IEEE TSC Special Issue: Processes meet Big Data

The aim of process mining is to discover, monitor and improve business processes by extracting knowledge from event logs readily available in today’s information systems. Process monitoring and analysis has enjoyed a tremendous growth and a rapid development at both conceptual and algorithmic levels. In particular, there have been successful realizations of process monitoring systems in many application areas, including manufacturing, e-health and e-government. Today, the current trend toward large-scale collaborative processes featuring thousands of elementary activities per minute is generating a number of new research issues. When large-scale processes are executed on (cloud-based) service-oriented environments or even on the global Net, elementary activities can be mapped to fine or coarse-grained protocol events and process logs increasingly come to show all typical properties of “big data”: wide physical distribution, diversity of formats, non-standard data models, heterogeneous semantics. Computing metrics over such “big logs” also requires to handle security and privacy concerns of many participants, and even to deal with non-uniform trustworthiness of log entries. New techniques are therefore required for designing, validating and deploying process metrics in this scenario, as well as for effectively dash-boarding the processes’ performance indicators.

This special issue of IEEE Transaction on Service-Oriented Computing is intended to create an international forum for presenting innovative developments of process monitoring and analysis over service-oriented architectures, aimed at handling “big logs” and use them effectively for discovery, dash-boarding and mining. The ultimate objective is to identify the most promising research avenues, report the main results and promote the visibility and relevance of this new area.

Special sessions this year:


  • Process monitoring on SOA and clouds
  • Validation and benchmarking of process monitoring
  • Efficiently mining rare patterns in “big logs”
  • Scalable techniques for distributed process monitoring
  • Monitoring and analysis of cloud-based processes
  • Architectures and data models for synthesizing and handling “big logs”
  • Privacy-aware computation of process metrics
  • Log obfuscation and access control
  • Practical systems and tools for big log analysis and log dashboards