BCO5501 - Business Process Engineering: BPM Article Review Essay

This essay presents a review of an article focusing on fast and accurate business process drift detection. It discusses techniques for identifying changes in business processes, particularly how transaction workers adapt to alterations in workload, regulations, or business rules. The review covers drift detection methods, their disadvantages, and relevance, including statistical approaches for analyzing event logs and identifying significant differences in observed processes. It evaluates the accuracy of drift detection using synthetic logs, measures like F-score and mean delay, and examines the impact of window size on accuracy. The essay also highlights issues raised by the authors, such as the effect of window size on accuracy and the performance of adaptive window strategies. Supporting articles are cited to provide a comprehensive overview of business process drift detection.

REVIEW ON BUSINESS PROCESS MANAGEMENT ARTICLE
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TITLE: REVIEW ON BUSINESS PROCESS MANAGEMENT ARTICLE.
ARTICLE: FAST AND ACCURATE BUSINESS PROCESS DRIFT DETECTION.
AUTHORS: 1.Abderrahmane Maaradji (NICT, Australia)
2. Marlon Dumas (University of Tartu, Estonia)
3. Marcello La Rosa (Queensland University of Technology Australia)
4. Alireza Ostrovar (Queensland University Of Technology Australia)
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INTRODUCTION.
This paper is purposed to define how one is able to identify and detect accurately business
process drift. The review gives different techniques in which the business is prone to changes
and how to identify them. A transaction worker might begin carrying out a transaction in a
different manner to fit into alteration in the amount of work assigned, period, rules or regulation
governing the business. By use of early detection method according to the event registry
commonly referred to as the detection of a drift in business transactions, strategists are able work
on these changes, (Bevilacqua, Ciarapica & Giacchetta, 2009). The methods used in detection of a
drift in business transactions are mostly based on the idea of extraction from a trace. In that, in
case there are two different occurrence of business process over a period of time analysts can
achieve a suitable level of accuracy by tracing the behavior and frequency of occurrence for a
particular technique and hence adjust it.
The review aims at looking at the various important aspects of business processes which are the
drift detection methods, the disadvantages of those methods and also the relevance of the
methods. The review also gives related articles which are relevant to the review of this paper.
This review is structured as follows; a brief description of business processes different methods
of analyzing and synthesizing these processes, their relevance, and also how to perform a
statistical analysis, (Harmon & Trends, 2010).
DISCUSSION.
The main aim of business process drift detection is to formulate techniques which help to
identify a drift in the business process. It entails different methodologies that are able to show the
drift before or after it has occurred. A drift maybe due to a competitive environment, supply,
demand and also the regulatory environment. The existing methods are based mainly on
extracting patterns from traces as stated before. For example; one event S occurs more frequently
than another R or even R occurs more than once in a pattern, (Houy, Fettke & Loos, 2010).
The above article proposes the automation and scaling of techniques in the detection of drift in
transactions of a business. Drift methods dwell mainly on the following important aspects; first,
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there is simply the identification of the point whereby there is a significant difference in the
observed processes between and after that particular point. In fact, it addresses the question of
where are these processes the same or when there is trace equivalence. It also shows a method of
identifying drifts on various channels and sources. This is whereby there is monitoring of any
statistical significant changes in the distribution that may be split to an identification and
discovery population. It also brings out the aspect of use of the adaptive window and finally the
evaluation on synthetic logs, (Röglinger, Pöppelbuß & Becker 2012).
Drift detection methods; this drift detection methods dwell mainly on the statistical view point.
These methods can be used from the registry of events to sectional order running. It has basically
a collection of simple traces where each requires and captures the event sequences. It also
contains three other definitions;
a) The event log trace-it can be considered as B being an event log and C being collection of
event occurrence, μ; B>L being a function for labeling. Any trace of an event ΦEB expressed in
accordance to say any order f E (0, n-1) if the trace expresses all orders of events, this gives a
relationship to concurrent partial order run, (Hallerbach, Bauer & Reichert, 2010).
b) The concurrency of alpha- considered as basically a relation that is symmetrical that is used as
a label on the occurrence of events. As stated before the parallel relationship is altered to give
incomplete presentation of the listing of events.
