Detecting Opportunities and Threats with Complex Event Processing: Case Studies in Predictive Customer Interaction Management and Fraud Detection, February 27, 2007 FINAL DRAFT 2, 8th Annual Japan\'s International Banking & Securities System Forum, Tim Bass, CISSP, Principal Global Architect, Director
WSO2Con USA 2017: Scalable Real-time Complex Event Processing at Uber
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Detecting Opportunities and Threats with Complex Event Processing: Case Studies in Predictive Customer Interaction Management and Fraud Detection
1. Detecting Opportunities and Threats with Complex Event Processing: Case Studies in Predictive Customer Interaction Management and Fraud Detection February 27, 2007 FINAL DRAFT 2 8th Annual Japan's International Banking & Securities System Forum Tim Bass, CISSP Principal Global Architect, Director
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6. CEP Brings Two Kinds Of Business Benefits Ref: Roy Schulte, Gartner, First Event Processing Symposium, 2006 2. Complex-Event Processing (CEP) for Earlier and Better Insight Order Entry Manufacturing Shipping 1. Event-Driven Architecture (EDA) for Flexibility and Maintainability
9. Reference Architecture An Enterprise View of Complex Event Processing 24 EVENT PRE-PROCESSING EVENT SOURCES EXTERNAL . . . LEVEL ONE EVENT TRACKING Visualization, BAM, User Interaction CEP Reference Architecture DB MANAGEMENT Historical Data Profiles & Patterns DISTRIBUTED LOCAL EVENT SERVICES . . EVENT PROFILES . . DATA BASES . . OTHER DATA LEVEL TWO SITUATION DETECTION LEVEL THREE PREDICTIVE ANALYSIS LEVEL FOUR ADAPTIVE BPM
10. Summary of Complex Event Processing Flexible SOA and Event-Driven Architecture
11. Bloor Report on Event Processing Event Processing and Decision Making Automated Operational Decisions Automated Predictive Decisions Human Predictive Decisions Human Operational Decisions Decision Latency Event Complexity Process Complexity Pattern Matching and Inferencing Anti-Money Laundering Credit-Card Fraud Exchange Compliance Database Monitoring Algorithmic Trading Trade Desk Monitoring Customer Interaction Order Routing RFID Tariff Look-Up Rail Networks Search & Rescue Baggage Handling Liquidity Management
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16. On-Line Fraud Detection Use Case Architecture and Capacity Planning Approx. 12,000 Hits Per Second During Peak Period Across the Three Sites – One Instance Of TIBCO BusinessEvents™ Capable of Handling Maximum Hits Overall 100 Million Hits Handled Between 3PM – 4 PM Peak Approx. 250 Million Hits Per Day Across the Three Sites Session Info Three Server Farms ~600-700 Application Servers
17. “ No Code” Custom User Interface Studio TIBCO’s Enterprise RTView™ or General Interface™
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24. Case Study: Bank Employee Interface TIBCO’s General Interface™ (AJAX Web Development)