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Native Directly Follows Operator [article]

Alifah Syamsiyah, Boudewijn F. van Dongen, Remco M. Dijkman
2018 arXiv   pre-print
req ← >L(a, b) 19 f req ← f req + 1 20 >L(a, b) ← f req 21 sa ← sc 22 ea ← ec 23 sc ← ea + 1 24 ec ← sc 25 return >L After the database engine parses directlyfollows, it calls the directly Follows() function  ...  do 11 cT ime ← #time(σ(sc)) 12 while ec + 1 < |σ| and cT ime = #time(σ(ec + 1)) do 13 ec ← ec + 1 14 for i ← sa; i ≤ ea; i + + do 15 for j ← sc; j ≤ ec; j + + do 16 a ← #act(σ(i)) 17 b ← #act(σ(j)) 18 f  ... 
arXiv:1806.01657v1 fatcat:wwmcgqvlbvd7fnvnw4rmldqque

EMiT: A Process Mining Tool [chapter]

Boudewijn F. van Dongen, Wil M. P. van der Aalst
2004 Lecture Notes in Computer Science  
Processed To bvdongen@staffw_ 2002/04/18 09:05 | B Processed To bvdongen@staffw_ 2002/04/18 13:48 C Released By bvdongen@staffw_ 2002/04/18 09:05 | B Released By bvdongen@staffw_ 2002/04/18 13:53 F  ...  Processed To bvdongen@staffw_ 2002/04/18 09:05 | E Processed To bvdongen@staffw_ 2002/04/18 13:53 E Released By bvdongen@staffw_ 2002/04/18 09:06 | D Released By bvdongen@staffw_ 2002/04/18 13:56 F  ... 
doi:10.1007/978-3-540-27793-4_26 fatcat:tc25tioczrh7tfugjpmrwry5ba

Discovering Petri Nets from Event Logs [chapter]

Wil M. P. van der Aalst, Boudewijn F. van Dongen
2013 Lecture Notes in Computer Science  
L 4 = [ a, b, e, f 2 , a, b, e, c, d, b, f 3 , a, b, c, e, d, b, f 2 , a, b, c, d, e, b, f 4 , a, e, b, c, d, b, f 3 ], i.e., α(L 4 ) = N 4 .  ...  p ({b},{c,f}) p ({a,d},{b}) p ({c},{d}) Fig. 10. WF-net N4 derived from L4 = [ a, b, e, f 2 , a, b, e, c, d, b, f 3 , a, b, c, e, d, b, f 2 , a, b, c, d, e, b, f 4 , a, e, b, c, d, b, f 3 ].  ... 
doi:10.1007/978-3-642-38143-0_10 fatcat:mdpeun735naizjky56ih6gsvwi

Aggregating Causal Runs into Workflow Nets [chapter]

Boudewijn F. van Dongen, Jörg Desel, Wil M. P. van der Aalst
2012 Lecture Notes in Computer Science  
) = {f (x) | x ∈ dom(f )}  ...  A function f : N → μ is a coloring function if, for all (n 1 , n 2 ) ∈ E, either n 1 = n 2 or f (n 1 ) = f (n 2 ).  ... 
doi:10.1007/978-3-642-35179-2_14 fatcat:p6dadpglivgald26kmx3knzwgu

Privacy Analysis of User Behavior Using Alignments

Arya Adriansyah, Boudewijn F. van Dongen, Nicola Zannone
2013 it - Information Technology  
Fig. 6 An optimal alignment between trace σ 2 = a, c, l, g, i, m, f and the net in Fig. 5 , showing that task insert biodata is swapped with lab test γ 4 = a c l g i m f a c l g i m f (swap i with l)  ...  However, the optimal alignment in Fig. 3 shows two moves on log and two moves on model. γ 3 = a c l g i e f a c i g e l f t1 t2 t3 t4 t6 t5 t7 Fig. 3 An optimal alignment between trace σ 2 and the  ... 
doi:10.1524/itit.2013.2006 fatcat:mlctmiwskzcsjjpj4osfnwt54i

