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Enhancing Flexibility and Managing Data Flow in Process-Aware Information Systems

This chapter discusses the critical need for flexibility within Process-Aware Information Systems (PAIS) and introduces strategies for managing variability in process models. It emphasizes the importance of adapting to unique instances and unexpected events, such as allergies in patient treatment processes. Additionally, it explores data flow inconsistencies, including unnecessary, missing, lost, and mismatched data, and presents repair strategies to ensure reliable execution and data integrity. The chapter also addresses process evolution amidst changing contexts and the challenges of migrating process model instances while preserving functional behavior.

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Enhancing Flexibility and Managing Data Flow in Process-Aware Information Systems

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  1. Enabling Flexibility in Process-Aware Information Systems (PAIS) Manfred Reichert and BarbaraWeber 2012

  2. Chapter 3. Flexibility needs in a PAIS • *Variability • Managing different versions of a process model • Looseness (patient treatment processes only) • A loose specification for process models belong to this category • Process instances are non-repeatable, unpredictable, emergent • Adaptation (execution time) • Plan for Exceptions (an allergic reaction) • Plan Special situations (rare events) • Evolution • (synchronising the process models with the real world processes)

  3. Our Scenario

  4. Chapter 4. Data flow Inconsistencies Unnecessary Data*: A data object is written by an activity but not read by any activity afterwards Missing Data* A data object is read by an activity but it has not been initialise beforehand. Lost Data * A data object is written twice without being read in between

  5. Data flow Inconsistencies (Sadiq, Sherry Sun) • Mismatched Data The structure of the data is incompatible with the structure required by the activity. • Inconsistent Data • Two activities reads an object from two different external sources and write it into two different data bases. • Misdirected Data Data flow direction conflicts control flow direction • Insufficient Data The question of whether the specified data is sufficient for successful completion of an activity

  6. Repair Plan for Data flow Inconsistencies • If missing data situation occurs, the data object of the activity causing the inconsistency must be mapped to the input data set of a preceding activity. • If unnecessary data occurs, the data object which causes the inconsistency needs to be removed from the out put data of the belonging task. • In case of occurring lost data Sadiq et al. provided three options for the process modeller in order to resolve the inconsistency : • Setting one activity for writing the data object causing the inconsistent situation. • Based on the timestamp of the activities, the more recent one can write into the data object. • Changing the inconsistent data object(s).

  7. Chapter 9. Process Evolution and Instance Migration • Changes in • Legal, technical, or business context • Process Model Evolution • Challenge: Migrating process model instances • Process Model Refactoring • Refers to the process of changing a software system in such a way that it doesn’t alter the external behaviour of the program code yet improves its internal structure.

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