Semantic Business Process Mining of SAP Transactions

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

As one of the hot topics in Business Process Management (BPM), process mining aims at constructing models to explain what is actually happening from different perspectives based on the process-related information that automatically extracted from event logs. Because the semantics of the data that recorded in event logs are not usually explicit, current mining approaches are somewhat limited. A number of studies have been carried out in the combination use of formalized semantic models and process mining technologies to obtain the semantic mining capability. However, among these researches, there is lack of a guideline that can clearly illustrate different stages during the semantic process mining. The objective of this study is to present a general framework, which unambiguously expresses the main stages of the semantic process mining. Based on this framework, an example about carbon footprint analysis is used to show the possibility of obtaining advantages from semantic process mining.

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

Many present-day companies carry out a huge amount of daily operations through the use of their information systems without ever having done their own enterprise modeling. Business process mining is a well-proven solution which is used to discover the underlying business process models that are supported by existing information systems. Business process discovery techniques employ event logs as input, which are recorded by process-aware information systems. However, a wide variety of traditional information systems do not have any in-built mechanisms with which to collect events (representing the execution of business activities). Various mechanisms with which to collect events from non-process-aware information systems have been proposed in order to enable the application of process mining techniques to traditional information systems. Unfortunately, since business processes supported by traditional information systems are implicitly defined, correlating events into the appropriate process instance is not trivial. This challenge is known as the event correlation problem. This paper presents an adaptation of an existing event correlation algorithm and incorporates it into a technique in order to collect event logs from the execution of traditional information systems. The technique first instruments the source code to collect events together with some candidate correlation attributes. Based on several well-known design patterns, the technique provides a set of guidelines to support experts when instrumenting the source code. The event correlation algorithm is subsequently applied to the data set of events to discover the best correlation conditions, which are then used to create event logs. The technique has been semi-automated to facilitate its validation through an industrial case study involving a writer management system and a healthcare evaluation system. The study demonstrates that the technique is able to discover an appropriate correlation set and obtain well-formed event logs, thus enabling business process mining techniques to be applied to traditional information systems.

Business process mining is a new method that amalgamates business process modelling and analysis with data mining and machine learning techniques, whereby knowledge from event logs stored in today's information system is extracted to automatically construct business process models, identify bottlenecks, and improve the business processes. Since process mining has recently received attention among the research community, there are many challenges. The most important issues have been defined in the process mining manifesto. However, none of the published works investigated, or the defined challenges, have been solved and are still being encountered. Therefore, this paper provides a comprehensive and critical review of the literature in the context of process mining challenges. The contribution of this paper is to increase the maturity of the field of process mining by providing researchers with the state-of-the-art of the most important challenges of process mining.

International Journal of Contemporary Management » 2017 » Issue 16(3) » Innovative Consistency in Organisations – Business Process Management Perspective A A A

Predictive monitoring of temporally-aggregated performance indicators of business processes against low-level streaming events

A company's competitiveness relies heavily on its business processes and accurate knowledge to execute its business processes with agility and efficiency. Business Process Management (BPM) initially promised to provide the business world with suitable tools and techniques for successful BPM without help from the IT world. However, the current practice of BPM has several fundamental problems, including difficulty with automatic discovery and the integration of business processes across organizations. Understanding that the main cause of these problems lies in the lack of semantics on business process, we first define a variety of business process ontologies in order to build a semantic business process space (SBPS) for the limited area of sales order. We then explain how the SBPS satisfies the requirements for successful implementation of semantic BPM (SBPM) and demonstrate with a scenario how SBPM can be realized in the environment of SBPS. Our novel approach will reduce the time and cost necessary for the development of a new business process in a fast-changing environment and provide practitioners with useful insights into the proper implementation of the SBPM. Although our paper defines semantic business process knowledge for only a limited domain, its insights can be readily extended to other areas of business.

Process mining techniques provide capabilities for discovering the real business process flows from data, and compare expected and actual behaviors. Actual behaviors, in many cases, are obtained from Enterprise Resource Planning (ERP) systems and other enterprise information systems transaction logs. These transaction logs provide valuable insight into the companies' business processes. They traditionally hold a large amount of data in a set of conceptual documents related to each other through one-to-many and many-to-many relations, where information changes occur in transactions. Underlying data model gives rise to complex interactions between multiple data objects without a clear notion of a unique case identifier in an isolated process. However, enterprise process mining techniques can be applied only to event logs containing event data related to one notion of process instances. Within ERP systems, such event logs are not explicitly given and substantial domain knowledge is required to select the right data from multiple tables in relational databases. In order to respond to this need, in this paper we present an abstract syntax of domain specific language (DSL) for facilitating the extraction of an appropriate dataset from ERP systems by domain experts, and its conversion into event log based on XES IEEE standard. It is developed specifically to describe behavior over complex data from ERP systems in terms of multiple interacting artifacts. The goal is to align the data and process perspectives, supporting extraction of complex ambiguous cases, affected by data convergence and data divergence problems. The basic concepts of the language as well as principles are discussed in depth in this paper.

