xoEPC - Jan Mendling

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D.2. Univariate Logistic Regression 429 variable set without collinearity every variable should have a tolerance value higher than 0.1, otherwise there is a collinearity problem. In the original variable set (Table D.1) there are several collinearity problems. We dropped the count metrics apart from SN since they were highly correlated. This resulted in a reduced variable set with almost no collinearity problems (Table D.2). The SN metric is close to the 0.1 threshold and therefore kept in the metrics set. D.2 Univariate Logistic Regression This section presents the results of the univariate logistic regression analysis. In particular we calculated univariate models with and without a constant (see Tables D.3 and D.4). As a conclusion from these models we drop the constant and the control flow complexity CFC for the multivariate analysis. First, the constant is not significantly different from zero (see Wald statistic) in the separability and the sequentiality model which suggests that it is not necessary. Second, the CFC metric is not significantly different from zero (see Wald statistic) in both models with and without constant. D.3 Multivariate Logistic Regression Based on a reduced set of variables without CFC we calculated multivariate logistic regression models. Figure D.1 shows that the Hosmer & Lemeshow Test indicates a good fit based on the difference between observed and predicted frequencies. This test should yield a value greater than 5% and this condition is fulfilled by all models from step 3 on. Figure D.2 summarizes the value of Nagelkerke’s R 2 , a statistic ranging from 0 to 1 that serves as a coefficient of determination. It indicates which fraction of the variability is explained. The figure shows that from step 3 on the value approaches 0.90 which is an excellent value. Figure D.3 and D.4 give the classification tables and the equations of the models in the different steps.
430 D. Logistic Regression Results Table D.3: Univariate logistic regression models without constant B Exp(B) Wald Hosmer & L. Nagelkerke R 2 N -0.440 0.957 0.000 0.000 0.256 diameter -0.112 0.894 0.000 0.000 0.387 CNC -2.082 0.013 0.000 0.000 0.637 Density -41.081 0.000 0.000 0.000 0.771 AvCDegree -0.532 0.588 0.000 0.000 0.506 MaxCDegree -0.351 0.704 0.000 0.000 0.396 Separability -4.657 0.009 0.000 0.000 0.733 Sequentiality -7.038 0.001 0.000 0.123 0.760 Structuredness -2.688 0.068 0.000 0.000 0.728 Depth -0.908 0.403 0.000 0.000 0.193 MM -0.090 0.914 0.000 0.000 0.066 cHeterogeneity -1.223 0.294 0.000 0.000 0.085 CFC 0.000 1.000 0.531 0.000 0.000 CYC 0.301 1.352 0.588 0.999 0.000 tokenSplit -0.067 0.935 0.000 0.000 0.020 Table D.4: Univariate logistic regression models with constant Cons. Exp(Cons.) Wald B Exp(B) Wald H. & L. N. R 2 N -3.954 0.019 0.000 0.068 1.070 0.000 0.000 0.295 diameter -3.306 0.037 0.000 0.087 1.091 0.000 0.000 0.132 CNC -9.411 0.000 0.000 7.294 1472.146 0.000 0.000 0.138 Density 0.634 1.885 0.001 -54.440 0.000 0.000 0.000 0.311 AvCDegree -3.029 0.048 0.000 0.291 1.338 0.000 0.000 0.042 MaxCDegree 3.575 0.028 0.000 0.344 1.411 0.000 0.000 0.145 Separability 0.027 1.028 0.872 -4.716 0.009 0.000 0.000 0.184 Sequentiality -0.204 0.815 0.117 -6.391 0.002 0.000 0.262 0.268 Structuredness 7.064 1169.081 0.000 -11.210 0.000 0.000 0.000 0.377 Depth -3.419 0.033 0.000 1.343 3.830 0.000 0.000 0.208 MM -3.459 0.031 0.000 0.270 1.310 0.000 0.000 0.318 cHeterogeneity -4.811 0.008 0.000 5.259 192.361 0.000 0.000 0.413 CFC -2.115 0.121 0.000 0.000 1.000 0.382 0.000 0.001 CYC -2.244 0.106 0.000 5.104 164.740 0.000 0.999 0.065 tokenSplit -2.871 0.057 0.000 0.269 1.308 0.000 0.000 0.235
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Detection and Prediction of Errors
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Abstract Business process modeling
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Acknowledgement On these pages it i
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Contents List of Figures iv List of
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Contents iii 4.3.4 Reduction of the
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List of Figures 1.1 Information Sys
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List of Figures vii 4.15 Reduction
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List of Figures ix A.5 Asset Accoun
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List of Figures xi A.40 Plant Maint
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List of Figures xiii A.77 Sales and
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List of Figures xv B.23 Personnel T
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List of Figures xvii C.3 Box plot f
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List of Figures xix C.59 Box plot f
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List of Tables 3.1 Overview of appr
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List of Acronyms ACM Association fo
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Table of Acronyms xxv WS-CDL Web Se
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Chapter 1 Introduction This chapter
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1.2. Research Contributions 3 1.2 R
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1.3. Epistemological Position 5 wit
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1.3. Epistemological Position 7 lem
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1.3. Epistemological Position 9 The
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1.3. Epistemological Position 11 ce
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1.4. Structure of this Thesis 13 ne
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3.4. EPC Semantics 53 a decision ha
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3.4. EPC Semantics 55 c1 c2 e1 f1 e
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3.4. EPC Semantics 57 e2 f2 a3 a4 a
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3.4. EPC Semantics 59 Boolean net.
