The document discusses getting data for process mining. It explains that process mining analyzes data from event logs of software systems or other records to discover, monitor and improve processes. Event logs contain information about cases, the order of events within cases, and attributes of events like activity, time, resource and costs. The document provides examples of standard event log structures and attributes. It also discusses transforming and filtering data from different sources into a format suitable for process mining analysis.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process mining chapter_13_cartography_and_navigationMuhammad Ajmal
This chapter discusses how process mining can be used for cartography and navigation of business processes. It draws parallels between how cartographers map geographical areas and how process mining can map business processes. Process mining can aggregate and abstract processes to highlight important paths and structures. It also allows seamless zooming to different levels of detail. Process mining maps can project dynamic information and support process navigation by recommending next steps based on goals and detected events, similar to how navigation systems guide drivers.
Process mining chapter_06_advanced_process_discovery_techniquesMuhammad Ajmal
The document discusses advanced process discovery techniques. It begins by describing the challenges of process discovery, including the need for models to be able to replay event logs while avoiding overfitting or underfitting the logs. It then provides examples of algorithmic techniques like the heuristic miner and genetic process mining. Region-based process mining is also introduced. The document discusses characteristics of different process discovery algorithms and provides examples to illustrate concepts like heuristic mining, genetic operations, and region-based mining.
This document provides an overview of key concepts in data mining. It discusses how data mining has grown with the increase in digital data and is now a mature discipline. The document outlines different types of data, variables, and learning techniques used in data mining like supervised learning, unsupervised learning, decision trees, clustering, association rule learning, and hidden Markov models. It also compares data mining and process mining, and discusses evaluating the quality of mining results using metrics like confusion matrices and cross-validation.
Object-Centric Processes - from cases to objects and relations… and beyondDirk Fahland
Through this tutorial-style presentation, I want to broaden the uptake of object-centric process mining in research and in practice. It introduces to the concept of object-centric processes, and highlights the core thinking and concepts that underly object-centric processes and explain what makes them effective in analyzing complex real-world processes.
The first part of the talk looks back at key ideas from academic research that led to object-centric process mining.
The second part first explains the basic ideas and techniques of object-centric process mining and the new kinds of process analysis that are enabled by it. We then take a look under the hood of object-centric process mining and look at the key data structures and operations that make it work.
In the third part, we show how these key ideas work for use cases that go far beyond object-centric process mining.
The talk gives pointers to ready-to-use Python libraries and public datasets and tutorials so that you can directly start doing research, development, and analysis in an object-centric approach.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process mining chapter_13_cartography_and_navigationMuhammad Ajmal
This chapter discusses how process mining can be used for cartography and navigation of business processes. It draws parallels between how cartographers map geographical areas and how process mining can map business processes. Process mining can aggregate and abstract processes to highlight important paths and structures. It also allows seamless zooming to different levels of detail. Process mining maps can project dynamic information and support process navigation by recommending next steps based on goals and detected events, similar to how navigation systems guide drivers.
Process mining chapter_06_advanced_process_discovery_techniquesMuhammad Ajmal
The document discusses advanced process discovery techniques. It begins by describing the challenges of process discovery, including the need for models to be able to replay event logs while avoiding overfitting or underfitting the logs. It then provides examples of algorithmic techniques like the heuristic miner and genetic process mining. Region-based process mining is also introduced. The document discusses characteristics of different process discovery algorithms and provides examples to illustrate concepts like heuristic mining, genetic operations, and region-based mining.
This document provides an overview of key concepts in data mining. It discusses how data mining has grown with the increase in digital data and is now a mature discipline. The document outlines different types of data, variables, and learning techniques used in data mining like supervised learning, unsupervised learning, decision trees, clustering, association rule learning, and hidden Markov models. It also compares data mining and process mining, and discusses evaluating the quality of mining results using metrics like confusion matrices and cross-validation.
Object-Centric Processes - from cases to objects and relations… and beyondDirk Fahland
Through this tutorial-style presentation, I want to broaden the uptake of object-centric process mining in research and in practice. It introduces to the concept of object-centric processes, and highlights the core thinking and concepts that underly object-centric processes and explain what makes them effective in analyzing complex real-world processes.
The first part of the talk looks back at key ideas from academic research that led to object-centric process mining.
