Process aware information system at amikom oct 2015

PROCESS AWARE INFORMATION SYSTEMS

Prof. Riyanarto Sarno, PhD

Institut Teknologi Sepuluh NopemberOktober 2015

1. From programming to assembling.

2. From data orientation to process orientation.

3. From design to redesignand organic growth.

“a software system that manages and executes operational processes involvingpeople, applications, and/or information sources on the basis of process models.“ [1].

Process-Aware Information System, PAIS, merupakan sistem perangkat yangmengelola dan mengeksekusi proses-proses operasional yang melibatkanorang, aplikasi, dan/atau sumber daya informasi dengan mengacu pada modelproses.

Users

...Anwendungen / Application Server

Instance 4Instance 3





Instance 7


Instance 12

Process-aware Information System (PAIS)

Process Execution Engine

Msg QueuingTime MgmtAuthorization

Late Modeling Web Clnt APIValidatíon

Dyn. Change APIModeling APIAdmin. API

Exceptions Audit Trail ...

Process Engineer

Process Composer

Create Process SchemaModify Process SchemaCheck Process Schema…

Process Repository

Process Schemas

ApplicationComponents

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

processesMaturation of tools: modeling & workflow management

Enterprise process architecture in ’00sMissing standards for BPM Constrained tools emphasize serial processing

5Still about people, processes, and systems

Process-aware collaboration (P2P)Project management Incident trackingEnterprise resource planning (ERP)Customer relationship management (CRM)Case handling Business process design/engineeringEnterprise Application Integration (EAI) suitesWeb integration servers

6

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

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

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

Business Process Management

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.

Source: v.d.Aalst, t.Hofstede, Weske 2003

Diagnosis

Process Design

System Configuration

Process Enactment

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.

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

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.

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

Benefits of BPM

BPM & SOA

Success Depends on Business Impact and Process Complexity

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

Cre

ate

Inst

ance

s

ProcessExecution

Process engineer /Process administrator

Process participant

ArbeitslisteTätigkeit 1Tätigkeit 2Tätigkeit 3Tätigkeit 4

Integrated Process Lifecycle Support

Schema S:

A

D

B

x x EC

Instance I1

A

D

B

x x EC

Execution Log

ProcessMonitoring

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

Cre

ate

Inst

ance

s

ProcessExecution


Process participant


Schema S:

A

D

B

x x EC

Instance I1

A

D

B

x x EC

Execution Log

ProcessMonitoring

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

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

Cre

ate

Inst

ance

s

ProcessExecution


ProcessMonitoring

Change Log

Instance-specificChange

Exception:Delete (I1, E)

Process participant


Ch

ange

Pr

opag

atio

n

Integrated Process Lifecycle Support

Schema S:

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Revised lifecycle for dynamic processes –The ProCycleApproach

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ProcessExecution


ProcessMonitoring

Change Log

Instance-specificChange

Exception:Delete (I1, E)

Process participant


Ch

ange

Pr

opag

atio

n

Memorization and Change

ReuseCase Base

Derive Process Type Change

Integrated Process Lifecycle SupportSchema S:

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Instance I1

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D

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Migrate Case Base

Process

Process

Process Process Process

Process

Process

Process

ProcessProcess

ProductProcess ProductProcess =

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WMS orchestrates activitiesto 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.

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).

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).

A technique to obtain a knowledge from event log, where certain organizational

activities are recorded.

Process Mining comprisesHow to Discover Process ModelHow to Check the Conformance between Actual Process Model and the event logsHow to Utilize Event Logs for Continuous Improvement of Process Modelling Involves multiple perspectives (process, data, resources, etc.)

39

information system

modelsanalyzes

discovery

records events, e.g., messages,

transactions, etc.

specifies configures implements analyzes

supports/controls

extensionconformance

“world”people machines

organizationscomponents

business processes

(process)model

event logs

Process Mining Tools

40

information system

modelsanalyzes

discovery


transactions, etc.


supports/controls




business processes

(process)model

event logs


Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContactcustomer

Archive order

End

Process Model

Organizational Model

Social Network

Organizational Miner

Social Network Miner

Analyze Social Network

41

information system

modelsanalyzes

discovery


transactions, etc.


supports/controls




business processes

(process)model

event logs


Auditing/Security

Start

Register order

Prepareshipment

Ship goods

(Re)send bill


Archive order

End

Compliance Process Model

Conformance Checker

LTL Checker

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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

information system

modelsanalyzes

discovery


transactions, etc.


supports/controls




business processes

(process)model

event logs


Start

Register order

Prepareshipment

Ship goods

(Re)send bill


Archive order

End

Bottlenecks/Business RulesProcess Model

Performance Analysis

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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?

