This document proposes an algorithm to merge business processes from different organizations to facilitate collaboration. The algorithm begins by preprocessing the business processes into directed graph formats. It then identifies the maximum common regions between the graphs and computes added nodes and edges. A transitive reduction is applied to extract the minimum number of edges that maintains reachability. The merged graph is then converted back into a business process model and validated. The algorithm aims to increase compatibility between organizational processes to reduce collaboration costs and effort.

1. Merging business processes for a
common workflow in an
organizational collaborative scenario
Maria Laura SEBU
Horia CIOCÂRLIE
Computer and Software Engineering Department
Politehnica University of Timisoara
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2. Business context
High competition on the market for better products and services →
organizations start collaborations
• Successful collaboration
– Products and operational
– Internal tools
– Market opportunities
– Audience
• Failed collaboration
– Credibility
– Customer satisfaction
– Bad results in portofolio
– Wasted resources
• Root cause - differences in
approaches, methods, processes
and tools used
• Half of outsourcing projects are
doomed to fail !
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3. Business context
• Process-based solution to support multi organizational
collaboration
• Identify if a collaboration is feasible (higher compatibility →
reduced costs and less effort for setup)
– compute the similarity between business processes
• Same domain, standardized processes OR different domains
• Different level of information for business process representation
• Considering a modular design
• Collaborative solution based on merging business processes
• Validation of the solution (in synch with business
objectives)
• Monitor the execution
• Increase the transparency and structuring of cooperation
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4. Business process management
• Business result = output of repetitive actions →
process definition
– Intra-organizational ↓ good compatibility
– Inter-organizational = ∑ intra-organizational business
process
• Process mining - extract process definition
– Data mining on mining event process logs
– Enhance
– Correct
– Improve
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5. Business Process Merge
• Assumption
– Collaborations = ∑ input from all parties →
common acceptance of a process → positive
context → successful outcome
• Preprocessing
– Business process representations
• Event driven Process Chains (EPC)
• Business Process Modeling Notation (BPMN)
– Abstract: reduce to directed graph format
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6. Business Process Merge -
Preprocessing
• BPMN: a function that maps nodes to types
– Activities (BPMN) → Node (Directed graph) Activity:Activity
Type:Activity Name
– Events (BPMN) → Node (Directed graph) Event:Event Type:Event
Name
• Start Event
• End event
• Intermediate event
– Gateway (BPMN) → Node (Directed Graph) Gateway:Gateway
Type:Condition
• Data based exclusive gateway
• Inclusive gateway
• Parallel gateway
• Event-based gateway
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7. Business Process Merge -
Preprocessing
Translate Activity in Node Translate Gateway in Node
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8. Business process merge - Algorithm
• Defined over a pair of directed graphs
• Initialize common graph with the set of maximum common
regions
– Maximum common region = maximum connected sub-graph
composed only of matched nodes and substituted edges
• Compute added nodes (nodes present in one or more of
the input graphs and not included in one of the maximum
common sub-graphs)
• Compute added edges and assign weights
– Weight = number of graphs which contain the edge
• Apply transitive reduction = extract the graph with as few
edges as possible that has the same reachability level as
the original graph
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9. Business process merge - Algorithm
• Match nodes – semantic similarity (SEMILAR
API)
– Wordnet Lesk Tanim (Greedy and Optimum)
• relatedness of two words is equal to the overlaps
counted in the dictionary definition
– Wordnet Lin (Greedy and Optimum)
• ratio between the amount of information describing
the common parts and the information needed to
describe in particular what strings represent
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10. Business Process Merge - Algorithm
Function MergeBusinessProcess (Graph G1, Graph G2)
Begin
Set{Graph} MCR ← MaximumCommonRegion(G1, G2)
Graph CG ← (NCG = {n € MCR}; ECG = {e € ECR})
Set{Node} AddedNodes =
ComputeAddedNodes(G1) ∪ ComputeAddedNodes(G2)
Set{Node} AddedEdges =
ComputeAddedEdges(G1) ∪ ComputeAddedEdges(G2)
Foreach (Node node : AddedNodes)
If {node} ∩ NCG = Ø
NCG = NCG ∪ {node}
Foreach (Edge edge : AddedEdges)
If {edge} ∩ ECG = Ø
ECG = ECG ∪ {edge}
Else
w(edge)++
Graph TR ← ApplyTransitiveReduction(CG)
Return TR
End
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11. Business process merge - Algorithm
Function ApplyTransitiveReduction (Graph MCG)
Begin
Foreach (Edge edge : GetEdges(MCG))
List<DirectedPath> dirPaths =
getListDirectedPath(edge.getNodeStart(),
edge.getNodeEnd());
Foreach (DirectedPath path:directedPaths)
If (weight(path)>weight(edge))
deleteEdge(MCG, edge);break;
End;
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12. Business Process Merge – Post-
processing
• Convert merged directed graph
into a business process
– Map node to business process
element by analyzing the label
• Calculate similarity with input
business process
– Graph comparison algorithms
• Name of the activities composing a
process (labels)
• Topology of the process models
• Validate
– Structural: conformance checking
techniques on the event log
dataset (fitness, precision,
structure)
– Functional: compliance of the
process model with the process
requirements
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13. Business Process Merge Algorithm –
Case Study
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14. Business Process Merge Algorithm –
Case Study
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15. Business Process Merge Algorithm –
Case Study
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16. Business Process Merge Algorithm –
Case Study
Removed Edge Path Coverage
[C, F] [ [C, E], [E, F]] and [ [C, P], [P, F] ]
[D, G] [ [D, C], [C, E] [E, G] ]
[P, R] [ [P, F], [F, M], [M, R] ]
[A, B] [ [A, X], [X, B] ] 16
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17. Related Work
Our approach Other solutions
Semantic evaluation of label content
→ merge business processes from
different domains
→flexibility
Labels are evaluated in a syntactic context
Nodes are compared only with nodes of
the same type (activities, control)
Nodes are compared with any nodes by
computing a context similarity
Risky !
Transitive reduction of common graph Merge control elements (consecutive
splits and joins)
Trivial connectors are removed
Maximum Common Region (MCR)
Compute added nodes and edges
Transitive Reduction
Construct process model such as the sum
of distance between each process and the
generic is minimal
Fully automated
BPMN exemplification
NOT fully automated
Backward tracebility
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18. Conclusions
• Input: set of business process models
• Output: business process collaborative
solution
• Implementation is
– Directed graph based
– Independent of process model representation
(directed graph)
• Business process = ∑ business processes easy
to set up
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19. Thank you for your attention !
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