynamically handle the changes in the underlying relational database schema. First we identify the basic change patterns that can be implemented using atomic task insertion and deletions. Then we characterize these basic pattern in terms of their data flow anomalies (Missing, Redundant, Conflicting data) that they can generate. The Data schema compliance criteria are developed to identify the data changes: (i) that makes the underlying database schema inconsistent with the modified workflow and (ii) generating the aforementioned data anomalies. The Data schema compliance criteria characterizes the change patterns in terms of its ability to work with the current relational data model. The Data schema compliance criteria show various properties required of the modified workflow to be consistent with the underlying database model. The data of any workflow instance conforming to Data schema compliance criteria can be directly accommodated in the database model. The data anomalies (of task insertion and deletion) identified using DSC are handled dynamically using respective Data adaptation algorithms. The algorithm uses the functional dependency constraints in the relational database model to adapt/handle these data anomalies. Such handled data (changes) that conform to DSC can be directly accommodated in the underlying database schema. Hence with this approach the workflow can be modified (using task insertion and deletion) and their data changes can be implemented on-the-fly using the Data adaptation algorithms. In this research the same old data model is evolved without abandoning it even after the modification of the workflow schema. This maintains the old data persistence in the existing database schema. Detailed implementation procedures to deploy the Data adaptation algorithms are presented with illustrative examples