In last few years lot of work is carried out in process mining field by various researchers. Process mining deals with analysis and extraction of process related information from the event logs created by business processes. Predictive monitoring of business process is subfield of process mining which includes activities where event logs are analyzed to make various process specific predictions. The various machine learning and deep learning techniques have been proposed in predictive business process monitoring (BPM). The aim of these techniques is to predict next process event, remaining cycle time, deadline violations etc. of running process instance. The goal of this paper is to discuss the most representative deep learning approaches used for the runtime prediction of business process. The different types of deep learning approaches used in predictive BPM based on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Stacked Autoencoders have are highlighted in this paper. Also we are focusing on aspects like type of dataset, predicted values, type of data encoding and quality evaluation metrics for the categorization of these approaches. In this paper we have highlighted various research gaps in mentioned deep learning approaches which can be referred by other researchers in this field to enhance effectiveness of predictive BPM.

Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution: a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack.

Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information. • A requirement-driven meta-model method for standardized process-mining assessment. • An iterative approach for low-cost development of tailored in-house tools. • Improves decisions through focused data extraction and need-aligned analysis.

Process Mining (PM) is a research area that focuses on helping organizations optimize their business processes. PM provides process analysis tools and techniques with a wide range of applications described in the literature. Additionally, PM can be associated with Root Cause Analysis (RCA), a problem-solving technique based on the assumption that a problem can only be solved by addressing its underlying cause. In this study, we performed a case on a real-world Brazilian telecommunication company that provides telephony, television, and broadband Internet subscription services. We applied PM and RCA to identify the possible causes for short and long-duration services, high rework rates, and activity repetition. We split the dataset into three aspects: Support, Cancellation, and Sales related services and analyzed them separately. For RCA, we applied both decision tree classification and rule mining. We identified 53 rules over 9 decision trees that were validated by process specialists from the tech company.

Process mining empowers companies to optimise operational processes by deriving insights from event logs. It involves assessing event logs or the resultant process models against existing models and identifying challenges within executed processes to enhance their efficiency. However, a critical prerequisite for effective process mining is data cleaning, which simplifies the complexities inherent in real-world event data, making it amenable to comprehension, processing, and leveraging with process mining techniques. Consequently, new methodologies and approaches for event data pre-processing have garnered significant attention within the business process scientific community. This paper presents a systematic literature review summarising relevant techniques for event data pre-processing in the context of business process mining. The objective of this study is to categorise approaches and methodologies related to event data pre-processing while shedding light on the crucial issues associated with these techniques.

비즈니스 프로세스 및 워크플로우 기술의 국제표준화기구인 WfMCl)에서는 최근 비즈니스 프로세스 인텔리전스 마이닝 분야에 대한 산업체의 관심이 증가함에 따라 프로세스 실행이벤트로그 표준포맷인 비즈니스 프로세스 분석로그 포맷, BPAF2) 1.0을 공식적으로 발표한 바 있다. 즉, 비즈니스 프로세스 인텔리전스 마이닝 기술은 비즈니스 프로세스 모델의 실행이벤트로그로부터 제어흐름, 데이터흐름, 역할흐름, 수행자흐름 등의 흐름중심의 인텔리전스와 최근에 관심이 집중되는 프로세스기반 소셜네트워크, 소속성네트워크 등의 관계중심의 인텔리전스를 마이닝하는 일련의 알고리즘들과 분석기법들로 구성되는데 현재의 표준포맷인 BPAF 1.0은 비즈니스 프로세스의 제어흐름 인텔리전스 마이닝에 초점 맞추고 있어 최근에 관심이 집중되는 관계중심의 인텔리전스 마이닝을 지원할 수가 없다. 따라서, 본 표준화 논문에서는 제어흐름 인텔리전스 이외에 데이터흐름, 역할흐름, 수행자흐름의 흐름 중심 인텔리전스 뿐만 아니라 프로세스기반 소셜네트워크, 소속성 네트워크의 관계중심 인텔리전스의 마이닝을 지원할 수 있도록 기존의 BPAF 1.0 표준포맷을 확장한 BPAF 2.0 표준포맷을 제안한다. 특히, 본 논문에서 제안하는 BPAF 2.0은 한국정보통신기술협회 표준총회의 e 비즈니스 프로젝트 그룹을 통한 국내 표준안의 기반기술이 될 뿐 만 아니라 BPAF 1.0을 제정한 WfMC 국제표준화기구의 국제 표준안의 확장에 기여할 것이라고 판단한다. WfMC, which is one of the international standardization organizations leading the business process and workflow technologies, has been officially released the BPAF1.0 that is a standard format to record process instances' event logs according as the business process intelligence mining technologies have recently issued in the business process and workflow literature. The business process mining technologies consist of two groups of algorithms and their analysis techniques; one is to rediscover flow-oriented process-intelligence, such as control-flow, data-flow, role-flow, and actor-flow intelligence, from process instances' event logs, and the other has something to do with rediscovering relation-oriented process-intelligence like process-driven social networks and process-driven affiliation networks from the event logs. The current standardized format of BPAF1.0 aims at only supporting the control-flow oriented process-intelligence mining techniques, and so it is unable to properly support the relation-oriented process-intelligence mining techniques. Therefore, this paper tries to extend the BPAF1.0 so as to reasonably support the relation-oriented process-intelligence mining techniques, and the extended BPAF is termed BPAF2.0. Particularly, we have a plan to standardize the extended BPAF2.0 as not only the national standard specifications through the e-Business project group of TTA, but also the international standard specifications of WfMC.