Process Mining for Ubiquitous Mobile Systems An Overview and a Concrete Algorithm Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information and Technology P.O. Box 513, 5600 MB Eindhoven The Netherlands Joint work with Ana Karla Alves de Medeiros, Boudewijn van Dongen, and Ton Weijters.

Outline Motivation: Process mining in the context of UMSs Process mining –An overview –The alpha algorithm –The alpha+ algorithm Applications Conclusion

Motivation Human activities are increasingly supported by electronic tools. These tools are increasingly mobile and ubiquitous, cf. PDAs, Bluetooth, WLAN, smart clothes, etc. Tracing human behavior/processes will become easier, cf. RFID, GSM, etc.

Ubiquitous computing Reference: Alan Daniel, Georgia Institute of Technology. “Each person is continually interacting with hundreds of … interconnected computers” which ideally “weave themselves into the fabric of everyday life until they are indistinguishable from it” Mark Weiser 1991/1993

Assumptions Increasingly information systems are composed of autonomous components/agents/… thus allowing for more flexibility and mobility. –This will trigger the need for monitoring processes/human behavior. Information systems will be ubiquitous. –This will allow for the collection of event data. Ubiquitous Mobile Systems (UMS) Process Mining (PM)

Process mining Process mining can be used for: –Process discovery (What is the process?) –Delta analysis (Are we doing what was specified?) –Performance analysis (How can we improve?) process mining

Process mining: Overview 1) basic performance metrics 2) process model3) organizational model4) social network 5) performance characteristics If …then … 6) auditing/security

Process Mining: The alpha algorithm alpha algorithm

Process log Minimal information in log: case id’s and task id’s. Additional information: event type, time, resources, and data. In this log there are three possible sequences: –ABCD –ACBD –EF case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D

>, ,||,# relations Direct succession: x>y iff for some case x is directly followed by y. Causality: x  y iff x>y and not y>x. Parallel: x||y iff x>y and y>x Choice: x#y iff not x>y and not y>x. case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D A>B A>C B>C B>D C>B C>D E>F ABACBDCDEFABACBDCDEF B||C C||B

Basic idea (1) xyxy

Basic idea (2) x  y, x  z, and y||z

Basic idea (3) x  y, x  z, and y#z

Basic idea (4) x  z, y  z, and x||y

Basic idea (5) x  z, y  z, and x#y

It is not that simple: Basic alpha algorithm Let W be a workflow log over T.  (W) is defined as follows. 1.T W = { t  T     W t   }, 2.T I = { t  T     W t = first(  ) }, 3.T O = { t  T     W t = last(  ) }, 4.X W = { (A,B)  A  T W  B  T W   a  A  b  B a  W b   a1,a2  A a 1 # W a 2   b1,b2  B b 1 # W b 2 }, 5.Y W = { (A,B)  X   (A,B)  X A  A  B  B  (A,B) = (A,B) }, 6.P W = { p (A,B)  (A,B)  Y W }  {i W,o W }, 7.F W = { (a,p (A,B) )  (A,B)  Y W  a  A }  { (p (A,B),b)  (A,B)  Y W  b  B }  { (i W,t)  t  T I }  { (t,o W )  t  T O }, and  (W) = (P W,T W,F W ).

Example case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D  (W) W

Problems of basic alpha algorithm Hidden tasks Duplicate tasks Short-loops Loop of length 2 Loop of length 1

Dealing with short loops: The alpha+ algorithm This paper deals with short loops. Loops of length 2 are addressed by refining the ordering relations (look for xyx to distinguish a loop from x||y). Loops of length 1 are addressed by a pre- processing step where they are first removed and then added in a post-processing step. (For details, see paper.) (Approach has been implemented and tested.)

Process Mining: Tooling

Applications We have applied/are applying our process mining techniques within several organizations (CJIB, RWS, UWV, …). We did not apply them in the context of Ubiquitous Mobile Systems (UMSs) yet, therefore we present some application scenarios.

Application scenario: Clinical information systems Use of PDAs for personnel, RFID tags for equipment, etc. Process mining can be used to support evidence- based medicine.

Application scenario: Web services Are organizations working the way they should? See BPEL4WS: “ Business processes can be described in two ways. Executable business processes model actual behavior of a participant in a business interaction. Business protocols, in contrast, use process descriptions that specify the mutually visible message exchange behavior of each of the parties involved in the protocol, without revealing their internal behavior. The process descriptions for business protocols are called abstract processes. BPEL4WS is meant to be used to model the behavior of both executable and abstract processes.”

Application scenario: Wireless gallery information system Use of PDAs for providing content based on proximity. Process mining can be used to monitor the interests of visitors. eDocent™ American Museum of the Moving Image

Conclusion Process mining seems to be interesting in the context of Ubiquitous Mobile Systems (UMSs). There are many challenges: –Improving the algorithms (hidden/duplicate tasks, …) –Gathering the data –Visualizing the results –Etc. In this paper we “solved” one of the these problems: short loops. Join us at