Enviar pesquisa
Carregar
Business Process Insight - SRII 2012
•
1 gostou
•
4,585 visualizações
Szabolcs Rozsnyai
Seguir
Tecnologia
Negócios
Vista de apresentação de diapositivos
Denunciar
Compartilhar
Vista de apresentação de diapositivos
Denunciar
Compartilhar
1 de 17
Recomendados
Cloud Computing and System z
Cloud Computing and System z
dkang
Converged Infrastructure as a Go Forward Strategy
Converged Infrastructure as a Go Forward Strategy
James Charter
Cloud - Acxiom Case StudyOrganic web assetNew asset
Cloud - Acxiom Case StudyOrganic web assetNew asset
IBM India Smarter Computing
Stuart Wakefield Cloud Computing
Stuart Wakefield Cloud Computing
Future Perfect 2012
Security in a Cloudy Architecture
Security in a Cloudy Architecture
Bob Rhubart
Managing change in the data center network
Managing change in the data center network
Interop
BSM201.pdf
BSM201.pdf
Novell
ITCamp 2012 - Adrian Stoian - Migrating from CFG MGR 2007 to CFG MGR 2012
ITCamp 2012 - Adrian Stoian - Migrating from CFG MGR 2007 to CFG MGR 2012
ITCamp
Recomendados
Cloud Computing and System z
Cloud Computing and System z
dkang
Converged Infrastructure as a Go Forward Strategy
Converged Infrastructure as a Go Forward Strategy
James Charter
Cloud - Acxiom Case StudyOrganic web assetNew asset
Cloud - Acxiom Case StudyOrganic web assetNew asset
IBM India Smarter Computing
Stuart Wakefield Cloud Computing
Stuart Wakefield Cloud Computing
Future Perfect 2012
Security in a Cloudy Architecture
Security in a Cloudy Architecture
Bob Rhubart
Managing change in the data center network
Managing change in the data center network
Interop
BSM201.pdf
BSM201.pdf
Novell
ITCamp 2012 - Adrian Stoian - Migrating from CFG MGR 2007 to CFG MGR 2012
ITCamp 2012 - Adrian Stoian - Migrating from CFG MGR 2007 to CFG MGR 2012
ITCamp
Make Your Business More Flexible with Scalable Business Process Management So...
Make Your Business More Flexible with Scalable Business Process Management So...
Perficient, Inc.
IDC Says, Don't Move To The Cloud
IDC Says, Don't Move To The Cloud
Novell
2012 ukdc shared services value prop growth day newbury
2012 ukdc shared services value prop growth day newbury
bara2cls
PRESENTATION: Tips and Tricks for Government Agencies to Push the Limits of P...
PRESENTATION: Tips and Tricks for Government Agencies to Push the Limits of P...
Adlib - The PDF Experts
Exadata meeting business challenges! - Doug Cackett
Exadata meeting business challenges! - Doug Cackett
ORACLE USER GROUP ESTONIA
SugarCON partner presentation by IBM
SugarCON partner presentation by IBM
Bevdewitt
Id105 fortify your ibm lotus notes and ibm lotus domino infrastructure agai...
Id105 fortify your ibm lotus notes and ibm lotus domino infrastructure agai...
waukema
E Business
E Business
Rizwan Qamar
Voith boosts productivity, cuts costs with IBM Power Systems and DB2
Voith boosts productivity, cuts costs with IBM Power Systems and DB2
IBM India Smarter Computing
Wall Street Technology
Wall Street Technology
Bharat Gera
PCTY 2012, Overvågning af forretningssystemer i et virtuelt miljø v. Hans Ped...
PCTY 2012, Overvågning af forretningssystemer i et virtuelt miljø v. Hans Ped...
IBM Danmark
Enterprise Architecture
Enterprise Architecture
Raman Kannan
Ibm log differentiators for strategic network planning 2011 v6
Ibm log differentiators for strategic network planning 2011 v6
Artem Vinogradov
Dc architecture for_cloud
Dc architecture for_cloud
Alain Geenrits
Top 6 Reasons to Use a Distributed Data Grid
Top 6 Reasons to Use a Distributed Data Grid
ScaleOut Software
1667 making z rules work session
1667 making z rules work session
nick_garrod
Cloud Computing -- Organizational Shift
Cloud Computing -- Organizational Shift
Raman Kannan
Network Operations Managed Services (NOMS)
Network Operations Managed Services (NOMS)
TMNG Global
Oracle Data Warehouse
Oracle Data Warehouse
DataminingTools Inc
Building a business intelligence architecture fit for the 21st century by Jon...
Building a business intelligence architecture fit for the 21st century by Jon...
