A markov prediction model for data-driven semi-structured business processes

Lakshmanan, Geetika T.; Shamsi, Davood; Doǧanata, Yurdaer N.; Unuvar, Merve; Khalaf, Rania

doi:10.1007/s10115-013-0697-8

Cited by 99 publications

(64 citation statements)

References 20 publications

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“…(ii) The number of characteristics can be real big (iii) Characteristics may not be evident in the Log (thus, they must be derived) (iv) The metric to discriminate cases (i.e., the dependent variable) may not be clear to process owners / decision makers. Because of this kind of difficulties, using ad hoc approaches are prominent in the literature [3][4][5][6]. In practice, a less sophisticated yet intuitive way to deal with this problem is to use filtering and subsetting techniques, available in all process discovery software tools.…”

Section: Introductionmentioning

confidence: 99%

Discovering Characteristics that Affect Process Control Flow

Delias

Grigori

Mouhoub

et al. 2015

Decision Support Systems IV – Information and Knowledge Management in Decision Processes

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Abstract. In flexible environments like healthcare and customer service, business processes are executed with high variability. Often, this is because cases' characteristics vary. However, it is difficult to correlate process flow with characteristics because characteristics may refer to different perspectives, their number can be real big or even because deep domain knowledge may be required to state hypotheses. The goal of this paper is to propose an effective exploratory tool for discovering the characteristics that are causing the process variation. To this end, we propose a process mining approach. First, we apply a clustering approach based on Latent Class Analysis to identify subtypes of related cases based on the case-wise process characteristics. Then, a process model is discovered for each cluster and through a model similarity step, we are able to recommend the characteristics that mostly diversify the flow. Finally, to validate our methodology, we applied it to both simulated and real datasets.

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Section: Introductionmentioning

confidence: 99%

Discovering Characteristics that Affect Process Control Flow

Delias

Grigori

Mouhoub

et al. 2015

Decision Support Systems IV – Information and Knowledge Management in Decision Processes

View full text Add to dashboard Cite

show abstract

“…When the size of sample data is small, or sometimes even with a large quantity of data, there might not be any statistical laws to be found, in these cases, an ERM cannot be used to forecast [15]. The Markov prediction model is effective in the prediction of the state of a process, but it is not suitable for medium and long-term predictions for a system [16]. The neural network model implements the mapping function from input to output, but 'over fitting' often results in poor prediction performance [17].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model

2017

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Abstract:With the rapid development of China's manufacturing, energy consumption has increased rapidly, and this has become a major bottleneck affecting the sustainable development of China's economy. This paper deduces and constructs a homologous grey prediction model with one variable and one first order equation (HGEM(1,1)) for forecasting the total energy consumption of China's manufacturing based on the Grey system theory. Both parameter estimation (PE) and the deduction of the final restored expression (FRE) of the HGEM(1,1) model are all from the time response expression of the whitenization differential equation, which solves the 'non-homologous' defects of PE and FRE with traditional grey prediction models. HGEM(1,1) has good performance and can unbiasedly simulate a homogeneous/non-homogeneous exponential function sequence and a linear function sequence. Then, the HGEM(1,1)model is used to simulate and forecast the total energy consumption of China's energy manufacturing, and the results show that the comprehensive performance of this model is much better than that of the classic Grey Model with one variable and single order equation, GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, we forecast the total energy consumption of China's manufacturing industry during the years 2018-2024. The results show that the total energy consumption in China's manufacturing is slowing down but is still too large. For this, some measures, such as optimizing the manufacturing structure and speeding up the development and promotion of energy saving and emission reduction technologies, to ensure the effective supply of energy in China's manufacturing industry are suggested.

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“…Many authors have proposed techniques to relate specific characteristics in an ad-hoc manner. For example, several approaches have been proposed to predict the remaining processing time of a case depending on characteristics of the partial trace executed [1][2][3]. Other approaches are only targeted to correlating certain predefined characteristics to the process outcome [4][5][6] or the violations of business rules [7].…”

Section: Introductionmentioning

confidence: 99%

“…In addition to decision trees, many other machine-learning techniques exist and some have already been applied in BPM, such as Bayesian Networks [11], Case-Based Reasoning [12] and Markov Models [3]. These are certainly valuable but they are only able to make correlations for single instances of interest or to return significant examples of relevant instances.…”

Section: Introductionmentioning

confidence: 99%