However, this method is highly stressed in that it does not capture event concurrency.
Bos et al. (1,3) “Dealing with concept drifts in process mining.” (2015)
Detecting process drift using statistical testing over feature vectors makes the method insufficient
in identification of specific drift types which may include insertion of a move that is conditional.
Statistical Testing Over Runs; This method is used mainly in the identification of drifts in a run
source. This is carried out basically on a pair of population which have similar quantity and sit
on runs that are latest. These runs are then divided into two; the reference and the detection
population, hence resulting to two transfixed windows which are also called the reference and
the detection window. From these two windows, a sequence is obtained in which for every new
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run observed, both the reference and the detection window are run in the right order to show a
new run and perform a new statistical test.
The main disadvantage of this method from Accorsi (“Discovering workflow changes with time-
based trace clustering. In: Data-Driven Process Discovery and Analysis.” Springer (2012) 154–
168) is that drifts detection based on trace clustering is heavily varies with the size of the
window that is selected for the test. This leads to a situation whereby a smaller sized window
causes positives that are false and a bigger sized window causes negatives that are low since
detection of occurring drifts happens, (Weske 2012).
EVALUATION ON SYNTHETIC LOGS; A tool that can read a complete event logs is established
so as to be used in the statistical test. So as to ensure accuracy in evaluating a drift the
measurement of both mean of recall and delay are taken. Secondly, they are computed in terms
of average number of log traces and finally from the result, the drift is realized from the
information. In this method, the goodness is measured on the basis of how much accurate and
scalable they are in various configurations, it is able to accomplish the tracing of events. The
traces are then dynamically incorporated to modify the alpha based association for a given group
of twos events, and further transform an order run that is partial, which then produces a stream of
runs, (Van Der Aalst 2013).
To assess the accuracy two measurements are used, an F-score is used as both the harmonic
average and the mean delay. It is then to find the mean and then detects the drift. This measures
not only how late the drift occurs; it also computes the scalability of tracing logs in detecting the
drifts.
IMPACT OF WINDOW SIZE ON ACCURACY; In this experiment fixed window sizes ranging
from 30- 160 traces in measurement of 30 against each of the 72 logs are used. The F-score
obtained with four log sizes (2500 to 10000traces) whereby in each log the F-score averaged
produced 18 change patterns. It is evident that the F-score changes with a rise in the size of the
window until it reaches a plateau point at the size of 150.As shown in the linear graphs below.
From these results it is evident that the more the data points used to include the reference and
detection windows, the more accurate it becomes leading to a detection of all concept drift, with
few or no false positives, (Zur Muehlen & Recker, 2013).
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1 2 3 4
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mean delay
An interesting feature from the findings is that after an initial high mean delay, the mean delay
grows very slowly as the window size increases. This proves that the method is resilient in terms
of mean delay to increase in window size, having a relative low delay of around 40 traces when
the window size is 50 or above.
The conclusion from these findings is that the accuracy of the results according to traces display
is repeatedly slightly less compared to the runs hence affirms the insinuations.
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Issues Highlighted by the Authors
How accuracy is affected with the window size.
The versatile window strategy on F-score and delay mean was surveyed and observed to be mean
of the three log sizes of 5000,7500 and 10000 follows, with the acquired outcomes utilizing a
versatile window. For instance, an unchanged 25 size of a window, with those got in a versatile
window introduced to 25traces. In any case, the size log of 2500 follows was not used to stay
away from the interaction between window size and number of drifts as seen in logs.
25 50 75 100 125
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FWIN
AWIN
F-SCORE
25 50 75 100 125
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FWIN
AWIN
window size
mean delay
From the outcomes got it is clear that the adaptive window beats the unmovable window both
regarding f-score and mean delay. The capacity to progressively change the window estimate in
light of the varieties enables a satisfactory number of runs to be gotten that is not very substantial
or too little, with reference to location windows to perform test factually.