Mark Tapley
Large-Scale Distributed Storage System for Business Provenance - Cloud 2011
Large-Scale Distributed Storage System for Business Provenance - Cloud 2011
Szabolcs Rozsnyai
Automated Correlation Discovery for Semi-Structured Business Processes
Automated Correlation Discovery for Semi-Structured Business Processes
Szabolcs Rozsnyai
Mais conteúdo relacionado
Mais procurados
Make Your Business More Flexible with Scalable Business Process Management So...
Make Your Business More Flexible with Scalable Business Process Management So...
Perficient, Inc.
IDC Says, Don't Move To The Cloud
IDC Says, Don't Move To The Cloud
Novell
2012 ukdc shared services value prop growth day newbury
2012 ukdc shared services value prop growth day newbury
bara2cls
PRESENTATION: Tips and Tricks for Government Agencies to Push the Limits of P...
PRESENTATION: Tips and Tricks for Government Agencies to Push the Limits of P...
Adlib - The PDF Experts
Exadata meeting business challenges! - Doug Cackett
Exadata meeting business challenges! - Doug Cackett
ORACLE USER GROUP ESTONIA
SugarCON partner presentation by IBM
SugarCON partner presentation by IBM
Bevdewitt
Id105 fortify your ibm lotus notes and ibm lotus domino infrastructure agai...
Id105 fortify your ibm lotus notes and ibm lotus domino infrastructure agai...
waukema
E Business
E Business
Rizwan Qamar
Voith boosts productivity, cuts costs with IBM Power Systems and DB2
Voith boosts productivity, cuts costs with IBM Power Systems and DB2
IBM India Smarter Computing
Wall Street Technology
Wall Street Technology
Bharat Gera
PCTY 2012, Overvågning af forretningssystemer i et virtuelt miljø v. Hans Ped...
PCTY 2012, Overvågning af forretningssystemer i et virtuelt miljø v. Hans Ped...
IBM Danmark
Enterprise Architecture
Enterprise Architecture
Raman Kannan
Ibm log differentiators for strategic network planning 2011 v6
Ibm log differentiators for strategic network planning 2011 v6
Artem Vinogradov
Dc architecture for_cloud
Dc architecture for_cloud
Alain Geenrits
Top 6 Reasons to Use a Distributed Data Grid
Top 6 Reasons to Use a Distributed Data Grid
ScaleOut Software
1667 making z rules work session
1667 making z rules work session
nick_garrod
Cloud Computing -- Organizational Shift
Cloud Computing -- Organizational Shift
Raman Kannan
Network Operations Managed Services (NOMS)
Network Operations Managed Services (NOMS)
TMNG Global
Oracle Data Warehouse
Oracle Data Warehouse
DataminingTools Inc
Building a business intelligence architecture fit for the 21st century by Jon...
Building a business intelligence architecture fit for the 21st century by Jon...
Mark Tapley
Mais procurados
(20)
Make Your Business More Flexible with Scalable Business Process Management So...
Make Your Business More Flexible with Scalable Business Process Management So...
IDC Says, Don't Move To The Cloud
IDC Says, Don't Move To The Cloud
2012 ukdc shared services value prop growth day newbury
2012 ukdc shared services value prop growth day newbury
PRESENTATION: Tips and Tricks for Government Agencies to Push the Limits of P...
PRESENTATION: Tips and Tricks for Government Agencies to Push the Limits of P...
Exadata meeting business challenges! - Doug Cackett
Exadata meeting business challenges! - Doug Cackett
SugarCON partner presentation by IBM
SugarCON partner presentation by IBM
Id105 fortify your ibm lotus notes and ibm lotus domino infrastructure agai...
Id105 fortify your ibm lotus notes and ibm lotus domino infrastructure agai...
E Business
E Business
Voith boosts productivity, cuts costs with IBM Power Systems and DB2
Voith boosts productivity, cuts costs with IBM Power Systems and DB2
Wall Street Technology
Wall Street Technology
PCTY 2012, Overvågning af forretningssystemer i et virtuelt miljø v. Hans Ped...
PCTY 2012, Overvågning af forretningssystemer i et virtuelt miljø v. Hans Ped...
Enterprise Architecture
Enterprise Architecture
Ibm log differentiators for strategic network planning 2011 v6
Ibm log differentiators for strategic network planning 2011 v6
Dc architecture for_cloud
Dc architecture for_cloud
Top 6 Reasons to Use a Distributed Data Grid
Top 6 Reasons to Use a Distributed Data Grid
1667 making z rules work session
1667 making z rules work session
Cloud Computing -- Organizational Shift
Cloud Computing -- Organizational Shift
Network Operations Managed Services (NOMS)
Network Operations Managed Services (NOMS)
Oracle Data Warehouse
Oracle Data Warehouse
Building a business intelligence architecture fit for the 21st century by Jon...
