Process aware information system at amikom oct 2015
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Makalah Artikel Presentasi IT SI Tentang Process aware information system at Amikom oct 2015
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Process aware information system at amikom oct 2015
- 1. PROCESS AWARE INFORMATION SYSTEMS Prof. Riyanarto Sarno, PhD Institut Teknologi Sepuluh Nopember Oktober 2015
- 2. 1. From programming to assembling. 2. From data orientation to process orientation. 3. From design to redesign and organic growth.
- 3. “a software system that manages and executes operational processes involving people, applications, and/or information sources on the basis of process models. “ [1]. Process-Aware Information System, PAIS, merupakan sistem perangkat yang mengelola dan mengeksekusi proses-proses operasional yang melibatkan orang, aplikasi, dan/atau sumber daya informasi dengan mengacu pada model proses.
- 4. Users ... Anwendungen / Application Server Instance 4 Instance 3 Instance 2 Instance 1 Instance 6 Instance 5 Instance 11 Instance 10 Instance 9 Instance 8 Instance 7 Instance 14 Instance 13 Instance 12 Process-aware Information System (PAIS) Process Execution Engine Msg Queuing Time MgmtAuthorization Late Modeling Web Clnt API Validatíon Dyn. Change APIModeling API Admin. API Exceptions Audit Trail ... Process Engineer Process Composer Create Process Schema Modify Process Schema Check Process Schema … Process Repository Process Schemas Application Components
- 5. Early work in ’70s and ’80s use Petri Nets Poor technology support Organizations focused on tasks, not processes Lack of unified modeling Business Process Reengineering (BPR) in ’90s Factoring overspecialized tasks into coherent and globally visible processes Maturation of tools: modeling & workflow management Enterprise process architecture in ’00s Missing standards for BPM Constrained tools emphasize serial processing 5 Still about people, processes, and systems
- 6. Process-aware collaboration (P2P) Project management Incident tracking Enterprise resource planning (ERP) Customer relationship management (CRM) Case handling Business process design/engineering Enterprise Application Integration (EAI) suites Web integration servers 6
- 8. People vs Software Applications Unframed person-to-person (P2P) person-to-application (P2A) application-to-application (A2A) Structure and Predictability of Processes Unframed Ad hoc framed Loosely framed Tightly framed PAIS Types vs Development Tools
- 10. validation, i.e., testing whether the process behaves as expected, verification, i.e., establishing the correctness of a process definition, performance analysis, i.e., evaluating the ability to meet requirements with respect to throughput times, service levels, and resource utilization
- 11. Process Discovery Conformance Checking Fitness (Is the observed behavior possible according to the model?) Appropriateness (Is the model “typical” for the observed behavior?) also possible to check conformance based on organizational models, predefined business rules, temporal formulas, Quality of Service (QoS) definitions Extension decision mining, Performance analysis, user profiling
- 13. A business process, or business method, is a model composed by a collection of related, structured activities or tasks that produce a specific service or product (serve a particular goal) for a particular customer or customers. Business Processes are designed to add value for the customer and should not include unnecessary activities. The outcome of a well designed business process is increased efectiveness (value for the customer) and increased efficiency (less costs for the company). Business Processes can be modeled through a large number of methods and techniques. For instance, the Business Process Modeling Notation is a Business Process Modeling technique that can be used for drawing business processes in a workflow.
- 14. Source: v.d.Aalst, t.Hofstede, Weske 2003 Diagnosis Process Design System Configuration Process Enactment
- 15. Management processes, the processes that govern the operation of a system. Typical management processes include "Corporate Governance“ and "Strategic Management“ Operational processes, processes that constitute the core business and create the primary value stream. Typical operational processes are Purchasing, Manufacturing, Marketing, and Sales. Supporting processes, which support the core processes. Examples include Accounting, Recruitment, Technical support.
