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Dolev Mezebovsky, Pnina Soffer, and Ilan Shimshoni BPMDS, Amsterdam, June 2009.

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Presentation on theme: "Dolev Mezebovsky, Pnina Soffer, and Ilan Shimshoni BPMDS, Amsterdam, June 2009."— Presentation transcript:

1 Dolev Mezebovsky, Pnina Soffer, and Ilan Shimshoni BPMDS, Amsterdam, June 2009

2  The implementation of enterprise systems is often a driver for business process change. ◦ System implementation as an opportunity for redesigning business processes ◦ Changes motivated by the need to adapt the enterprise to the system rather then the other way around  “Vanilla” implementations: ◦ Implement basic functionality without modifications and make improvements afterwards ◦ Cases of partial support to existing processes – people are forced to make workarounds and work inefficiently for the process to achieve its goal.

3  Process: change a student’s study program Before implementationAfter implementation 1.The secretary reports the change. 2.Acquired credits are automatically transformed to the new program. 1. The secretary reports the change. 2. She prints a report of acquired credits 3. For every course, she detaches it from the old program and attaches it to the new one. Total time: 1-2 minutesTotal time: up to 20 minutes Error freeError prone

4  Many such cases may exist in an organization  At first: all users complain  With time, some users may get used to the inefficient way of working  The question: How to identify the inefficient processes and prioritize their improvement?

5  The cases we are looking for include some repetition of a set of operations, as part of one “logical” task  These situations should be reflected in the event log of the system  Solution approach: mine for recurrent patterns of operations

6 Row Num OperationDateTimeUser Name Student Name Course NameProgram Name 1 Attach Course15.06.0813:45:52 YPRESS FredrickLinear AlgebraMIS Major 2 Attach Course15.06.0813:46:26 YPRESS FredrickAlgorithmsMIS Major 3 Attach Course15.06.0813:47:44 YPRESS FredrickData StructuresMIS Major 4 Detach Course15.06.0813:49:18 YPRESS FredrickLinear AlgebraCS Minor 5 Detach Course15.06.0813:49:24 YPRESS FredrickAlgorithmsCS Minor 6 Detach Course15.06.0813:49:31 YPRESS FredrickData StructuresCS Minor 7 Attach Course15.06.0813:54:19 YPRESS FredrickIntroduction to ITMIS Major 8 Detach Course15.06.0813:58:20 YPRESS FredrickBusiness Intelligence MIS Minor 9 Attach Course15.06.0813:59:35 YPRESS FredrickProgrammingMIS Major 10 Detach Course15.06.0814:01:29 YPRESS FredrickProgrammingMIS Minor


8  Log entry= ◦ ORSO: an ordered set of operands ◦ Example: YPRESS, 13:50, Detach course, Fredrick, Linear Algebra, CS Minor.  For two entries in a log: ◦ Invariant set: set of entry elements whose values are equal for the two entries ◦ Variant set: set of entry elements whose values are different for the two entries

9  Two entries are potentially in the same pattern if: ◦ User  {Invariant} ◦ Timestamp  {Variant}; |TS(1)-TS(2)| < Timeframe ◦ {Operation, ORSO}  {Invariant}  ◦ {Operation, ORSO}  {Variant}   Potential pattern entry:  The algorithm dynamically aggregates entries into potential pattern entries, seeking for largest possible patterns.

10  [(1),(2)] = [(1):, (2): ] ◦ (1, 2) :  Second iteration:  [(1, 2), (3)] = [(1, 2) :, (3): ] ◦ (1, 2, 3):

11  Pattern type definition:.  I: a set of invariant element types (Operation, operand type)  V: a set of variant element types (Operation, operand type)  Example: ◦ I = {Operation, Student, Program} ◦ V = {Course}

12  The count C P of a pattern type P: the number of patterns of this type in the log file.  The average size AS P of a pattern type P: the average number of entries in patterns of type P. Let P occur C P times in a log file, so occurrence i includes n i entries. Then:  The average time AT P of a pattern type p: the average time range (difference between the maximal and minimal timestamps) in patterns of type p..

13  Find out which of the identified patterns reflects inefficient processes ◦ By interviewing users  Prioritize patterns to be automated ◦ By size-weighted count: SC P = AS P *C P ◦ By time-weighted count: TC P = AT P *C P

14  We address a situation where technology drives processes in an undesirable way  We utilize mining technology to identify and prioritize requirements for automating inefficient processes.  Our solution identifies recurrent patterns in the system log and provides metrics for prioritization.

15  Finalize the overall algorithm  Experiment with the university log to evaluate the proposed method ◦ Is it capable of identifying patterns that are a-priori known? ◦ Ratio of real problems identified vs. patterns that reflect “normal” processes ◦ Sensitivity to the timeframe parameter  Experiment with logs from other domains

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