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Business Process Insight - SRII 2012

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Business Process Insight - SRII 2012

  1. 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. 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. 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. 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. 5. BPI Life-Cycle 5 © 2009 IBM Corporation
  6. 6. Architecture Overview 6 © 2009 IBM Corporation
  7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. Thank You 17 © 2009 IBM Corporation

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