- 19. non-alignment of business and IS/IT strategy; poor levels of management commitment; constraints imposed by legacy systems; risks associated with business and IS/IT change; limited scope for team work between business and IS/IT people; negative employee attitude; red tape and bureaucracy within functionally oriented organisations; lack of frameworks for integrating BP&ISR
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Business Process Management Business processes can be very large and complex Thousands of concurrently executed process instances High need for flexibility and adaptability Feedback and learning from real process executions PAIS correctness and PAIS robustness are fundamental Example of an Automotive Engineering Process Healthcare Process
- 22. What is BPM Business Process Management is a generic term, that encompasses the techniques, structured methods, and means to streamline operations and increase efficiency. BPM techniques and methods enable you to identify and modify existing processes to align them with a desired (improved) future state.
- 23. Business Process Management (BPM) Software and strategy for modeling, automating, managing and optimizing business processes across organizational divisions, systems and applications. Systems Goals Process People Information Strategy Policies Compliance
- 24. Benefits of BPM
- 25. BPM & SOA
- 26. Success Depends on Business Impact and Process Complexity
- 28. Instance I1 A D B x x EC Instance I1 A D B x x EC Schema S‘: A D B x xC Traditional Process Lifecycle Support Create Instances Process Execution Process engineer / Process administrator Process participant Arbeitsliste Tätigkeit 1 Tätigkeit 2 Tätigkeit 3 Tätigkeit 4 Integrated Process Lifecycle Support Schema S: A D B x x EC Instance I1 A D B x x EC Execution Log Process Monitoring Page 28
- 29. Instance I1 A D B x x EC Instance I1 A D B x x EC Schema S‘: A D B x xC Traditional Process Lifecycle Support Create Instances Process Execution Process engineer / Process administrator Process participant Arbeitsliste Tätigkeit 1 Tätigkeit 2 Tätigkeit 3 Tätigkeit 4 Schema S: A D B x x EC Instance I1 A D B x x EC Execution Log Process Monitoring Need for Exception Handling and Ad-hoc Changes Need for Process Evolution Some Flexibility Issues Along the Lifecycle Need for Learning from Instance Executions (incl. Ad-hoc Changes) Source: [WRWR09] 29 Need for Dealing with Variations Need for Decision Deferral
- 30. Instance I1 A D B x x EC Instance I1 A D B x x EC Schema S‘: A D B x xC Lifecycle Support in adaptive PAISs Create Instances Process Execution Process engineer / Process administrator Process Monitoring Change Log Instance- specific Change Exception: Delete (I1, E) Process participant Arbeitsliste Tätigkeit 1 Tätigkeit 2 Tätigkeit 3 Tätigkeit 4 Change Propagation Integrated Process Lifecycle Support Schema S: A D B x x EC Instance I1 A D B x x EC Execution Log
- 31. Instance I1 A D B x x EC Instance I1 A D B x x EC Schema S‘: A D B x xC Revised lifecycle for dynamic processes – The ProCycle Approach Create Instances Process Execution Process engineer / Process administrator Process Monitoring Change Log Instance- specific Change Exception: Delete (I1, E) Process participant Arbeitsliste Tätigkeit 1 Tätigkeit 2 Tätigkeit 3 Tätigkeit 4 Change Propagation Memorization and Change Reuse Case Base Derive Process Type Change Integrated Process Lifecycle Support Schema S: A D B x x EC Instance I1 A D B x x EC Execution Log Migrate Case Base
- 32. Process Process Process Process Process Process Process Process Process Process ProductProcess ProductProcess =
- 33. Spezifi- zieren Spezifiziert AngeliefertBestellen Tür-Steuergerät S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Klima-Steuergerät S ESicht- Prüfung [i.O.] [Nicht i.O.] Installieren τ τ DatengetriebeneProzessstruktur Installieren Installieren BestätigungMechanik Spezifi- zieren Spezifiziert AngeliefertBestellen Display S ESicht- Prüfung [i.O.] [Nicht i.O.] Auswahl Lieferant Ausgewählt Aufgebautτ Labor- test Navigation S E Aufbau Mechanik Aufgebaut GetestetTest Mechanik Testfahrt E-Klasse S E Auswahl Lieferant Ausgewählt Aufgebautτ Labor- test Powertrain S E Spezifi- zieren Spezifiziert AngeliefertBestellen Getriebe Steuergerät S ESicht- Prüfung [i.O.] [Nicht i.O.] Installieren Installieren BestätigungMechanik τ Spezifi- zieren Spezifiziert AngeliefertBestellen Motor-SG Benzin S ESicht- Prüfung [i.O.] [Nicht i.O.] Installieren Spezifi- zieren Spezifiziert AngeliefertBestellen Multimedia-Einheit S ESicht- Prüfung [i.O.] [Nicht i.O.] τ Spezifi- zieren Spezifiziert AngeliefertBestellen GPS Sensor S ESicht- Prüfung [i.O.] [Nicht i.O.] Installieren