The second part first explains the basic ideas and techniques of object-centric process mining and the new kinds of process analysis that are enabled by it. We then take a look under the hood of object-centric process mining and look at the key data structures and operations that make it work.
In the third part, we show how these key ideas work for use cases that go far beyond object-centric process mining.
The talk gives pointers to ready-to-use Python libraries and public datasets and tutorials so that you can directly start doing research, development, and analysis in an object-centric approach.
Keynote speech at KES 2022 on "Intelligent Systems for Process Mining". I introduce process mining, discuss why process mining tasks should be approached by using intelligent systems, and show a concrete example of this combination, namely (anticipatory) monitoring of evolving processes against temporal constraints, using techniques from knowledge representation and formal methods (in particular, temporal logics over finite traces and their automata-theoretic characterization).
eBay processes extremely large amounts of data daily, including over 50 terabytes of new data with over 100,000 data elements. Their analytics platform processes over 100 petabytes of data per day with over 5,000 business users. eBay uses several data platforms including SQL, Hadoop, and Singularity to analyze both structured and unstructured data in real-time and handle millions of queries per day. Singularity is used to process semi-structured data more efficiently through functions like normalize_list and xpath that can extract and aggregate metrics from complex event data.
The document provides an overview of process mining and its applications. It introduces process modeling and analysis, data mining, and the three main types of process mining: process discovery, conformance checking, and process enhancement. It also discusses different perspectives that can be analyzed from event logs, including the control-flow, organizational, case, and time perspectives. The starting point for process mining is an event log that can be used to discover process models and check for conformance.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process mining chapter_09_operational_supportMuhammad Ajmal
This document provides an overview of Chapter 9 on Operational Support from the book. Operational support uses event log data from ongoing ("pre-mortem") cases to detect violations, predict outcomes, and recommend suitable next steps based on goals like minimizing costs or time. It works by annotating process models with case data from the event log and using the annotated model to inform predictions and recommendations in real-time.
This document summarizes digital science and the future of online research. It discusses how technology is changing the research workflow by enabling more efficient sharing of ideas, literature reviews, results, materials and data. However, there are still roadblocks to overcome, including specialization of tools, lack of interoperability, and accessibility issues. The key constituencies that must be considered are machines/tools, researchers, and decision makers. While the future of research is digital, adoption remains uneven and cultural shifts are needed to fully realize the benefits of new technologies.
This document provides an introduction and overview of data mining and the data mining process. It discusses different types of data like transactional data, temporal data, spatial data, and unstructured data. It also covers common data mining tasks like classification, clustering, association rule mining and frequent pattern mining. Additionally, it discusses related fields like statistics, machine learning, databases and visualization and how they differ from data mining. Finally, it provides examples of different data mining models and tasks.
This paper proposes an improved framework for analyzing tree-structured business process logs through data mining. The framework consists of four phases: pre-processing of raw process data, extraction of a Document Structure Model (DSM) to transform the tree data into a flat table format, knowledge discovery on the flat data table through techniques like frequent subtree mining, and interpretation of the discovered knowledge by mapping it back to the DSM. The framework aims to address challenges of large and complex business process logs typically stored in XML format by facilitating advanced data mining and knowledge extraction to optimize processes.
Process mining chapter_12_analyzing_spaghetti_processesMuhammad Ajmal
The document discusses analyzing "Spaghetti processes" using process mining. It provides examples of Spaghetti processes from ASML, Philips Healthcare, and an AMC hospital. Spaghetti processes typically have many activities, cases, and individuals involved. Process mining can help analyze such complex processes by discovering models from event logs, checking conformance to reference models, and identifying bottlenecks and deviations. While more difficult than structured processes, analyzing Spaghetti processes can provide significant insights to improve performance and redesign disorganized workflows.
Process mining chapter_11_analyzing_lasagna_processesMuhammad Ajmal
The document describes analyzing a "Lasagna process" of handling requests for household help (WMO process) at a Dutch municipality. It summarizes the key steps and findings of process mining analysis on an event log from the municipality containing 528 cases over approximately one year. It describes discovering a process model from the log, checking conformance, detecting bottlenecks, and identifying groups of resources. The analysis helps understand the process and identify opportunities for improvement.