Building a business intelligence architecture fit for the 21st century by Jon...
Destaque
Large-Scale Distributed Storage System for Business Provenance - Cloud 2011
Large-Scale Distributed Storage System for Business Provenance - Cloud 2011
Szabolcs Rozsnyai
Automated Correlation Discovery for Semi-Structured Business Processes
Automated Correlation Discovery for Semi-Structured Business Processes
Szabolcs Rozsnyai
Business Process Management and Virtual Worlds
Business Process Management and Virtual Worlds
Ian Hughes / epredator
Business process modelling with sbi an example
Business process modelling with sbi an example
Satyam Anand
Business Process Modeling Case Study
Business Process Modeling Case Study
Akash Gajjar
Supply chain excellence
Supply chain excellence
Keivan Zokaei
Business Process Management in Higher Education Institutions - an award winni...
Business Process Management in Higher Education Institutions - an award winni...
Tomislav Rozman
The Forrester Wave BPM Suites 2013
The Forrester Wave BPM Suites 2013
Luciano Gomes
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Marlon Dumas
Introduction to the BPM Lifecycle
Introduction to the BPM Lifecycle
Michael zur Muehlen
Service Management with Odoo/OpenERP - Opendays 2014
Service Management with Odoo/OpenERP - Opendays 2014
Daniel Reis
H&M Strategic Recommendations in Depth
H&M Strategic Recommendations in Depth
Vasiliki Evangelou
Destaque
(12)
Large-Scale Distributed Storage System for Business Provenance - Cloud 2011
Large-Scale Distributed Storage System for Business Provenance - Cloud 2011
Automated Correlation Discovery for Semi-Structured Business Processes
Automated Correlation Discovery for Semi-Structured Business Processes
Business Process Management and Virtual Worlds
Business Process Management and Virtual Worlds
Business process modelling with sbi an example
Business process modelling with sbi an example
Business Process Modeling Case Study
Business Process Modeling Case Study
Supply chain excellence
Supply chain excellence
Business Process Management in Higher Education Institutions - an award winni...
Business Process Management in Higher Education Institutions - an award winni...
The Forrester Wave BPM Suites 2013
The Forrester Wave BPM Suites 2013
Beyond Process Mining: Discovering Business Rules From Event Logs
Beyond Process Mining: Discovering Business Rules From Event Logs
Introduction to the BPM Lifecycle
Introduction to the BPM Lifecycle
Service Management with Odoo/OpenERP - Opendays 2014
Service Management with Odoo/OpenERP - Opendays 2014
H&M Strategic Recommendations in Depth
H&M Strategic Recommendations in Depth
Semelhante a Business Process Insight - SRII 2012
How to get cloud architecture and design right the first time 2012
How to get cloud architecture and design right the first time 2012
David Linthicum
Network Sage™ Into To C Level V1.4
Network Sage™ Into To C Level V1.4
ikirmer
Cloud computing sucess
Cloud computing sucess
Anwar Bakhashwain
Ugif 04 2011 france ug04042011-jroy_part1
Ugif 04 2011 france ug04042011-jroy_part1
UGIF
The Power of Two: Using IBM Standards Processing Engine for EDI Commerce or H...
The Power of Two: Using IBM Standards Processing Engine for EDI Commerce or H...
Brian Wilson
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...
Igor De Souza
Kognitio overview april 2013
Kognitio overview april 2013
Kognitio
Using a private cloud to automate and govern enterprise development
Using a private cloud to automate and govern enterprise development
WSO2
Journey to the Programmable Data Center
Journey to the Programmable Data Center
Toby Weiss
Analyze This! Best Practices For Big And Fast Data
Analyze This! Best Practices For Big And Fast Data
EMC
AWS Partner Presentation - PetaByte Scale Computing on Amazon EC2 with BigDat...
AWS Partner Presentation - PetaByte Scale Computing on Amazon EC2 with BigDat...
Amazon Web Services
Cloud nativecomputingtechnologysupportinghpc cognitiveworkflows
Cloud nativecomputingtechnologysupportinghpc cognitiveworkflows
Yong Feng
Konsolider, optimer og automatiser dit servermiljø med IBM PureApplications S...
Konsolider, optimer og automatiser dit servermiljø med IBM PureApplications S...
