CHAPTER 6. ILLUSTRATIVE EXAMPLE
6.3. D ETECTION OF R EDUNDANT W RITE A NOMALIES
Table 6.4 shows the steps to calculate the set of unused artifacts for every activity.
Table 6.4. Steps to Calculate the Unused Artifacts for Every Activity.
ps, ls1 NCu=
{
d ,d1 13}
, NCc= ∅t1 1
NCu=
{
d ,d , d , d , d , d , d13( ) ( ) ( ) ( ) ( )
2 3 4 5 10}
, NCc= ∅t2 13 2
NCu=
{
d , d , d3 , d4 ,d ,d5 10}
, NCc = ∅le1,
t11 NCu=
{
d ,d , d13 5( )
18}
, NCc={
d , d6 9}
xj3
{
13 5} {
6 17 18}
u c
NC = d ,d , NC = d ,d ,d
aj3 NCu=
{
d ,d , NC13 5}
c={
d ,d ,d6 17 18}
xj1,xs4
{
13 5}
6 17 18u c
NC = d ,d , NC =
{
d ,d , d}
t12 NCu=
{
d ,d , d13 5( )
20}
, NCc ={
d ,d6 17}
t13 NCu=
{
d ,d , d13 5( )
19}
, NCc ={
d ,d6 17}
xj4 NCu=
{
d ,d , NC13 5}
c={
d ,d ,d ,d6 17 20 19}
t14
{
13 5}
6 17 20u c
NC = d ,d , NC =
{
d ,d , d , d19}
pe
{
13 5} {
6 17}
u c
NC = d ,d , NC = d ,d
After visiting Process End vertex, the redundant update anomalies are detected as follows:
Explicit Redundant Update
{
13 5} { } {
21 13 5}
u
EC NC= \Ow= d ,d \ d = d ,d is not empty and thus, a redundant update anomaly occurs for every artifact d EC∈ due to Completely Unused for the Process.
Potential Redundant Update
{
6 17} { } {
21 6 17}
c
CC NC= \Ow= d ,d \ d = d ,d is not empty and thus, a redundant update anomaly occurs for every artifact d CC∈ due to Conditional Unused for the Process.
Chapter 7. Comparisons of Data-flow Analysis Approaches
Current workflow modeling and analyzing paradigms are mainly focused on the control-flow and resource dimensions. Literature reports indicate little work in data-flow dimension such as those in conventional programming languages. Sadiq et al. [7] and Sun et al. [8－10] are two groups working on the analysis in data-flow dimension. In this chapter, we compare our work with them.
Regarding the anomalies addressed, Sun et al. [8－10] claimed that seven types of data-flow anomalies proposed by Sadiq et al. [7] can be either represented by their three basic data-flow anomalies or not a problem at the conceptual level. The anomalies in [7] can be found with our model as follows.
A redundant data anomaly occurs when an activity produces an intermediate data output but this data is not required by any succeeding activity. This anomaly is classified as Redundant Write in our approach.
A lost data anomaly occurs when two parallel activities perform non-read operations on an artifact. This anomaly is classified as Conflict Write (Multiple Parallel Production/Updates) in our approach.
A missing data anomaly occurs when an artifact is accessed before it is initialized. This anomaly is classified as Missing Production in our approach.
A mismatched data anomaly occurs when the structure of the data produced by the source is incompatible with the structure required by the activity that uses the artifact. This anomaly can be regarded as the occurrence of both Missing Production and Redundant Write in our approach.
An inconsistent data anomaly occurs when an initial input artifact of a workflow is updated externally during the execution time of the workflow. As stated by Sun et al., this anomaly is not a problem at the conceptual level.
A misdirected data anomaly occurs when a data-flow direction conflicts with the control flow in a workflow schema. This anomaly is classified as Missing Production (Conditional Production) in our approach.
An insufficient data anomaly occurs when data specified are not sufficient to complete an activity successfully. This anomaly results from ill-designed activity and can be classified as Missing Production in our approach at the semantic level. Table 7.1 summaries the comparison of anomalies addressed among Sadiq et al., Sun et al., and our work.
Table 7.1. Comparison of anomalies addressed.
Our approach Sadiq et al. Sun et al.
After Last Write Redundant data
Mismatched data Redundant data No Consumption
After Last Write
Conflict Write
Multiple Parallel Productions
Lost data
Conflicting data Multiple Parallel Updates
N/A
Parallel Read and Update N/A
Sadiq et al. [7] identify and justify the importance of data modeling in overall workflow design process. In addition, data-flow validation issues and essential requirements of data-flow modeling in workflow specifications are identified. They illustrate and define seven potential data-flow anomalies in above table. However, Sadiq’s work is discussed only on the conceptual level and thus, neither concrete data-flow model nor detecting algorithms are proposed.
Furthermore, operations on data are only classified into read and write type.
Sun et al. [8－10] formulate the data-flow perspective by means of dependency analysis. The data-flow matrix and an extension of the unified modeling language (UML) activity diagram are proposed to specify the data flow in a business process. Then, three basic types of data-flow anomalies, missing data, redundant data, and conflicting data, are defined. Based on the dependency analysis, algorithms to data-flow analysis for discovering the data-flow anomalies are presented. However, as Sadiq’s work, no explicit model is proposed to characterize the behaviors of data. Also, read and initial write operation types are considered only.
Our approach presents a process model to describe workflow schemas. The behaviors of an artifact are explicitly modeled by a finite state machine. The operation types including Initialize, Read, Update, and Destroy, are concerned in this dissertation. Table 7.2 summaries the comparisons.
Table 7.2. A summary of comparisons.
Sadiq et al. Sun et al. This Work
Process Model
N/A
(Conceptual Level)
Data-flow matrices Process data diagram
Control flow Diagram
Operations
Concerned Read, Write Read, Write Read, Initialize, Update, Destroy Detecting
Method N/A Data dependency
analysis
Artifact Usage dependency analysis
Concrete
Algorithm N/A Yes Yes
Complexity N/A Ο(n )3
Ο(n)for Redundant Write and Conflict Writes
Ο(n) for missing productions (without destroy operations)
(n )2
Ο for missing productions (with destroy operations)
Chapter 8. Conclusion and Future Work
The main contribution of this dissertation is to introduce an artifact usage analysis technique into workflow design phase. To achieve this goal, this dissertation presents a business process model for describing a business process and analyzes the artifact usages on this model. In our model, the usages of an artifact are characterized by its state transition diagram. Among the usages of artifacts, three types with thirteen cases of improper artifact usage affecting workflow execution are identified and formulated and a set of algorithms to discovering these anomalies is presented. An example is demonstrated to validate the usability of the proposed algorithms.
We are currently continuing our research in several directions. First, we plan to implement the proposed model and algorithms on current workflow management systems, such as Agentflow [38], so that our research result can be tested in real-world applications. Second, we will continue the analysis on composite artifacts with more complex usages using Revise operations. The third is to integrate resource constrains analysis techniques with our work to build a practical workflow design methodology.
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