This document discusses tool support for process mining. It introduces the process mining tool ProM, which supports all the techniques discussed in the book and slides. ProM has a pluggable architecture and the major differences between versions 5.2 and 6 are highlighted. Screenshots of the ProM user interface are provided. Example plug-ins in ProM 6 for the alpha miner and social network analyzer are described. Other process mining tools mentioned include Futura Reflect, which can show process views and social networks, and tools for loading and converting event logs like XESame, Nitro, and ProMimport.
Process mining chapter_08_mining_additional_perspectivesMuhammad Ajmal
This document discusses mining additional perspectives from event logs beyond the initial process model. It describes how event logs contain attributes relating to various perspectives like resources, time, cases, and costs. These additional attributes can be analyzed and visualized using techniques like social network analysis, resource behavior analysis, and decision mining to gain additional insights into the process. Classification techniques can also be applied to the event data to understand reasons for decisions, delays, or non-conformance in the process.
Process mining chapter_07_conformance_checkingMuhammad Ajmal
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Process mining chapter_07_conformance_checkingMuhammad Ajmal
The document summarizes the key aspects and goals of conformance checking in process mining. Conformance checking involves replaying event logs on process models to detect deviations. It can identify problems like missing or remaining tokens, as well as extract timing information. Diagnostics from replay can detect non-conformance at the trace and log levels, and quantify differences to understand problems in detail. Conformance checking is important for auditing, compliance, and aligning systems and processes.
Process mining chapter_05_process_discoveryMuhammad Ajmal
The document discusses process discovery and introduces the α algorithm. It begins by defining key terms like process discovery, fitness, precision, generalization, and simplicity. It then walks through examples of event logs and the corresponding process models that could be discovered from them. The α algorithm is introduced and explained as a basic process discovery technique. Limitations of the α algorithm are also discussed, such as its inability to handle implicit places, loops, and non-local dependencies. The challenges of process discovery are summarized, including noise, incompleteness, and balancing between underfitting and overfitting models.
Process mining chapter_02_process_modeling_and_analysisMuhammad Ajmal
This document provides an overview of process modeling and analysis techniques. It discusses how process models have historically been used for operations management and decision making. However, process models often do not accurately capture reality. Process mining aims to address this by extracting models directly from event logs, allowing users to view processes at different levels of abstraction based on facts rather than an idealized model. It also discusses limitations of model-based analysis when models do not align with reality.
This document introduces process mining and provides an overview of the book "Process Mining: Discovery, Conformance and Enhancement of Business Processes" by Wil van der Aalst. The book is divided into 5 parts covering preliminaries on process modeling and data mining, discovering process models from event logs, conformance checking and additional analysis, applying process mining in practice, and reflections on process mining. The document acknowledges contributors to process mining research and tools, and provides pointers to related works, software, and the IEEE task force on process mining.
This document provides an overview of process mining techniques and how process mining can be used to analyze business processes. It discusses the challenges of process discovery and conformance checking. Process mining aims to bridge the gap between data mining and business process management by using event logs to discover, monitor and improve processes. The document encourages readers to start using process mining today with freely available open-source tools.
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Keynote speech at KES 2022 on "Intelligent Systems for Process Mining". I introduce process mining, discuss why process mining tasks should be approached by using intelligent systems, and show a concrete example of this combination, namely (anticipatory) monitoring of evolving processes against temporal constraints, using techniques from knowledge representation and formal methods (in particular, temporal logics over finite traces and their automata-theoretic characterization).
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2. Overview
Chapter 1
Introduction
Part I: Preliminaries
Chapter 2 Chapter 3
Process Modeling and Data Mining
Analysis
Part II: From Event Logs to Process Models
Chapter 4 Chapter 5 Chapter 6
Getting the Data Process Discovery: An Advanced Process
Introduction Discovery Techniques
Part III: Beyond Process Discovery
Chapter 7 Chapter 8 Chapter 9
Conformance Mining Additional Operational Support
Checking Perspectives
Part IV: Putting Process Mining to Work
Chapter 10 Chapter 11 Chapter 12
Tool Support Analyzing “Lasagna Analyzing “Spaghetti
Processes” Processes”
Part V: Reflection
Chapter 13 Chapter 14
Cartography and Epilogue
Navigation
PAGE 1
3. Goal of process mining
• What really happened in the past?