IBM Danmark
What is BI on Cloud
What is BI on Cloud
tdwiindia
An overview of modern scalable web development
An overview of modern scalable web development
Tung Nguyen
Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4
Shawn D'souza
Kognitio feb 2013
Kognitio feb 2013
Kognitio
Postgres Plus Cloud Database
Postgres Plus Cloud Database
Gary Carter
ADV Slides: Data Pipelines in the Enterprise and Comparison
ADV Slides: Data Pipelines in the Enterprise and Comparison
DATAVERSITY
Destroying Data Silos
Destroying Data Silos
DataWorks Summit
Semelhante a Business Process Insight - SRII 2012
(20)
How to get cloud architecture and design right the first time 2012
How to get cloud architecture and design right the first time 2012
Network Sage™ Into To C Level V1.4
Network Sage™ Into To C Level V1.4
Cloud computing sucess
Cloud computing sucess
Ugif 04 2011 france ug04042011-jroy_part1
Ugif 04 2011 france ug04042011-jroy_part1
The Power of Two: Using IBM Standards Processing Engine for EDI Commerce or H...
The Power of Two: Using IBM Standards Processing Engine for EDI Commerce or H...
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...
Kognitio overview april 2013
Kognitio overview april 2013
Using a private cloud to automate and govern enterprise development
Using a private cloud to automate and govern enterprise development
Journey to the Programmable Data Center
Journey to the Programmable Data Center
Analyze This! Best Practices For Big And Fast Data
Analyze This! Best Practices For Big And Fast Data
AWS Partner Presentation - PetaByte Scale Computing on Amazon EC2 with BigDat...
AWS Partner Presentation - PetaByte Scale Computing on Amazon EC2 with BigDat...
Cloud nativecomputingtechnologysupportinghpc cognitiveworkflows
Cloud nativecomputingtechnologysupportinghpc cognitiveworkflows
Konsolider, optimer og automatiser dit servermiljø med IBM PureApplications S...
Konsolider, optimer og automatiser dit servermiljø med IBM PureApplications S...
What is BI on Cloud
What is BI on Cloud
An overview of modern scalable web development
An overview of modern scalable web development
Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4
Kognitio feb 2013
Kognitio feb 2013
Postgres Plus Cloud Database
Postgres Plus Cloud Database
ADV Slides: Data Pipelines in the Enterprise and Comparison
ADV Slides: Data Pipelines in the Enterprise and Comparison
Destroying Data Silos
Destroying Data Silos
Último
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
Anna Loughnan Colquhoun
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
Michael W. Hawkins
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Delhi Call girls
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
gurkirankumar98700
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Delhi Call girls
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
Paola De la Torre
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
Pixlogix Infotech
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
BookNet Canada
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
naman860154
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Gabriella Davis
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
2toLead Limited
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
giselly40
Slack Application Development 101 Slides
Slack Application Development 101 Slides
praypatel2
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
soniya singh
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Alan Dix
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
Delhi Call girls
Último
(20)
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
Slack Application Development 101 Slides
Slack Application Development 101 Slides
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
Business Process Insight - SRII 2012
1.
Szabolcs Rozsnyai July 2012 Business
Process Insight An Approach and Platform for the Discovery and Analysis of End-to-End Business Processes Szabolcs Rozsnyai, Geetika T. Lakshmanan, Vinod Muthusamy, Rania Khalaf and Matthew J. Duftler © 2009 IBM Corporation
2.
IBM Presentation Template
Full Version Agenda Introduction and Motivation BPI Life-Cycle Architecture Research Challenges Conclusion and Future Work Source: If applicable, describe source origin 2 © 2009 IBM Corporation
3.
Introduction and Motivation
1/2 Understanding, managing and improving business processes in complex environments proves to be a significant challenge and has a severe impact on the organizations process maturity Organizational Challenges Technical Challenges •Business processes • Processes are not coordinated by one entity • can stretch across complex organizational silos and in • Systems are loosely coupled, heterogeneous and distributed many cases even extends to customers • Business Process artifacts range from simple record entries to • are not necessarily complete or accurate complex events at various granularity levels • are heavily human-driven, require a lot of knowledge • Business Process activities might be represented through and have a large number of exceptions multiple events • Are simplified to preserve a high degree of freedom • Sometimes workflow engines emit events to mark the • Are often in the heads of individuals, groups or buried start and end of an activity in application logic 3 © 2009 IBM Corporation
4.
