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Process Mining in Data mining

Process Mining in Data mining

$296.00
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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Course access is prepared after purchase and delivered via email
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What does the Process Mining in Data mining course cover?

Process Mining in Data mining is covered here in 9 modules: Foundations of Process Mining in Enterprise Contexts, Event Log Extraction and Preprocessing, Process Discovery Techniques and Model Construction and 6 more. The outline lists 72 specific topics, opening with selecting event log sources from heterogeneous systems such as ERP, CRM, and BPM platforms based on data availability and business criticality and.

How do you approach Process Mining in Data mining step by step?

The work is sequenced in 9 stages. It starts with Foundations of Process Mining in Enterprise Contexts, moves through Event Log Extraction and Preprocessing and Process Discovery Techniques and Model Construction, and ends at Governance, Ethics, and Change Management. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Process Mining in Data mining course?

Module 1 is Foundations of Process Mining in Enterprise Contexts. It works through selecting event log sources from heterogeneous systems such as ERP, CRM, and BPM platforms based on data availability and business criticality, mapping organizational process ownership to ensure alignment between technical analysis and business stakeholder responsibilities, defining scope boundaries for process mining initiatives to avoid overreach into unrelated workflows and.

How is the Process Mining in Data mining course delivered?

The Process Mining in Data mining course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Process Mining in Data mining course cost?

The Process Mining in Data mining course is $296 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Process Mining Toolkit, Process Mining in Business Process Redesign, Latent Process in Data mining, Process Combination in Data mining.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the technical, organizational, and governance dimensions of process mining with a scope comparable to a multi-workshop program embedded within an ongoing internal capability build, addressing real-world challenges from log extraction and model validation to ethical deployment and integration with business process management.

Module 1: Foundations of Process Mining in Enterprise Contexts

  • Selecting event log sources from heterogeneous systems such as ERP, CRM, and BPM platforms based on data availability and business criticality
  • Mapping organizational process ownership to ensure alignment between technical analysis and business stakeholder responsibilities
  • Defining scope boundaries for process mining initiatives to avoid overreach into unrelated workflows
  • Assessing maturity of existing process documentation to determine baseline for conformance checking
  • Establishing data governance policies for handling personally identifiable information in event logs
  • Choosing between on-premise and cloud-based process mining tools based on data residency and compliance requirements
  • Integrating process mining initiatives with existing data warehouse architectures and ETL pipelines
  • Documenting assumptions about timestamps, activity names, and case identifiers during initial data intake

Module 2: Event Log Extraction and Preprocessing

  • Designing SQL queries to extract event logs from transactional databases without impacting production system performance
  • Resolving inconsistent case identifiers caused by system migrations or data merging across business units
  • Handling missing or malformed timestamps due to system downtime or logging errors
  • Normalizing activity names across systems where synonyms describe the same business action
  • Filtering out test, bot, or administrative transactions from raw event logs to prevent process model distortion
  • Deciding on granularity level for activities (e.g., screen-level vs. transaction-level) based on analysis goals
  • Implementing incremental log updates to support continuous process monitoring
  • Validating completeness of event logs against known process volumes and durations

Module 3: Process Discovery Techniques and Model Construction

  • Selecting between Alpha, Heuristic, and Inductive miners based on log complexity and desired model accuracy
  • Adjusting noise thresholds in discovery algorithms to balance model simplicity and real-world variability
  • Interpreting directly-follows graphs to identify high-frequency and low-frequency paths
  • Handling invisible or skipped tasks in discovered models when logs lack full observability
  • Deciding when to split a monolithic process model into subprocesses based on functional or organizational boundaries
  • Validating discovered models with subject matter experts using playback techniques and scenario testing
  • Managing computational load when processing large-scale logs with billions of events
  • Documenting model limitations due to incomplete or biased data coverage

Module 4: Conformance Checking and Deviation Analysis

  • Choosing between token-based replay and alignment-based conformance techniques based on performance and precision needs
  • Quantifying deviation severity by linking non-conforming paths to compliance, cost, or risk impact
  • Identifying root causes of frequent deviations through correlation with organizational units or system configurations
  • Handling cases where the normative model is outdated or does not reflect actual practice
  • Configuring tolerance levels for acceptable deviations in high-variability processes
  • Integrating conformance results into audit reporting frameworks for regulatory compliance
  • Mapping deviations to control weaknesses in SOX, GDPR, or ISO 9001 contexts
  • Automating alerts for real-time detection of critical non-conformances

Module 5: Performance and Bottleneck Analysis

  • Calculating cycle times per activity and path while accounting for parallel execution and rework loops
  • Distinguishing between system-induced delays and human-induced waiting times in timestamp analysis
  • Identifying resource bottlenecks by correlating workload distribution with processing times
  • Adjusting time calculations for time zones, holidays, and non-working hours in global processes
  • Visualizing throughput times using heatmaps and histograms to communicate bottlenecks to stakeholders
  • Setting performance baselines before process improvement initiatives for impact measurement
  • Attributing delays to specific organizational units or handover points in cross-functional processes
  • Validating performance metrics against operational SLAs and service contracts

Module 6: Organizational and Social Network Analysis

  • Extracting role-based patterns from resource assignments to identify de facto organizational structures
  • Detecting shadow workflows where informal teams handle cases outside official procedures
  • Measuring workload imbalance across employees or departments using case volume and duration metrics
  • Identifying key performers or bottlenecks based on centrality measures in social network graphs
  • Assessing compliance with segregation of duties policies using co-occurrence analysis of resource actions
  • Mapping actual collaboration patterns against formal reporting lines for change management planning
  • Handling anonymized resource data in compliance with privacy regulations while preserving analytical value
  • Monitoring changes in collaboration networks after organizational restructuring or system changes

Module 7: Integration with Business Process Management

  • Translating process mining findings into executable BPMN models for workflow automation
  • Aligning process variants with configurable process templates in case handling systems
  • Feeding performance metrics into process KPI dashboards and operational control rooms
  • Using root cause analysis from mining outputs to prioritize process improvement projects
  • Embedding process mining checks into continuous process improvement (CPI) cycles
  • Coordinating with BPM teams to ensure discovered models are updated post-optimization
  • Defining triggers for re-mining cycles based on system changes or performance degradation
  • Linking process variants to customer segments or product types for targeted optimization

Module 8: Advanced Analytics and Predictive Process Monitoring

  • Designing features from event logs for predicting case outcomes such as delays or rejections
  • Selecting machine learning models (e.g., random forests, LSTM) based on prediction horizon and data sparsity
  • Handling concept drift in predictive models due to process changes or policy updates
  • Implementing real-time prediction services for active case routing or intervention
  • Calibrating confidence thresholds to minimize false positives in alert systems
  • Validating predictive accuracy using historical backtesting on time-separated datasets
  • Integrating predictions into case management interfaces for operational decision support
  • Monitoring model performance decay and scheduling retraining intervals

Module 9: Governance, Ethics, and Change Management

  • Establishing data access controls for event logs containing sensitive operational or personal data
  • Designing audit trails for process mining activities to meet internal control standards
  • Communicating findings to employees without inducing fear of surveillance or performance penalties
  • Obtaining legal and data protection approvals for cross-border log transfers
  • Managing resistance from middle management when mining reveals inefficiencies or non-compliance
  • Documenting ethical guidelines for using process insights in workforce planning or evaluation
  • Creating feedback loops so frontline staff can explain anomalies in the data
  • Aligning process mining initiatives with corporate sustainability and digital transformation strategies
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