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Making Workflows Work

Making Workflows Work. Prof. Yike Guo Dept. of Computing Imperial College London InforSense Limited. DiscoveryNet Project. Funding One of the Eight UK National e-Science Projects (£2.2 M) Sept 2001 – March 2005 Partners Achievements

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Making Workflows Work

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  1. Making Workflows Work Prof. Yike Guo Dept. of Computing Imperial College London InforSense Limited

  2. DiscoveryNet Project • Funding • One of the Eight UK National e-Science Projects (£2.2 M) • Sept 2001 – March 2005 • Partners • Achievements • Constructing the World’s First Infrastructure for Building Analytical Services by Scientists • For the First time Discovery Net Realises the Dynamic Construction of Compositional Services on GRID for Real Time Knowledge Discovery and Decision Making • Outputs • Software Research: DNet platform commercialized by InforSense Ltd (>100 customers) • Total user numbers > 2000 • Applications Research: Application out • puts in sensor technology commercialized by deltaDot Ltd • Number papers published: 10 Journal Papers, 30 Conference Papers • 8 PhD completed and 50 Master students • Ranked OUTSTANDING at the project final review Proprietary and Confidential

  3. 100+ customers (70% Fortune 200 companies) InforSenseIntroduction • 2006 3rd fastest growing company in UK (Sunday Times Tech Track) • 2007 8th fastest growing venture-based company in UK (Financial Times) • Global footprint with offices in London (HQ and R/D), Boston (USA HQ) and Shanghai (Asia HQ and Development base) • Global sales with 70% outside of Europe • 7 years of delivering products and services to pharmaceutical and Financial industries • Spin out from Imperial College London Innovation in Embedding Analytics InforSense Formed IntroducedKDE Analytics Platform Embedding Analytics Technology 3rd fast growing company in UK IEEE Super-computing Award –Grid based analytics ‘00 ,03 ‘05 Discovery Net Project Invented “Distributed Data Mining ” Embedding Analytics in Major Enterprise Systems First Enterprise Deployment ‘98 ‘01 2004 ‘06 Proprietary and Confidential

  4. Those who are using our workflow CAMBRIA BIOSCIENCES Proprietary and Confidential

  5. Files Portal / Dashboard InforSense Workflow Methodology Application Delivery to End User Business Process Administrator Clinician Disease Biologist Rapid Application Deployment Integrative Analytics Workflow Environment Automation & Scheduling Interactive Solution Building Interactive Knowledge Discovery Dynamic Data & App Integration Data Applications Components Oracle Pre-processing 3rd party Analytics Web services Biomedical Informatics tools Multiple data sources InforSense Analytics EMR Databases Excel Proprietary and Confidential

  6. Chem-Studio ADMET Browser What is InforSense WF System Designed for ? • InforSense workflow system is not an application but a framework to build and deliver applications directly to scientist/business user: Proprietary and Confidential

  7. Simulation & Modelling Pipelining InforSense Generic Workflow Engine Business Process Managenment Web Service Orchestration ETL Data/Text Mining Enterprise Service Bus Proprietary and Confidential

  8. Experience of 7 years in WF business • Building workflow is easy ! • However, • Building a USABLE workflow is not easy • Building a REUSABLE workflow is hard • Building a REUSABLE workflow applications is very hard • Building a REUSABLE workflow application for EVERYONE is very very hard • Building a function is easy, building an application is hard, it is even harder if we enable a non-IT person to build a good reliable application for other people to use everyday! Proprietary and Confidential

  9. Workflow Deployment: Building Reusable WF Applications Native MPI OGSA-service Condor-G Web Service Web Wrapper Sun Grid Engine Unicore Oralce 10g InforSense Workflow System Development Resource Mapping Workflow Embedding Pervasive WF applications Workflow Execution Reliable Enterprise Wide Execution Workflow Authoring Composing services Workflow Warehousing Service Abstraction Workflow Management Collaborative Knowledge Management Proprietary and Confidential