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3.4. EPC Semantics 61 Even though t
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3.4. EPC Semantics 63 mann propose
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3.4. EPC Semantics 65 Table 3.3 sum
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3.4. EPC Semantics 67 In the follow
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3.4. EPC Semantics 69 token. The di
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3.4. EPC Semantics 71 have a wait c
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3.4. EPC Semantics 73 d dead contex
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3.4. EPC Semantics 75 (a) (b) (c) (
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3.4. EPC Semantics 77 (a) d (b) (c)
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3.4. EPC Semantics 79 • Figure 3.
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3.4. EPC Semantics 81 assigns a pos
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3.4. EPC Semantics 83 are in a wait
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3.4. EPC Semantics 85 • m → −
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3.4. EPC Semantics 87 is set to dea
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3.4. EPC Semantics 89 • m ∗ →
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3.4. EPC Semantics 91 Algorithm 1 P
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3.4. EPC Semantics 93 format [IDS03
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3.4. EPC Semantics 95 Figure 3.22:
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3.5. EPCs and other Process Modelin
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224 A. Errors found with xoEPC Anal
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226 A. Errors found with xoEPC Post
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228 A. Errors found with xoEPC Post
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230 A. Errors found with xoEPC Need
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236 A. Errors found with xoEPC Capa
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238 A. Errors found with xoEPC Shop
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240 A. Errors found with xoEPC Open
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242 A. Errors found with xoEPC Requ
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244 A. Errors found with xoEPC Mat.
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246 A. Errors found with xoEPC Chec
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248 A. Errors found with xoEPC Comp
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250 A. Errors found with xoEPC Budg
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252 A. Errors found with xoEPC Paym
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254 A. Errors found with xoEPC Mate
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256 A. Errors found with xoEPC Prov
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266 A. Errors found with xoEPC Main
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276 A. Errors found with xoEPC Proj
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312 A. Errors found with xoEPC OTC
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314 A. Errors found with xoEPC
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316 B. EPCs not completely reduced
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374 C. Descriptive Statistics of Va
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376 C. Descriptive Statistics of Va
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Page 313 and 314: 442 Bibliography [ACD + 03] T. Andr
Page 315 and 316: 444 Bibliography [ARD + 06] W.M.P.
Page 317 and 318: 446 Bibliography [BE05b] U. Brandes
Page 319 and 320: 448 Bibliography [Boe81] B.W. Boehm
Page 321 and 322: 450 Bibliography Proceedings of the
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Page 333 and 334: 462 Bibliography [IEE83] IEEE. IEEE
Page 335 and 336: 464 Bibliography [Kie03] B. Kiepusz
Page 337 and 338: 466 Bibliography volume 4103 of Lec
Page 339 and 340: 468 Bibliography [Löw00] J. Löwer
Page 341 and 342: 470 Bibliography [MH05] J. Mendling
Page 343 and 344: 472 Bibliography many, pages 61-80,
Page 345 and 346: 474 Bibliography Workshops at the 1
Page 347 and 348: 476 Bibliography [Nis98] M.E. Nisse
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480 Bibliography [RV04] H.A. Reijer
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482 Bibliography [SF06] H. Smith an
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484 Bibliography on Conceptual Mode
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486 Bibliography A. Haller, editors
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488 Bibliography O. Pastor, editors
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490 Bibliography [WVA + 06b] M.T. W
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xoEPC - Jan Mendling

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