Privacy Analysis of User Behavior Using Alignments

Arya Adriansyah, Boudewijn F. van Dongen, Nicola Zannone
2013 it - Information Technology  
Figure 6 An optimal alignment between trace σ 2 = a, c, l, g, i, m, f and the net in Fig. 5 , showing that task insert biodata is swapped with lab test. place p t j →t i .  ... 
doi:10.1515/itit.2013.2006 fatcat:bw4sspha65dtjbqys5gihsjuym

Discovering Hierarchical Consolidated Models from Process Families [chapter]

Nour Assy, Boudewijn F. van Dongen, Wil M. P. van der Aalst
2017 Lecture Notes in Computer Science  
Let F 1 , F 2 ⊆ E • be two SHESHEs and F = F 1 ∪ F 2 be their union such that F is weakly connected. F is a SHESHE iff id(F 1 ) = id(F 2 ) or F 1 ⊆ F 2 .  ...  One of the three statements holds: (i) FF , (ii) FF or (iii) FF = ∅.  ... 
doi:10.1007/978-3-319-59536-8_20 fatcat:2virbfjm6beyxnxlaqvjuj2tfy

Online Conformance Checking Using Behavioural Patterns [chapter]

Andrea Burattin, Sebastiaan J. van Zelst, Abel Armas-Cervantes, Boudewijn F. van Dongen, Josep Carmona
2018 Lecture Notes in Computer Science  
Bibliography [1] van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer (2016)  ...  Unfolded Reverse Petri Net Reverse Petri Net A A1 E B F A2 D C p1 p2 p0 p4 p5 p3 p6 A A1 A2 B C D E F A > A1 > > A2 > B > > C > D > E > F A A1 A2 B C D E F A 0/ 0 A1 1/ 3 1/ 3 A2 2/ 3 B 2/ 4 2/ 4 C 3  ...  A A1 E B F A2 D C F A1 E B F A2 D C F A1 {A≺A1, A1≺A2, A2 ≺A1} p'' 2 p''' 2 p' 2 {A≺A1, A1≺A2, A2 ≺A1} {A≺A1} Step 3 : Computation of PMOC's Data Structures via Reachability Graphs Given the complete  ... 
doi:10.1007/978-3-319-98648-7_15 fatcat:ldaxhuu6ifh5bj55kasx55txdq

Alignment Based Precision Checking [chapter]

Arya Adriansyah, Jorge Munoz-Gama, Josep Carmona, Boudewijn F. van Dongen, Wil M. P. van der Aalst
2013 Lecture Notes in Business Information Processing  
Some possible alignments between the two are: γ 1 = a a ⊥ c e f a ⊥ d c e f γ 2 = a a ⊥ c e f ⊥ a d c e f γ 3 = a ⊥ a c e f a d ⊥ c e f γ 4 = a a c ⊥ ⊥ e f ⊥ a ⊥ d c e f The moves are represented vertically  ...  When we iterate over m, we refer to each unique element in m, e.g. for all function f : W → IN, ∑ x∈m f (x) = ∑ x∈W m(x) · f (x).  ... 
doi:10.1007/978-3-642-36285-9_15 fatcat:cjslmst3qbcktfor462iikruzu

Avoiding Over-Fitting in ILP-Based Process Discovery [chapter]

Sebastiaan J. van Zelst, Boudewijn F. van Dongen, Wil M. P. van der Aalst
2015 Lecture Notes in Computer Science  
10 , a, c, b, d, f 10 , a, c, e, d, f 10 , a, e, c, d, f 10 ] 2 .  ...  Fig. 4 : 4 a, b, c, d, e, f ], e) ([a, b, c, d, e 2 , f ], Sequence encoding graph based on event log L .  ... 
doi:10.1007/978-3-319-23063-4_10 fatcat:ulw4hp4aj5duje5zlgkqbqx24i