  10. Three Tiers of Workflow Framework Service Orchestration Embedding Layer Business Rules Embed in Other Applications BPEL Analytic Service Encapsulation Application Layer Publish Services for Display Analytical Workflow Development Building Layer Rapid Application Development Proprietary and Confidential

  11. InforSense Workflow Building:Not about another graph notation but about how to build a meaningful graph

  12. Current model of workflow authoring/execution • No help provided to user (authoring/execution) • Model is based on expert user who know about services • Model requires user to be trained in a workflow language/system • Interoperability between workflow systems is only at run-time Proprietary and Confidential

  13. The key the success : End User Oriented Workflow Construction • Build semi-automatic tools that advise/assist user in wf authoring • Make use of previous knowledge about developing workflows • Explicit/Expert knowledge • Implicit knowledge in previous workflows The aim is to help user, not replace him Proprietary and Confidential

  14. Guided Workflow Construction • User is presented by high-level descriptions of predefined task steps • User is guided iteratively in instantiating the task descriptions using workflow templates • User can retrieve workflows and workflow templates from repository • Approach supports using workflows from multiple systems using existing run-time interoperability mechanisms Proprietary and Confidential

  15. Workflow Advisor: InforSense Customer Hubs Proprietary and Confidential

  16. Extended infrastructure:Workflow warehousing and mining • Workflow Advisor • Initial implementations of prototype for bio applications • Workflow Assistant • Abstract component initial prototypes • Workflow Mining • Repository of workflows from Southampton • Workflow Annotations • independent from workflow language • Warehouse • Search and execute web services/Grid services and workflows • Syntactic and semantic search Proprietary and Confidential

  17. Extended infrastructure: Workflow warehouse/registry Proprietary and Confidential

  18. InforSense Embedding and DeploymentWorkflow output is not a data, but an application/service

  19. InforSense KDE Deployment Strategies Deploy workflows to InforSense portal • Deployment features: multi-page, service chain, layout editor • Multi-stage applications: group workflows into stages Component based deployment • Portlet based deployment • Portlet component: JSR 168 compatible portlet components Business process workflow • Based on control flow orchestrated workflows and role based deployment Proprietary and Confidential

  20. Portal Container allows users to build dashboards Each Workflow generate data for a dashboard component Workflow results viewed in simple charts - can be linked to other pages Web-based Deployment Proprietary and Confidential

  21. Deployment Features (2) Define multiple pages Move to next page Proprietary and Confidential

  22. Analytical stage Example Application Chip QC Normalise Analyse Interpret • Design Experiment • Design Study groups for transcriptomics portal • Gene Expression Profiling • Pre-process and Analyse the results of a gene expression analysis to compare control vs. test populations • Splice Variance Analysis • Pre-process and Analyse the results of an Exon Chip to find differences in splice variance between control vs. test populations Results Submit to Report> Next Steps • Normalisation services • RMA (recommended) • LiWong • ETC Workflow configured to group according to stage Portal look and feel can be customized by style sheet Proprietary and Confidential

  23. Example Application Chip QC Normalise Analyse Interpret • Design Experiment • Design Study groups for transcriptomics portal • Gene Expression Profiling • Pre-process and Analyse the results of a gene expression analysis to compare control vs. test populations • Splice Variance Analysis • Pre-process and Analyse the results of an Exon Chip to find differences in splice variance between control vs. test populations Results Submit to Report> Next Steps • Analysis services • Volcano Plot (recommended) • PCA • Dendrogram Proprietary and Confidential

  24. Example Application Chip QC Normalise Analyse Interpret • Design Experiment • Design Study groups for transcriptomics portal • Gene Expression Profiling • Pre-process and Analyse the results of a gene expression analysis to compare control vs. test populations • Splice Variance Analysis • Pre-process and Analyse the results of an Exon Chip to find differences in splice variance between control vs. test populations Results Submit to Report> Save Result to Report Next Steps • Analysis services • Select Transcripts • Filter Data Proprietary and Confidential