• Why did it happen?
• What is likely to happen in the future?
• When and why do organizations and people deviate?
• How to control a process better?
• How to redesign a process to improve its
performance?
PAGE 2
4. Getting the data
supports/
“world” business
controls
processes software
people machines system
components
organizations records
events, e.g.,
messages,
specifies transactions,
models
configures etc.
analyzes
implements
analyzes
discovery
(process) event
conformance
model logs
enhancement
PAGE 3
5. From heterogeneous data sources to
process mining results
Extract, Transform,
and Load (ELT)
optional
data
source ELT
data
ELT warehouse
data
source
ELT
data coarse-grained
source scoping
data
source
extract
XES, MXML, or
data similar
source
unfiltered event logs process mining
discovery conformance enhancement
filter
filtered event logs (process) models answers
fine-grained
scoping
PAGE 4
6. Example log
• A process consists of
cases.
• A case consists of
events such that each
event relates to precisely
one case.
• Events within a case are
ordered.
• Events can have
attributes.
• Examples of typical
attribute names are
activity, time, costs, and
resource.
PAGE 5
7. process cases events attributes
activity= register request
Another view 1 35654423
time = 30-12-2010:11.02
resource = Pete
costs = 50
35654424 ...
...
activity= reject request
time = 07-01-2011:14.24
35654427 resource = Pete
costs = 200
activity= register request
time = 30-12-2010:11.32
35654483 resource = Mike
2 costs = 50
35654485 ...
...
activity= pay compensation
time = 08-01-2011:12.05
35654489 resource = Ellen
costs = 200
activity= register request
time = 30-12-2010:11.32
35654521 resource = Pete
3 costs = 50
35654522 ...
...
activity= pay compensation
time = 15-01-2011:10.45
35654533 resource = Ellen
costs = 200
... ... ...PAGE 6
11. Using attributes
social network showing how work
flows from one person to another
Pete Sara Sue
Mike
performance indicators per activity
Ellen Sean
Activity b
Frequency: 456 role Activity g
Waiting time: 15.6 +/- 2.5 hours Frequency: 311
Service time: 1.2 +/- 0.5 hours E Waiting time: 12.4 +/- 2.1 hours
Costs: 412 +/- 55 euros Service time: 0.5 +/- 0.2 hours
b Costs: 198 +/- 35 euros
A
examine
thoroughly
A
g
A M
c1 c3 pay
c compensation
a examine
e
A
start register casually
A decide c5 end
request
h
c2 d c4 M reject
Activity h
check ticket request
Frequency: 407
f
reinitiate Waiting time: 7.4 +/- 1.8 hours
request Service time: 1.1 +/- 0.3 hours
control flow Costs: 209 +/- 38 euros PAGE 10
12. XES (eXtensible Event Stream)
• See www.xes-standard.org.
• Adopted by the IEEE Task Force on Process Mining.
• Predecessor: MXML and SA-MXML.
• The format is supported by tools such as ProM (as of
version 6), Nitro, XESame, and OpenXES.
• ProMimport supports MXML.
PAGE 11
15. extensions
loaded
every trace
has a name
every event has a
name and a transition
start of trace (i.e.
process instance) classifier = name + transition
name of trace
resource
timestamp
name of event
transition (activity name)
PAGE 14
16. end of trace (i.e.
process instance)
start of trace
name of trace
resource
timestamp
name of event (activity name)
data associated to event
PAGE 15
17. Challenges when extracting event logs
• Correlation: Events in an event log are grouped per
case. This simple requirement can be quite challenging
as it requires event correlation, i.e., events need to be
related to each other.
• Timestamps: Events need to be ordered per case.
Typical problems: only dates, different clocks, delayed
logging.
• Snapshots. Cases may have a lifetime extending beyond
the recorded period, e.g., a case was started before the
beginning of the event log.
• Scoping. How to decide which tables to incorporate?