τ Installieren Spezifi- zieren Spezifiziert AngeliefertBestellen Motor-SG Diesel S ESicht- Prüfung [i.O.] [Nicht i.O.] Installieren Installieren Spezifi- zieren Spezifiziert AngeliefertBestellen Verstärker S ESicht- Prüfung [i.O.] [Nicht i.O.] Auswahl Lieferant Ausgewählt Aufgebautτ Labor- test Innenraum S E τ Installieren Spezifi- zieren Spezifiziert AngeliefertBestellen Tür-SG Beifahrer S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Klima-Steuergerät low S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Klima Display S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Getriebe Steuergerät 2 S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Motor-SG Benzin 2 S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Klima Bedieneinheit 2 S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Klima Bedieneinheit 1 S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Motor-SG Diesel 2 S ESicht- Prüfung [i.O.] [Nicht i.O.] Spezifi- zieren Spezifiziert AngeliefertBestellen Klima Bedieneinheit 3 S ESicht- Prüfung [i.O.] [Nicht i.O.] Installieren Installieren Installieren Installieren Installieren Installieren InstallierenInstallieren Installieren Installieren τ τ
- 34. WMS orchestrates activities to compose a process rather than performs data management. Process Mining combines the strengths of both data mining and process modeling by automatically creating process models based on existing IT event log data. It yields live models connected to the business and can be updated easily at any point in time.
- 35. WMS facilitates system to adapt with rapid changes of business process. WMS could decrease the chances affecting human error by automating several processes. (Van der Weken, et al., 2009). Nonetheless, Human Workflow in BPM manages some processes performed manually. (Liu, et al., 2012).
- 36. Research (Green & Choi, 1997; Fanning & Cogger, 1998) propose neural network solution to detect fraud. Data mining techniques are also used to investigate some potentially fraudulent patterns (Lee, et al., 2001; Wheeler & Aitken, 2000). In addition to that, some techniques using machine learning and genetic algorithm are also implemented (Williams, et al., 2006; Gottlieb, et al., 2006).
- 37. A technique to obtain a knowledge from event log, where certain organizational activities are recorded.
- 38. Process Mining comprises How to Discover Process Model How to Check the Conformance between Actual Process Model and the event logs How to Utilize Event Logs for Continuous Improvement of Process Modelling Involves multiple perspectives (process, data, resources, etc.)
- 40. 40 information system models analyzes discovery records events,e.g., messages, transactions, etc. specifies configures implements analyzes supports/ controls extension conformance “world” people machines organizations components business processes (process) model event logs Process Mining Tools Start Register order Prepare shipm ent Ship goods (Re)send bill Receive paym ent Contact custom er Archive order End Process Model Organizational Model Social Network Organizational Miner Social Network Miner Analyze Social Network
- 41. 41 information system models analyzes discovery records events,e.g., messages, transactions, etc. specifies configures implements analyzes supports/ controls extension conformance “world” people machines organizations components business processes (process) model event logs Process Mining Tools Auditing/Security Start Register order Prepare shipm ent Ship goods (Re)send bill Receive paym ent Contact custom er Archive order End Compliance Process Model Conformance Checker LTL Checker
- 42. 42 Organizational mining plug-ins can discover Roles/Teams in organizations Social networks for originators Some metrics of social networks are based on ordering relations (e.g., the ordering relations used by the Alpha algorithm) Conformance Checker assesses how much a process model matches process instances LTL (Linear Temporal Logics) Checker uses logics to verify properties in event logs
- 43. 43 information system models analyzes discovery records events,e.g., messages, transactions, etc. specifies configures implements analyzes supports/ controls extension conformance “world” people machines organizations components business processes (process) model event logs Process Mining Tools Start Register order Prepare shipm ent Ship goods (Re)send bill Receive paym ent Contact custom er Archive order End Bottlenecks/ Business Rules Process Model Performance Analysis
- 44. 44 Detection of data dependencies that affect the rounting the routing of process instances Which conditions influence the choice between a full check and a policy only one?