  25. Example Application Chip QC Normalise Analyse Interpret • Design Experiment • Design Study groups for transcriptomics portal • Gene Expression Profiling • Pre-process and Analyse the results of a gene expression analysis to compare control vs. test populations • Splice Variance Analysis • Pre-process and Analyse the results of an Exon Chip to find differences in splice variance between control vs. test populations Results Submit to Report> Save Selected Items to Report Next Steps • Interpretation services • Send Data to Ingenuity • Send Data to Gene Go • Send Data to • Text Analysis Proprietary and Confidential

  26. Example Application Chip QC Normalise Analyse Interpret • Design Experiment • Design Study groups for transcriptomics portal • Gene Expression Profiling • Pre-process and Analyse the results of a gene expression analysis to compare control vs. test populations • Splice Variance Analysis • Pre-process and Analyse the results of an Exon Chip to find differences in splice variance between control vs. test populations Results Submit to Report> Save to Report Next Steps • Interpretation services • Send Data to Gene Go • Text Analysis Proprietary and Confidential

  27. Example Application Chip QC Normalise Analyse Interpret • Design Experiment • Design Study groups for transcriptomics portal • Gene Expression Profiling • Pre-process and Analyse the results of a gene expression analysis to compare control vs. test populations • Splice Variance Analysis • Pre-process and Analyse the results of an Exon Chip to find differences in splice variance between control vs. test populations Results Submit to Report> Select Subset for Text Analysis Next Steps • Interpretation services • Send Data to Gene Go • Text Analysis Proprietary and Confidential

  28. Business Process Workflow (1) Business Process Management Development • A Business Process Management (BPM) describes the orchestration of different tasks to complete a specific business objective • Business Processes need to orchestrate • Automated Tasks • User Tasks • Exception Handling • Running Tasks in parallel • Synchronisation of parallel tasks Proprietary and Confidential

  29. Run Task Initiate Parallel Tasks Apply Rules Synchronize Parallel Tasks Handle Exceptions Business Process Workflow (2) InforSense Control Flow • InforSense Control Flow for Orchestrating Workflows for Business Process Proprietary and Confidential

  30. Business Process Workflow (3) Control Flow Represents a Business Process Orchestra business analytics by control flow Deploy to Portal Process Building Blocks definition of linkage/control and user interactions Sub-process 1 Sub-process 2 ApplicationBuilding Blocks services Workflow A Workflow B Workflow C Proprietary and Confidential

  31. Workflow interoperabilityWorkflows and business processes (BPEL) Proprietary and Confidential

  32. Get Value Score Get Churn Score Risk Assessment Acceptable Risk? No Yes Normal Service Upgrade offer Model Repository Embedding Workflow Analytics into Applications Process View Analytical Workflows Embeddable Analytic Applications customer data Churn Service Predictive scores Lifetime Value Service Risk data Risk Service Risk Evaluation KVM Deploy New Actions Business Rules and Model Proprietary and Confidential

  33. Enterprise Services Bus Business Portal Business Process Integrating Analytics with Business Rules: Adaptive Business Process Rule engine Analytics to drive adaptive processes Rule Engine Business operational data Proprietary and Confidential

  34. Embedding with Applications InforSense Tools as one item in Windows based application system Proprietary and Confidential

  35. Proprietary and Confidential

  36. Making Workflow Work “One of the biggest barriers to achieving productivity and responsiveness is IT – it has become a bottleneck. Another barrier to achieving the goal is the lack of intelligence that drives most IT applications. They are just operating as a rapid functional replacement, and failing to exploit the data which is being generated within other elements of the IT infrastructure. A product that could meet that challenge and enable business to generate and deploy intelligence with speed, accuracy and without the need for specialized skills would be remarkable. I believe that InforSense is that remarkable tool.” -- David Norris, Senior Analyst, Bloor Research Proprietary and Confidential

  37. Thank You ! Proprietary and Confidential

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