• Granularity: the events in the event log are at a different
level of granularity than the activities relevant for end
users. PAGE 16
20. Order:91245
Order instance
Case id: 91245 Case id: 91245 Case id: 91245
Activity: create order Activity: pay order Activity: complete order
Timestamp: 28-11-2011:08.12 Timestamp: 02-12-2011:13.45 Timestamp: 05-12-2011:11.33
Customer: John Customer: John Customer: John
Amount: 100 Amount: 100 Amount: 100
OrderLine:112345
Case id: 91245 Case id: 91245
Activity: enter order line Activity: secure order line
Timestamp: 28-11-2011:08.13 Timestamp: 28-11-2011:08.55
Orderline OrderLineID: 112345 OrderLineID: 112345
Product: iPhone 4G Product: iPhone 4G
Order OrderLineID : OrderLineID
NofItems: 1
TotalWeight: 0.250
NofItems: 1
TotalWeight: 0.250
1 1..* DellID: 882345 DellID: 882345
OrderID : OrderID OrderID : OrderID
Customer : CustID Product : ProdID OrderLine:112346
Amount : Euro NofItems : PosInt Case id: 91245 Case id: 91245 Case id: 91245
Activity: enter order line Activity: create backorder Activity: secure order line
Created : DateTime TotalWeight : Weight Timestamp: 28-11-2011:08.14 Timestamp: 28-11-2011:08.55 Timestamp: 30-11-2011:09.06
OrderLineID: 112346 OrderLineID: 112346 OrderLineID: 112346
Product: iPod nano Product: iPod nano Product: iPod nano
Paid : DateTime Entered : DateTime NofItems: 2 NofItems: 2 NofItems: 2
TotalWeight: 0.300 TotalWeight: 0.300 TotalWeight: 0.300
DellID: 882346 DellID: 882346 DellID: 882346
Completed : DateTime BackOrdered : DateTime
Secured : DateTime OrderLine:112347
DelID : DelID
Case id: 91245 Case id: 91245
1..* Activity: enter order line
Timestamp: 28-11-2011:08.15
Activity: secure order line
Timestamp: 29-11-2011:10.06
OrderLineID: 112347 OrderLineID: 112347
Product: iPod classic Product: iPod classic
NofItems: 1 NofItems: 1
0..1 TotalWeight: 0.200
DellID: 882345
TotalWeight: 0.200
DellID: 882345
Attempt Delivery
Delivery:882345
0..* 1
DelID : DelID DelID : DelID
When : DateTime DelAddress : Address
Successful : Bool Contact : PhoneNo
Attempt:882345-1 Attempt:882345-2 Attempt:882345-3
Case id: 91245 Case id: 91245 Case id: 91245
Activity: delivery attempt Activity: delivery attempt Activity: delivery attempt
Timestamp: 05-12-2011:08.55 Timestamp: 06-12-2011:09.12 Timestamp: 07-12-2011:08.56
DellID: 882345 DellID: 882345 DellID: 882345
Successful: false Successful: false Successful: true
DelAddress: 5513VJ-22a DelAddress: 5513VJ-22a DelAddress: 5513VJ-22a
Contact: 0497-2553660 Contact: 0497-2553660 Contact: 0497-2553660
Delivery:882346
Attempt:882346-1
Case id: 91245
Activity: delivery attempt
Timestamp: 05-12-2011:08.43
DellID: 882346
Successful: true
DelAddress: 5513XG-45
PAGE 19
Contact: 040-2298761
21. Order:91245
Case id: 91245 Case id: 91245 Case id: 91245
Activity: create order Activity: pay order Activity: complete order
Timestamp: 28-11-2011:08.12 Timestamp: 02-12-2011:13.45 Timestamp: 05-12-2011:11.33
Customer: John Customer: John Customer: John
Amount: 100 Amount: 100 Amount: 100
OrderLine:112345
Case id: 91245 Case id: 91245
Activity: enter order line Activity: secure order line
Timestamp: 28-11-2011:08.13 Timestamp: 28-11-2011:08.55
OrderLineID: 112345 OrderLineID: 112345
Product: iPhone 4G Product: iPhone 4G
NofItems: 1 NofItems: 1
TotalWeight: 0.250 TotalWeight: 0.250
DellID: 882345 DellID: 882345
OrderLine:112346
Case id: 91245 Case id: 91245 Case id: 91245
Activity: enter order line Activity: create backorder Activity: secure order line
Timestamp: 28-11-2011:08.14 Timestamp: 28-11-2011:08.55 Timestamp: 30-11-2011:09.06
OrderLineID: 112346 OrderLineID: 112346 OrderLineID: 112346