- 45. 45 Make tacit knowledge explicit Better understand the process model
- 46. 46
- 47. 48 1. Read a log + model 2. Identify the decision points in a model 3. Find out which alternative branch has been taken for a given process instance and decision point 4. Discover the rules for each decision point 5. Return the enhanced model with the discovered rules
- 48. 49 1. Read a log + model 2. Identify the decision points in a model 3. Find out which alternative branch has been taken for a given process instance and decision point 4. Discover the rules for each decision point 5. Return the enhanced model with the discovered rules How can we spot the decision points in a Petri net?
- 49. 50 1. Read a log + model 2. Identify the decision points in a model 3. Find out which alternative branch has been taken for a given process instance and decision point 4. Discover the rules for each decision point 5. Return the enhanced model with the discovered rules
- 51. 52 1. Read a log + model 2. Identify the decision points in a model 3. Find out which alternative branch has been taken for a given process instance and decision point 4. Discover the rules for each decision point 5. Return the enhanced model with the discovered rules Which elements are the classes and which are the attributes?
- 52. CEPEngine WMS Event Repository Conformance Checking Workflow Event Process Discovery Rule Repository Fraud Notification BPM CEP Process Mining GUI
- 53. 54 Training examples for decision point "p0" Discovered decision tree for point "p0"
- 54. 55
- 55. 56
- 56. 57
- 57. 62 Motivation Provide different Key Performance Indicators (KPIs) relating to the execution of processes Main idea Replay the log in a model and detect Bottlenecks Throughput times Execution times Waiting times Synchronization times Path probabilities etc
- 58. 63 How can we spot the difference between waiting and execution times?
- 59. 64
- 60. 65
- 61. 66 20.8 minutes 1.3 minutes What are these average synchronization times telling us?
- 62. 67 What are these path probabilities telling us?
- 63. 68
- 64. 69 Extension techniques enhance existing models with information discovered from event logs The Decision Point Analysis plug-in can discover the “business rules” for the moments of choice in a process model The Performance Analysis with Petri Nets plug-in provides various KPIs w.r.t. the execution of processes The results of both techniques can be used to create simulation models for CPN Tools
- 65. Zeng, Qingtian, Sherry Sun, Hua Duan, Cong Liu, Huaiqing Wang “Cross- Organizational Collaborative Workflow Mining from a Multi Source Log”, Elsevier, vol. 54, Dec. 2012 pp.1280-1301. B.F .van Dongen, J. Mendling, W .M.P . Van der Aalst “Structural Patterns for Soundness of Business Process Models” W.M.P. van der Aalst, K.M. van Hee, A.H.M. ter Hofstede, N. Sidorova ,H.M.W. Verbeek, M. Voorhoeve and M.T. Wynn “Soundness of Workflow Nets:Classication, Decidability, andAnalysis” Wil M.P. van der Aalst “Intra- and Inter-Organizational Process Mining:Discovering Processes Within and Between Organizations” Riyanarto Sarno, Bandung Arry Sanjoyo, Imam Mukhlash, hanim Maria Astuti“Petri Net Model of ERP business Process Variation For Small and Medium Enterproses”
- 68. The Indonesian National Police (POLRI) officially announces that there have been 71 fraudulent cases since 2003. Indonesia becomes the second biggest country in terms of fraud frequency.
- 69. In 2012, there were 1,388 frauds across 96 countries causing 1,411,000 USD loss (ACFE, 2012).