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To finish up the discoveries it is discovered that it prompts a reduced mean delay when the
window measure is reduced when the variety are lower cause the listed scenarios have a reduced
count of runs that are adequate to carry out the factual test. The fundamental preferred standpoint
is that the versatile window technique conquers the low exactness got when the window estimate
counts are lower or equal to 25 number of traces and a mean count of 28. This likewise results to
a conclusion that the mean delay if kept as low as conceivable is necessary to get various large
counts of drifts as could be expected under the circumstances.
Exactness change in each pattern
To additionally verify exactness, the common point of f-score and mean delay for every 12
simply change pattern and 6 composite change pattern are checked. The window size is then
settled for a hundred traces used to give the best exchange off regarding f-score, the delay of
mean plus the mean quotient got from the given adaptive windows instated with a hundred traces
on the following log sizes of 5000,7500 and 10000 traces.
Also, the strategy experiences a sensible lower f-score both for settled and versatile windows for
the recurrence change pattern(fr). this design results to the adjustment of recurrence of specific
occasions connection in the log. The low f-score is because of a low accuracy, (Rao, Mansingh
and Osei-Bryson 2012).
Data set generation
On informational collection age an informational collection of 18 logs, one of each change
design is join with the base log of 18 modified logs in the interleaving manner.To reproduce
slow changes ,the base log is joined with an adjusted log in an alternate manner.It basically
begins by testing traces from the base log just so the quantity of traces increments. In this way
expanding the likelihood of testing from the modified log until just the traces from the adjusted
log are inspected. This is otherwise called likelihood slow float.
To test the conduct of two logs the straight likelihood work with an incline of 0.2% are used.In
other terms,from a likelihood beginning at 1(rep increment) by 0.02 each time another trace is
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broke down to achieve zero after 500 traces, every slow float interim includs 25% of the conduct
of each log so an exact segment of each float is known, (Trkman 2010).
Creteria for evaluating accuracy
To asess the exactness of the continuous float location technique, the f-score and the mean
delay,are characterized in a marginally extraordinary way.To process prcision and review for the
f-score it is said that an identified float is genuine positive and on the off chance that it
incorporates the essential issue of the interim of the real float is viewed as a false positive.To
check this if the real float occurs between the numbers 751 and 1250 the middle would be the
trace number 1000. Which for this situation a continuous float that it would identify involves the
middle 1000, (McCormack and Johnson, 2016).
Nonetheless, if an examination is made for sudden float location, the technique accomplishes a f-
score that is beneath 0.7 and a moderately higher mean deferral for three patterns.For disposition,
the most minimal exactness is determined with the fr design which is in accordance with the
outcomes for sudden float recognition, (Scheer 2012).
CONCLUSION
To conclude it is clear that a computerized technique for recognizing sudden and continuous
float in business transactions making use of traces is discussed and found to be effective. It
likewise gives an assessment over logs further demonstrates if strategy precisely finds normal
changes in processes, (Neiger, Rotaru and Churilov, 2009). It additionally demonstrates that
continuous drift identification techniques primarily depend on pure presequites that a steady float
is not limited to two back to back sudden drifts, in a way that runs dissemination contained is a
straight blend of dispersions of runs preceding the two drifts. The exactness established is the
thing that demonstrates that the presumption for the most part holds in place. Coming up with a
better and modern strategy could alter the avenue for future research and analysis,(Papageorgiou
2009).
It also holds that the accuracy for any data trace should be evaluated so as to detect drift in the
data trace.T he different methods outlined in the review provides the relevant techniques to
determine the drift in business processe. this aanalysis proves to be impotrant in business
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processes management since it allows for one to predict changes in business process and
detrmine the legitimacy and outcome of the business process environment.This could be in terms
of supply, demand, or change in regulatory environment.
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References.̇
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Zur Muehlen, M. and Recker, J., 2013. How much language is enough? Theoretical and practical
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