Event stream-based process discovery using abstract representations

Sebastiaan J. van Zelst, Boudewijn F. van Dongen, Wil M. P. van der Aalst
2017 Knowledge and Information Systems  
Boudewijn F. van Dongen is an associate professor at the Architecture of Information Systems group at the Department of Mathematics and Computer Science of the Eindhoven University of Technology.  ...  IEEE Trans Serv Comput 8(6):833-846. doi:10.1109/TSC.2015.2459703 51. de Medeiros AKA, van Dongen BF, van der Aalst WMP, Weijters AJMM (2005) Process mining for ubiquitous mobile systems: an overview and  ... 
doi:10.1007/s10115-017-1060-2 fatcat:iunnvliqqrdidginn5k7q4cjie

Process Mining Framework for Software Processes [chapter]

Vladimir Rubin, Christian W. Günther, Wil M. P. van der Aalst, Ekkart Kindler, Boudewijn F. van Dongen, Wilhelm Schäfer
2007 Lecture Notes in Computer Science  
Software development processes are often not explicitly modelled and sometimes even chaotic. In order to keep track of the involved documents and files, engineers use Software Configuration Management (SCM) systems. Along the way, those systems collect and store information on the software process itself. Thus, SCM information can be used for constructing explicit process models, which is called software process mining. In this paper we show that (1) a Process Mining Framework can be used for
more » ... k can be used for obtaining software process models as well as for analysing and optimising them; (2) an algorithmic approach, which arose from our research on software processes, is integrated in the framework.
doi:10.1007/978-3-540-72426-1_15 fatcat:ckfbxdu73jb7nldiq2jbqhx4zm

Maximizing Synchronization for Aligning Observed and Modelled Behaviour [chapter]

Vincent Bloemen, Sebastiaan J. van Zelst, Wil M. P. van der Aalst, Boudewijn F. van Dongen, Jaco van de Pol
2018 Lecture Notes in Computer Science  
., a, b, b, c, a, f \ {b, f } = a, c, a .  ...  G D G,F G F E C E G G G C F F F We can use this property to search for a subsequence of the log trace that can fully synchronize with the model.  ... 
doi:10.1007/978-3-319-98648-7_14 fatcat:3jf5vljrgjes7dtaimr6cnu2vi

Conformance Checking of Interacting Processes with Overlapping Instances [chapter]

Dirk Fahland, Massimiliano de Leoni, Boudewijn F. van Dongen, Wil M. P. van der Aalst
2011 Lecture Notes in Computer Science  
A Petri net N = (S, T, F, ) consists of a set S of places, a set T of transitions disjoint from S, arcs F ⊆ (S × T ) ∪ (T × S) , and a labeling : T → Σ ∪ {τ } assigning each transition t an action name  ...  A proclet P = (N, ports) consists of a labeled Petri net N = (S, T, F, ) and a set of ports, where some transition initial ∈ T has no pre-place (i.e., {s | (s, initial ) ∈ F } = ∅) and some transition  ... 
doi:10.1007/978-3-642-23059-2_26 fatcat:6i2hzehkw5ddpcnw46olcxwto4

DB-XES: Enabling Process Discovery in the Large [chapter]

Alifah Syamsiyah, Boudewijn F. van Dongen, Wil M. P. van der Aalst
2018 Lecture Notes in Business Information Processing  
In our example above, the rows in table dfr containing (dfr 1 , , c, f ) and (dfr 1 , c, ⊥, f ) will be updated as (dfr 1 , , c, f + 1) and (dfr 1 , c, ⊥, f + 1) with f is the frequency value.  ...  Referring to our example, row (dfr 1 , b, ⊥, f ) is updated to (dfr 1 , b, ⊥, f -1).  ... 
doi:10.1007/978-3-319-74161-1_4 fatcat:ql5fxjhn65go5gndj2g5jjraou
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