- 70. One of factors causing fraud is the opportunity to deceive or to embezzle the system [ACFE 2012]. Type of Fraud Technologies ATM & Internet Utilization: counterfeit, not present, altered Handling of transaction: lost or stolen, not received Transactions Product: Credit & Debit, Card, and Check Relationship to accounts first, second, and third parties Business Process: Application Transaction Embellishment, theft, & fabrication
- 73. Mesin CEP ? ? ? ? ? 143224 7 2 3 13.22.11 Event Event Event ? ? ? ? ? 143223 2 2 4 13.22.10 ? ? ? ? ? 143223 2 2 4 13.22.10 Engine Observation Time
- 75. How to mine process from the incoming events as soon as they are observed? Process Mining Engine 2 1 3 4
- 76. Fitness: ? %
- 78. DBMS CEP Queries Ad hoc on stored data Continuous standing queries Latency Seconds Milliseconds Data Rate Hundreds per Second Tens of thousands per second request response Event output stream input stream
- 79. Standing Queries Query Logic Event sources Event targets ` Devices, Sensors Web servers Event stores & Databases Stock ticker, news feeds Event stores & Databases Pagers & Monitoring devices KPI Dashboards, SharePoint UI Trading stations Input Adapters Output AdaptersStreamInsight Engine Query Logic Query Logic StreamInsight Application Development StreamInsightApplication at Runtime
- 80. Sequence Pattern Mining (SPM) is a data mining technique to find pattern of related sequences. (Marbroukeh & Ezeife, 2010) Table of Traces <a(bc)dc> is a subsequence of <a(abc)(ac)d(cf)> Case ID Activities 10 <a(abc)(ac)d(cf)> 20 <(ad)c(bc)(ae)> 30 <(ef)(ab)(df)cb> 40 <eg(af)cbc> Rule : <(ab)c> Both explicit and implicit dependency are investigated using rules.
- 82. Creating Input Adapter on CEP Engine Applying Online Heuristic Miner to discover streaming events Implementing SPM for Conformance Checking Providing process-aware workflow events
- 83. CEPEngine WMS Event Repository Conformance Checking Workflow Event Process Discovery Rule Repository Fraud Notification BPM CEP Process Mining GUI
- 84. FRAUD !!!
- 85. CEPEngine WMS Event Repository Conformance Checking Workflow Event Process Discovery Rule Repository Fraud Notification BPM CEP Process Mining GUI
- 87. CEPEngine WMS Event Repository Conformance Checking Workflow Event Process Discovery Rule Repository Fraud Notification BPM CEP Process Mining GUI
- 89. CEPEngine WMS Event Repository Conformance Checking Workflow Event Process Discovery Rule Repository Fraud Notification BPM CEP Process Mining GUI
- 90. Check whether the sequence of rules is a subsequence of the sequence of process model. If it is not, then categorized as a fraud. And vice versa. Storing Rules in Repository Storing Process in Repository Defining Rules Discovering Process from Incoming Events is the Rule a Subsequence of the Process Notifying as a Fraudulent Case Notifying as a Legitimated Case Conformance Checking No Yes
- 91. No List of Traces 1 Create PO–Sign–Release–IR–SF-Pay 2 Create PO–Sign–Release–GR–IR–Pay 3 Create PO–Change Line–Sign–Release–IR–GR-Pay 4 Create PO–Change Line–Sign–Release–IR-Pay No List of Legitimated Rules 1 <Create PO, Sign, Release, (IR,SF), Pay> 2 <Create PO, Sign, Release, (GR,IR), Pay> 3 <Create PO, Sign, Release, (SF, IR), Pay> 4 <Create PO, Sign, Release, (IR, GR), Pay> 4 Create PO–Change Line– Sign–Release–IR-Pay Is each sequence of legitimated rules a sub sequence of sequence of trace ??
- 92. CEPEngine WMS Event Repository Conformance Checking Workflow Event Process Discovery Rule Repository Fraud Notification BPM CEP Process Mining GUI Capturing Workflow Events Using CEP Mining Streaming Events on the fly with Limited Size of Repository Conformance Checking Using SPM
- 96. 1. Several legitimated (not-fraud) patterns are provided in the rule repository. 2. Randomized events of ERP 2013, which have role as a dataset, are streamed through CEP engine. 3. Every Process Model discovered from the events is compared to every legitimated pattern. Data Set Engine Rule Repository Result
- 97. Evaluate the proposed method with several datasets from both real and simulated events. The datasets are deliberately provided in cases of comparing fraudulent event to legitimated event.