Product: iPod nano Product: iPod nano Product: iPod nano
NofItems: 2 NofItems: 2 NofItems: 2
TotalWeight: 0.300 TotalWeight: 0.300 TotalWeight: 0.300
DellID: 882346 DellID: 882346 DellID: 882346
OrderLine:112347
Case id: 91245 Case id: 91245
Activity: enter order line Activity: secure order line
Timestamp: 28-11-2011:08.15 Timestamp: 29-11-2011:10.06
OrderLineID: 112347 OrderLineID: 112347
Product: iPod classic Product: iPod classic
NofItems: 1 NofItems: 1
TotalWeight: 0.200 TotalWeight: 0.200
DellID: 882345 DellID: 882345
Delivery:882345
Attempt:882345-1 Attempt:882345-2 Attempt:882345-3
Case id: 91245 Case id: 91245 Case id: 91245
Activity: delivery attempt Activity: delivery attempt Activity: delivery attempt
Timestamp: 05-12-2011:08.55 Timestamp: 06-12-2011:09.12 Timestamp: 07-12-2011:08.56
DellID: 882345 DellID: 882345 DellID: 882345
Successful: false Successful: false Successful: true
DelAddress: 5513VJ-22a DelAddress: 5513VJ-22a DelAddress: 5513VJ-22a
Contact: 0497-2553660 Contact: 0497-2553660 Contact: 0497-2553660
Delivery:882346
Attempt:882346-1
Case id: 91245
Activity: delivery attempt
Timestamp: 05-12-2011:08.43
DellID: 882346
Successful: true
DelAddress: 5513XG-45
Contact: 040-2298761
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22. Orderline
Order OrderLineID : OrderLineID
1 1..*
OrderID : OrderID OrderID : OrderID
Orderline instance Customer : CustID
Amount : Euro
Product : ProdID
NofItems : PosInt
Created : DateTime TotalWeight : Weight
Paid : DateTime Entered : DateTime
Completed : DateTime BackOrdered : DateTime
Secured : DateTime
DelID : DelID
1..*
0..1
OrderLine:112345
Attempt Delivery
0..* 1
Case id: 112345 Case id: 112345 DelID : DelID DelID : DelID
Activity: enter order line Activity: secure order line
Timestamp: 28-11-2011:08.13 Timestamp: 28-11-2011:08.55 When : DateTime DelAddress : Address
OrderLineID: 112345 OrderLineID: 112345
Product: iPhone 4G Product: iPhone 4G Successful : Bool Contact : PhoneNo
NofItems: 1 NofItems: 1
TotalWeight: 0.250 TotalWeight: 0.250
DellID: 882345 DellID: 882345
Order:91245
Case id: 112345 Case id: 112345 Case id: 112345
Activity: create order Activity: pay order Activity: complete order
Timestamp: 28-11-2011:08.12 Timestamp: 02-12-2011:13.45 Timestamp: 05-12-2011:11.33
Customer: John Customer: John Customer: John
Amount: 100 Amount: 100 Amount: 100
Delivery:882345
Attempt:882345-1 Attempt:882345-2 Attempt:882345-3
Case id: 112345 Case id: 112345 Case id: 112345
Activity: delivery attempt Activity: delivery attempt Activity: delivery attempt
Timestamp: 05-12-2011:08.55 Timestamp: 06-12-2011:09.12 Timestamp: 07-12-2011:08.56
DellID: 882345 DellID: 882345 DellID: 882345
Successful: false Successful: false Successful: true
DelAddress: 5513VJ-22a DelAddress: 5513VJ-22a DelAddress: 5513VJ-22a PAGE 21
Contact: 0497-2553660 Contact: 0497-2553660 Contact: 0497-2553660
23. Other examples
• The life cycles of reviewers, authors, papers,
reviews, PC chairs, etc.
• The life cycles of job applications and vacancies.
• X-ray machine logs: machine, machine day, patient,
treatment, routine, etc.?
• Therefore, the selection and scoping of instances is
needed.
• Like making deciding on the elements to be put on
map; there may be many maps covering partially
overlapping areas.
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24. Extracting event logs
• Not just a syntactical issue.
• Different views are possible.
• Important:
− Selecting the right instance notion.
− Ordering of events.
− Selection of events.
• Proclets: the true fabric of real-life processes.
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