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Chapter 10 Tool Support prof.dr.ir. Wil van der Aalst www.processmining.org Overview Chapter 1 Introduction Part I: Preliminaries Chapter 2 Process Modeling and Analysis Chapter 3 Data Mining Part II: From Event Logs to Process Models Chapter 4 Getting the Data Chapter 5 Process Discovery: An Introduction Chapter 6 Advanced Process Discovery Techniques Part III: Beyond Process Discovery Chapter 7 Conformance Checking Chapter 8 Mining Additional Perspectives Chapter 9 Operational Support Part IV: Putting Process Mining to Work Chapter 10 Tool Support Chapter 11 Analyzing “Lasagna Processes” Chapter 12 Analyzing “Spaghetti Processes” Part V: Reflection Chapter 13 Cartography and Navigation Chapter 14 Epilogue PAGE 1 Business Intelligence? • “BI is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision making” • Examples of products: IBM Cognos Business Intelligence (IBM), Oracle Business Intelligence (Oracle), SAP BusinessObjects (SAP), WebFOCUS (Information Builders), MS SQL Server (Microsoft), MicroStrategy (MicroStrategy), NovaView (Panorama Software), QlikView (QlikTech), SAS Enterprise Business Intelligence (SAS), TIBCO Spotfire Analytics (TIBCO), Jaspersoft (Jaspersoft), and Pentaho BI Suite (Pentaho). PAGE 2 Typical functionality ETL (Extract, Transform, and Load). Ad-hoc querying. Reporting. Interactive dashboards Alert generation. all iPhone 4G sales in region West in the fourth quarter of 2011 iPhone 4G gi o re by le s sa sales by quarter t es Q1 Q2 Q3 Q4 h ut So iPod classic n Ea th or t N s iPod nano W Three dimensional OLAP cube containing sales data. Each cell refers to all sales of a particular product in a particular region and in a particular period. For each cell the BI product can compute metrics such as the number of items sold or the total value. sales by product • • • • • PAGE 3 Example: Pentaho www.pentaho.com PAGE 4 Business Unintelligence • No real process orientation. • Only simple views on event data. • Focus on reporting and monitoring of KPIs. Data mining ≠ process mining • Data mining tools provide more “intelligent functionality” than BI tools, but are also not processcentric. • See for example WEKA (Waikato Environment for Knowledge Analysis, weka.wikispaces.com) and R (www.r-project.org). PAGE 5 ProM • www.processmining.org • ProM supports all of the techniques mentioned in book and on slides! • Pluggable architecture. • Major differences between ProM 5.2 (and earlier) and ProM 6. PAGE 6 Screenshot of ProM 5.2 PAGE 7 Screenshot of ProM 6 (based on handover of work) PAGE 8 ProM 6: α miner PAGE 9 ProM 6: Social network analyzer PAGE 10 Example plug-ins in ProM 6 (see book and website) PAGE 11 Some process mining tools PAGE 12 Futura Reflect (process view) (also embedded in BPM|one) PAGE 13 Futura Reflect (social network) PAGE 14 Loading and converting event logs • XESame, Nitro, ProMimport PAGE 15