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Discovering Social Networks from Event Logs

Discovering Social Networks from Event Logs. Wil M.P van der Aalst 1 , Hajo A. Reijers 1 , Minseok Song 2,1. Data Mining TIE 522 KIM, HYUNKI. Contents. ■ Introduction. ■ The concept of process mining. ■ Mining organizational relations. ■ Metrics. ■ MiSoN. ■ Case study.

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Discovering Social Networks from Event Logs

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  1. Discovering Social Networks from Event Logs WilM.P van der Aalst1, Hajo A. Reijers1, Minseok Song2,1 Data Mining TIE 522 KIM, HYUNKI

  2. Contents ■ Introduction ■ The concept of process mining ■ Mining organizational relations ■ Metrics ■ MiSoN ■ Case study ■ Conclusion

  3. Introduction ▣ Two concepts • Business Process Management(i.e., systems supporting the design, analysis, and enactment of operational business processes) • Sociometry: refered to as sociography, refers to methods presenting data on interpersonal relationships in graph or matrix form. ▣ SNA(Social Network Analysis) : refers to the collection of method, techniques and tools in sociometry aiming at the analysis of social networks. ▣ The main challenge is to derive social networks from this type of data such as today’s enterprise information systems. ▣ This paper presents the approach, the various metrics that can be used to build a social network, MiSoN(Mining Social Networks), and case study.

  4. Process mining Discovering social networks ▣ The goal of process mining is to extract information about processes from transaction logs. - An event log - - The sociogram based on the event log - ▣ Sociograms can be used as input for SNA tools that can visualize the network in various way.(compute metrics like the density of the network, analyze the role of an individual in the network, and identify cliques)

  5. Process mining Other types of mining ▣ α-algorithm - A process model based onα-algorithm - ▣ The presence of timing information and information on cases/activities allows for more advanced forms of process mining. ▣ Another interesting application of process mining is fraud detection.

  6. Mining organizational relations Social network analysis ▣ Motivations1. Existing systems record information about human activity 2. There is a wide variety of mature techniques and tools to analyze such sociograms. ▣ Two approaches 1. Sociocentric approach: consider interactions within a defined group and consider the group as a whole2. Egocentric approach: consider the network of an individual(relations among the friends of a given person). ▣ Graph notation • can be undirected or directed.• the relations may be binary or weighted. The weight is used to qualify the relation.

  7. Mining organizational relations Deriving relations from event logs ▣ How to derive meaningful sociograms from event log? ▣ Four types of metrics1. Metrics based on (possible) causality: monitor for individual cases how work moves among performers.(handover of work)2. Metris based on joint cases: ignore causal dependencies but simply count how frequently two individuals are performing activities for the same case.3. Metrics based on joint activities: do not consider how individuals work together on shared cases but focus on the activities they do.4. Metrics based on special event types: consider the type of event.

  8. Metrics ▣ Convenient notation for event logs • Definition 4.1. (Event log) A: a set of activities P: a set of performers E: a set of possible event(E = A x P) C: the set of possible event sequences(C = E) L∋В(C) is the set of all bags(multi-sets) over C• First, abstract from additional information such as time stamps, data, etc. • Second, do not consider the ordering of events corresponding to different cases.• For convenience, we define two operations on events: πa(e) = a and πp(e) = p for some event e = (a,p).

  9. Metrics Metrics based on (possible) causality ▣ Metrics based on causality take into account both handover of work and sub-contracting. ▣ Three kinds of refinements1. One can differentiate with respect to the degree of causality.(length of handover)2. We can ignore multiple transfers within one instance or not3. We can consider arbitrary transfers of work or only consider those where there is a casual dependency - A process model based onα-algorithm - :the function which returns true if within the context of case c performers p1 and p2 both executed some activity such that the distance between these two activities is n. :the function which returns the number of times in the case c. is the same as ?

  10. Metrics Metrics based on (possible) causality

  11. Metrics Metrics based on (possible) causality

  12. Metrics Metrics based on (possible) causality

  13. Metrics Metrics based on joint cases Metrics Metrics based on joint activities ▣ Measurement of the distance between two performers.• Minkowski distance  Hamming distance• Pearson’s correlation coefficient

  14. Metrics Metrics based on special event types ▣ This method considers event types such as schedule, assign, withdraw, reassign, start, suspend, resume, pi-abort, ate-abort, complete, autoskip, manualskip, and unknown. : the function which returns true if within the context of the case c performers p1 and p2 both executed the same activity and p1 was responsible for a specific type of event and p2 is the first performer of some event for the same activity : the function which returns the number of times in the case c.

  15. MiSoN(Mining Social Networks) ▣ MiSoN has been developed to discover relationships between individuals from a range of enterprise information systems including workflow management systems. ▣ MiSoN constructs sociograms that can be used as a starting point for SNA.

  16. MiSoN(Mining Social Networks) ▣ MiSoN• provides functionalities for displaying user statistics and event log statistics.• the user can select suitable metrics and set relevant options.• can export the mining result using the AGNA-translator.(AGNA: a SNA tool)• can export the mining result to other SNA tools like UCINET and NetMiner.

  17. Case study Type of event Matrix view - MiSoN screenshot of ProM-

  18. Case study ▣ The result from the handover of work.

  19. Case study ▣ The result from the working together – considering distance with causality.

  20. Case study ▣ The result from the similar task– considering Hamming distance.

  21. Conclusion ▣ This paper is to present an approach to discover sociograms. ▣ MiSoN can interface with commercial systems such as Staffware and standard SNA tools like AGNA and UCINET and NetMiner. ▣ Two kinds of limitations.1. Only mornitoring the events.2. Enforcing the interaction patterns. ▣ Future research1. We plan to repeat our analysis within the public works department and apply our approach in many other organizations as well.2. we also investigate extensions of the approach using filtering techniques and more advanced forms clustering.

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