http://www.springer.com/us/book/9783319250366 ISBN 978-3-319-25037-3

1. Process Analytics
Dr. Amin (Seyed M.) Beheshti
Lecturer and Senior Research Associate
University of New South Wales, Australia
(Service Oriented Computing Group, CSE)
http://www.cse.unsw.edu.au/~sbeheshti/
Fifth Australasian Symposium on Service Research and Innovation (ASSRI'15)
The University of Sydney
19 February 2016

2. Business Process (BP)
A Business Process (BP) is a set of
coordinated tasks and activities, carried
out (manually/automatically) to achieve
a business objective or goal.
In Modern Enterprises, Process Data
is stored across different systems,
applications and services in the
enterprise.
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3. Example: Home Buyer Scenario
Consider a business process scenario in banking context
that spans across multiple systems and services inside
and among third party services providers.
The Modern Enterprises and
the Need for Process Analytics
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4. Smart ATMs:
Deposit Cash and Cheque.
Make payments and Transfer funds.
eMail Banking:
Security alerts & reporting
deposit cheque into account by mail (Virtual Banking)
Social Banking:
Leveraging Social Media to Enhance Customer Engagement.
Cloud-based Services for Banking:
Channel Services (authorization, access control, etc)
Business Services (Retail, Trading, etc)
…
The Modern Enterprises and
the Need for Process Analytics
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5. Smart ATMs:
Deposit Cash and Cheque.
Make payments and Transfer funds.
eMail Banking:
Security alerts & reporting
deposit cheque into account by mail (Virtual Banking)
Social Banking:
Leveraging Social Media to Enhance Customer Engagement.
Cloud-based Services for Banking:
Channel Services (authorization, access control, etc)
Business Services (Retail, Trading, etc)
…
The Modern Enterprises and
the Need for Process Analytics
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6. The Modern Enterprises and
the Need for Process Analytics
Data-Services, can be leveraged to reduce the effort
required to set up a data integration system…
Data-Spaces, aim to manage large number
of diverse interrelated data sources in enterprises…
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7. The Modern Enterprises and
the Need for Process Analytics
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8. The Modern Enterprises and
the Need for Process Analytics
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9. The Modern Enterprises and
the Need for Process Analytics
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10. The Modern Enterprises and
the Need for Process Analytics
ProcessSpaces
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11. The Modern Enterprises and
the Need for Process Analytics
ProcessSpaces
*Motahari-Nezhad et al., “From Business Processes to Process Spaces”. IEEE Internet Computing 15(1): 22-30 (2011)
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12. Process Analytics?
Beheshti et al. “A Query Language for Analyzing Business Processes Execution”. BPM 2011: 281-297
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13. Process Analytics, is the family of methods and tools that can be applied to
process data, instances, and models in order to support decision-making in
organizations by:
• Analyzing the behavior of completed processes and their models,
• Evaluating currently running process instances, and
• Predicting the behavior of process instances in the future.
• Discovering meaningful patterns in process execution data.
Process Analytics?
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Beheshti et al. "Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data", Springer Book, 2016

14. Process Models
(Analyzing, Querying and Matching)
• What are the approaches for matching different dimensions of process
models (data, interface, protocol, etc).
• What are the Process Matching techniques that are useful in a series of
process models analytics tasks?
• Why schema matching techniques are useful for the integration of
process execution data from various systems and service?
• What are the Process similarity measures that can be used for organizing
process models mined from event logs?
• What are the techniques for querying and analyzing process models?
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15. Process Instances
(Analyzing, Querying and Mining)
• What are the querying techniques that can be utilized for analyzing and
understanding the execution patterns of the business processes?
• What are the key concepts, methods and languages for querying business
processes?
• Why it is important to understand the execution of a business processes?
What insight we can get from that?
• What are the techniques to monitor the status of running processes?
• Why it is important to trace the progress of process execution? What are
the techniques to detect deadlocks or unbalanced load on resources?
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16. Process Data
(Analyzing, Querying and Mining)
In today’s knowledge-, service-, and cloud-based economy, businesses
accumulate massive amounts of data from a variety of sources.
Process data, increasingly come to show all the typical properties of ‘big data’:
• Wide physical distribution,
• Diversity of formats,
• Non-standard data models,
• Independently-managed, and
• Heterogeneous semantics.
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Beheshti et al. "Scalable graph-based OLAP analytics over process execution data", DAPD Journal, 2015

17. Process Data
(Analyzing, Querying and Mining)
We are generating vast amount of Process related Data.
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18. Process Data
(Analyzing, Querying and Mining)
We are generating vast amount of Process related Meta-Data.
Actors Who did What? When? Where? Why? How? …
Business Artifacts Who created it? How it evolved over time? …
Beheshti et al. "Enabling the Analysis of Cross-Cutting Aspects in Ad-Hoc Processe", CAiSE 2013: 51-67
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19. Process Data
(Analyzing, Querying and Mining)
Why analysis of process data can help in discovering useful information
and supporting decision making for enterprises? What are the existing tools and
techniques? What are the challenges and future opportunities?
How data-services and data-spaces can facilitate organizing and analyzing
process related data?
How can we support big data analytics over process execution data?
What is process space and process mining? How process analytics can benefit
from them?
What are the Cross-Cutting Aspects (e.g. Provenance and Time) and why they
are important in process analytics?
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20. • What are the open source and commercial softwares for process analytics?
• Do they support scalable analysis techniques?
• What are BPM in the Cloud solutions? How they offer visibility and
management of business processes?
Process Analytics, Tools
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21. • Big Data Analytics for Process Data
• Analyzing Big Process Data Problem
• Organizing Big Process Data
• Indexing, and Querying Big Process Data
• Supporting Big Data Analytics Over Process Execution Data
• Crowdsourcing and social BPM
• Process Data Management in the Cloud
Process Analytics, Future Directions
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22. Process Analytics, Future Directions
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23. Our Book: Process Analytics
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24. Our Book: Process Analytics
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25. Our Book: Process Analytics
Acknowledgement:
The Content discussed in this talk can be found in the Book.
The Book is my joint work with:
• Scientia Prof. Boualem Benatallah (Scientia Professor at UNSW Australia),
• Dr. Sherif Sakr (Senior Researcher at UNSW),
• Dr. Hamid Motahari (Team Leader and Data Scientist at IBM, Silicon Valley),
• Prof. Daniela Grigori (Professor at Université Paris-Dauphine) and et al.
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