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Make tacit knowledge explicit

Better understand the process model

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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

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5. Return the enhanced model with the discovered rulesHow can we spot the decision points in a

Petri net?

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Attributes Classes: Yes/No

52






Which elements are the classes and which are

the attributes?

CEP

Engi

ne

WMS

EventRepository

ConformanceChecking

WorkflowEvent

ProcessDiscovery

Rule Repository

Fraud Notification

BPM CEP Process Mining GUI

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Training examples for decision point "p0"

Discovered decision tree for point "p0"

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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

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How can we spot the difference between waiting and execution

times?

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20.8 minutes

1.3 minutes

What are these average synchronization times

telling us?

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What are these path probabilities telling us?

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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

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”

Thank [email protected]

http://riyanarto.blogspot.com

The Indonesian National Police (POLRI) officially announces

that there have been 71fraudulent cases since 2003.

Indonesia becomes the second biggest country in terms of

fraud frequency.

In 2012, there were 1,388 frauds across 96 countries causing

1,411,000 USD loss (ACFE, 2012).

One of factors causing fraud is the opportunity to deceive or to embezzlethe system [ACFE 2012]. Type of

Fraud

TechnologiesATM & Internet

Utilization:counterfeit,not present,

altered

Handling oftransaction:

lost or stolen,not received

TransactionsProduct: Credit & Debit, Card,

and Check

Relationship to accounts first, second, and third parties

Business Process:

Application Transaction

Embellishment, theft, &

fabrication

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

How to mine process from the incoming events as soon as they are observed?

Process Mining Engine

2

1

3

4

Fitness: ? %

DBMS CEPQueries Ad hoc on stored data Continuous standing queriesLatency Seconds MillisecondsData Rate Hundreds per Second Tens of thousands per second

request

response

Event output stream

input stream

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

InputAdapters

OutputAdaptersStreamInsight Engine

Query Logic

Query Logic

StreamInsightApplication Development

StreamInsight Application at Runtime

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 Activities10 <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.

Creating Input Adapteron CEP Engine

Applying Online Heuristic Miner to discover streaming

events

Implementing SPM forConformance Checking

Providing process-awareworkflow events

CEP

Engi

ne

WMS

EventRepository

ConformanceChecking

WorkflowEvent

ProcessDiscovery

Rule Repository

Fraud Notification


FRAUD !!!

CEP

Engi

ne

WMS

EventRepository

ConformanceChecking

WorkflowEvent

ProcessDiscovery

Rule Repository

Fraud Notification


WMS

InputAdapter

Web Services

CEP Engine

StreamingEventsActivities

ContinuousQuery

CEP

Engi

ne

WMS

EventRepository

ConformanceChecking

WorkflowEvent

ProcessDiscovery

Rule Repository

Fraud Notification


Extend

Repository

CEP

Engi

ne

WMS

EventRepository

ConformanceChecking

WorkflowEvent

ProcessDiscovery

Rule Repository

Fraud Notification


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 Rulea Subsequence of the Process

Notifying asa Fraudulent Case

Notifying asa Legitimated Case

ConformanceChecking

No Yes

No List of Traces1 Create PO–Sign–Release–IR–SF-Pay2 Create PO–Sign–Release–GR–IR–Pay3 Create PO–Change Line–Sign–Release–IR–GR-Pay4 Create PO–Change Line–Sign–Release–IR-Pay

No List of Legitimated Rules1 <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 ??

CEP

Engi

ne

WMS

EventRepository

ConformanceChecking

WorkflowEvent

ProcessDiscovery

Rule Repository

Fraud Notification


Capturing Workflow Events Using CEP

Mining Streaming Events on the fly with Limited Size

of Repository

Conformance Checking Using SPM

CEP

Engi

ne

EventRepository

ConformanceChecking

WorkflowEvent

ProcessDiscovery

Rule Repository

Fraud Notification

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

RuleRepository

Result

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.

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?

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.

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.

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.

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.

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.

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File

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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

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”.

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.

Most of the algoritihmscould 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’sactivities

Event logfor 1 day

ProcessMiningEngine

FraudNotification

Human Task

Human Task

Atom

ic Se

rvic

e(M

s. W

WF)

Com

posi

te S

ervi

ce(M

s. B

izTal

k)

Com

posi

te S

ervi

ce +

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man

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s. S

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poin

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The Three Levels of Workflow Management System

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.

Process aware information system at amikom oct 2015

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