Introduction and Motivation
2/2 We propose a system to enable Process Intelligence from two perspectives – Analytics on historical data • to understand what, how, who and why aspects of end-to-end business process based on real-time and historical data • identify root causes of problems, • understand process deficiencies and • provides means to improve process performance – Analytics on real-time data • to increase the effectiveness of business operations, and managing operational risk • to identify and predict situations in order to react on them BPI platform is a software as a service (SaaS) enabled, collaborative system that realizes the end-to-end BPI life-cycle. Process Intelligence BI BAM CEP BPM Process Mining The platform allows users to manage a variety of data at different levels of granularity including raw captured events, correlated instance traces, mined process models, and prediction alerts. 4 © 2009 IBM Corporation
5.
BPI Life-Cycle 5
© 2009 IBM Corporation
6.
Architecture Overview 6
© 2009 IBM Corporation
7.
Architecture – Data
Management • Volume and the complexity makes tracking and processing a difficult and resource intensive task • As data grows at a very high rate, tracking arbitrary artifacts for provenance purposes within large organizations is very costly • Storing, organizing, retrieving and analyzing the artifacts necessitate allocating large amount of computing resources • RDBMS requires trade-offs need to be made between the amount of captured data and the granularity levels • Aggregation vs. leaving out data both impact the potential for analytics 7 © 2009 IBM Corporation
8.
Architecture – Data
Management • Cloud-based elastic storage (Hadoop/HBase) • Distributed column-oriented key-value storage • NoSQL but BPI API supports • a limited set of queries • Joins with constraint that has high selectivity • Secondary indexing • Allows to compose annotated graphs of relationships 8 © 2009 IBM Corporation
9.
Architecture – Data
Integration • Schema-less structure easily allows • to “dump” everything into data storage • following a LET (Load Extract Transform) paradigm in contrast to classical ETL approaches • RAW data is preserved • Attributes of interest are extracted based on deployed and configured transformers • Integration options: • Using ESB (especially for real-time processing) • Loading files that are following a defined XML schema 9 © 2009 IBM Corporation
10.
Architecture – Correlation
Module • Correlation Discovery • Determines correlation rules that express how certain events are related to each other by combining a unique combination of statistics on event attributes • Applies graph reduction algorithms to reduce the number of correlation rules OrderToShipment : OrderReceived.OrderId = ShipmentCreated.OrderId, ShipmentCreated.ShipmentId = TransportStarted.ShipmentId, TransportStarted.TransportId = TransportEnded.TransportId How can I reduce the complexity for rules? 10 © 2009 IBM Corporation
11.
Architecture – Correlation
Engine • Higher level aggregations can be created that include several lower level aggregation nodes using representation of correlations. • Statistics can be calculated over correlated events and updated every time new events enter a correlation • User can place queries for aggregates and drill-down based on his interests © 2009 IBM Corporation
12.
Architecture – Process
Aware Analytics • Pluggable analytics module for • Process mining • Process comparison • Predictive analytics • Process Mining • Algorithms can be plugged in (Alpha, Heuristics, Biased, …) • Results are transformed to a BPMN representation • Queries can be applied to mine subsets of traces to observe variations in the behavior © 2009 IBM Corporation
13.
Architecture – Process
Aware Analytics • Process Comparison • Tree-Based comparison returns a detailed diff-list of the process model • Visual Overlay returns a visual representation of how process models differ from each other © 2009 IBM Corporation
14.
Architecture – Process
Aware Analytics • Predictive Analytics • algorithms in BPI currently include decision trees and an instance-specific probabilistic process model © 2009 IBM Corporation
15.
Research Challenges BPI addresses
several key challenges defined by the process mining manifesto *) C1 Finding, Merging, and Cleaning Event Data C4 - Dealing with Concept Drift When extracting event data suitable for process mining The process may be changing while being analyzed. several challenges need to be addressed: Understanding such phenomena is of prime importance for the management of processes. • data may be distributed over a variety of sources, • event data may be incomplete, • an event log may contain outliers and • events at different level of granularity. C2 Dealing with Complex Event Logs Having Diverse C7 - Cross-Organizational Mining Characteristics Event logs may be extremely large making them difficult to Some organizations work together to handle process handle whereas other event logs are so small that not enough instances (e.g., supply chain partners) or organizations data is available to make reliable conclusions. are executing essentially the same process while sharing experiences, knowledge, or a common infrastructure. The analysis of event logs originating from multiple organizations provides several challenges. C8 - Providing Operational Support Process mining is not restricted to off-line analysis and can also be used for online operational support. Three operational support activities can be identified: detect, predict, and recommend.. 15 © 2009 IBM Corporation
16.
Future Work Scale
vs. Query Expressiveness – Data management scales out on cost of query expressiveness • Experiments with relational-cloud hybrid models Parallelizing algorithms to scale-out 16 © 2009 IBM Corporation
17.
Thank You 17
© 2009 IBM Corporation