- 99. What event attributes are required to store in case of fraud investigation? How to capture workflow events immediately by using Complex Event Processing? How to mine process of the incoming events in certain observation time? How to perform a conformance checking to investigate internal frauds in Workflow Management System?
- 100. Streaming events should define a very basic and simple attributes of the event. Complex Event Processing could improve the responsiveness of the system in cases of capturing emitted events. Process Mining empowers the system to discover the actual process model from the incoming events. Sequential Pattern Mining could be utilized to conform between the actual model discovered by the incoming events and that of the rules.
- 101. To define standard of streaming event attributes as efficient as possible. To increase system responsiveness in terms of catching workflow events by integrating Workflow Management System with CEP engine. To combine the power of Process Mining in terms of process auditing and the power of Complex Event Processing in terms of real-time processing. To implement Sequential Pattern Mining as a fraud-investigating algorithm based on process, which is discovered from the incoming events.
- 102. A new analysis to define standard attributes of streaming events for purposes of providing process-aware WMS. A special technique to integrate CEP engine and WMS will be proposed to catch web services, which represent workflow activities, and convert them into events. A chain of procedures for discovering the sequences from the incoming events and storing them in a repository is going to be introduced. A modified Sequential Pattern Mining technique would be applied, as a conformance checking technique, in cases of fraud detection in Workflow Management System.
- 103. The inspected cases belong to internal frauds, which are performed by human. The method focuses on analyzing the sequential pattern rather than investigating data. There will not be any invalid data on evaluation phase. Every cases evaluates in this research belongs to internal fraud. This research emphasizes on utilizing Complex Event Processing to catch fraudulent events in Workflow Management System, instead of analyzing the complexity of the investigating algorithm. This research aims at fraud detection and not at fraud mitigation. Every Case ID of event log has been labeled before mined. In cases of evaluation, both real-time and stored events would be investigated. Every activity, which performed by human, should be recorded in event logs. The event logs enact as data set to evaluate the proposed method.
- 104. The practical ways to prevent is providing SOP and limited access on the Graphical User Interface (GUI). e.g., providing a limited option in a combo box when choosing an option, providing SOP to mitigate fraud, etc. New Open… Save Save As… Page Setup… Print… Exit File text text text text
- 105. Nevertheless, There is no one able to validate what really occurs in the system, since there is no proof based on actual data. Therefore, we need to mine knowledge from actual data (i.e., event log, audit trail, etc.). Event Log
- 106. Huge amounts of data Enterprise IT systems collect more and more data about the business processes they support. These data usually reflect very closely what happened in “the real world” and can be a great source of insight for understanding and improving the business. Process perspective Unlike data mining, process mining focuses on the process perspective: It includes the temporal aspect and looks at a single process execution as a sequence of activities that have been performed. Most data mining techniques extract abstract patterns in the form of, for example, rules or decision trees. In contrast, process mining creates complete process models, and then uses them to precisely highlight where the bottlenecks are. Also exceptions are important In data mining, generalization is very important to avoid what is called “overfitting the data”. This means that one wants to strip away all the examples that do not match the general rule. In process mining, generalization is also necessary to deal with complex processes and understand the main process flows. However, understanding the exceptions is often important to discover inefficiencies and points of improvement. Focus on discovery In data mining, models are often trained to make predictions about future similar instances in the same space. Quite a few data mining and machine learning methods operate as a “black box” that spills out predictions without the possibility to trace back the “why”.
- 107. It analyzes the control flow model of actual process. Event logs are utilized as sources from which actual model of workflow process is built. To investigate a fraud, the control flow model is examined in various perspectives. It could analyze fraudulent patterns found in workflow process.
- 108. Most of the algoritihms could only be conducted in a batch processing, not in a real-time processing. To mine process, a complete set of event log is required. 1 day’s activities Event log for 1 day Process Mining Engine Fraud Notification
- 109. Human Task Human Task AtomicService (Ms.WWF) CompositeService (Ms.BizTalk) CompositeService+ HumanTask (Ms.Sharepoint) The Three Levels of Workflow Management System
- 110. It is impossible to store all of the streaming events. The events are evolving. It is not operated in a batch manner. A model is discovered from an incomplete event log.