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Page 1. Page 2 Data explosion Moore’s law: predicted in 1965 that the number of components in integrated circuits would double every year. For example,

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Presentation on theme: "Page 1. Page 2 Data explosion Moore’s law: predicted in 1965 that the number of components in integrated circuits would double every year. For example,"— Presentation transcript:

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3 Data explosion Moore’s law: predicted in 1965 that the number of components in integrated circuits would double every year. For example, the number of transistors on integrated circuits has been doubling every two years. Disk capacity, performance of computers per unit cost, the number of pixels per dollar, etc. have been growing at a similar pace The IDC Digital Universe Study of May 2010 illustrates the spectacular growth of data. This study estimates that the amount of digital information (cf. personal computers, digital cameras, servers, sensors) stored exceeds 1 Zettabyte (ZB, or 2 70 byes) and predicts that the “digital universe” will to grow to 35 Zettabytes in 2010 Page 3

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7 Physical universe vs. digital universe “The state of a bank” is mainly determined by the data stored in the bank’s information system. Money has become a predominantly digital entity. Nowadays, when the booking of an HSR seat is successful, the customer can receive an e-ticket that can be carried by a smart phone. 政府推動電子發票全面無紙化,財稅資料中心高級 分析師盧志山今天表示,自三月一日起,民眾只要 上網輸入自己手機號碼,經驗證就可下載一組專屬 的條碼當做「共同型載具」,只要帶著這組條碼就 可儲存所有發票資料,雲端系統還會自動幫忙「對 獎」,環保又便利。 Page 7

8 Physical universe vs. digital universe (cont) When the ERP system of a manufacturer indicates that a particular product is out of stock, it is impossible to sell or ship the product even when it is available in physical form. Technologies such as RFID, GPS, and sensor networks will stimulate a further alignment of the digital universe and the physical universe. Page 8

9 Data explosion challenge One of the main challenges of today’s organizations is to extract information and value from data stored in their information systems. The importance of information systems is not only reflected by the spectacular growth of data, but also by the role that these systems play in today’s business processes as the digital universe and the physical universe are becoming more and more aligned. Page 9

10 Data explosion challenge (cont) The growth of a digital universe that is well-aligned with processes in organizations makes it possible to record and analyze events. The challenge is to exploit event data in a meaningful way, for example, – to provide insights, – identify bottlenecks, – anticipate problems, – record policy violations, – recommend countermeasures, – and streamline processes. That is what process mining is all about. Page 10

11 Process mining vs BPM vs WFM Process mining, i.e., extracting valuable, process-related information from event logs, complements existing approaches to Business Process Management (BPM). BPM is the discipline that combines knowledge from information technology and knowledge from management sciences and applies this to operational business processes. BPM can be seen as an extension of Workflow Management (WFM). Page 11

12 Process mining vs BPM vs WFM (cont) WFM primarily focuses on the automation of business processes, whereas BPM has a broader scope: from process automation and process analysis to process management and the organization of work. BPM aims to improve operational business processes; for example, by modeling a business process and analyzing it using simulation, management may get ideas on how to reduce costs while improving service levels. Page 12

13 Process-Aware Information Systems Process-Aware Information Systems (PAISs) include the traditional WFM systems, but also include systems that provide more flexibility or support specific tasks. For example, larger ERP systems (SAP, Oracle), CRM systems can be seen as process-aware. These systems have in common that there is an explicit process notion and that the information system is aware of the processes it supports. Page 13

14 Process-Aware Information Systems (cont) BPM and PAIS have in common that they heavily rely on process models. A plethora of notations exists to model operational business processes (e.g., Petri nets, BPMN, UML, and EPCs). Page 14

15 Process-Aware Information Systems (cont) The ordering of these activities is modeled by describing casual dependencies. Moreover, the process model may also describe temporal properties, specify the creation and use of data, e.g., to model decisions, and stipulate the way that resources interact with the process (e.g., roles, allocation rules, and priorities) Page 15

16 Fig. 1.1 A Petri net modeling the handling of compensation request Page 16

17 Fig. 1.2 The same process modeled in terms of BPMN Page 17

18 Modeling Business Processes Figures 1.1 and 1.2 show only the control-flow, this is a rather limited view on business processes. Therefore, most modeling languages offer notations for modeling other perspectives such as the organizational or resource perspective (“The decision needs to be made by a manager”), the data perspective (“After four iteration always a decision is made unless more than 1 million Euro is claimed”), and the time perspective (“After two weeks the problem is escalated”). Page 18

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20 Process Models --- Informal and Formal Process models play important role in larger organizations. Typically, two types of models are used: (a) informal models and (b) formal models (also referred to as “executable models). Informal models are used for discussion and documentation whereas formal models are used for analysis or enactment (i.e., the actual execution of process). 20

21 Process Models --- Informal and Formal (cont) On the one hand of the spectrum there are “PowerPoint diagrams” showing high-level processes whereas on the other end of the spectrum there are process models captured in executable code. Whereas informal models are typically ambiguous and vague, formal models tend to have a rather narrow focus or are too detailed to be understandable by the stakeholders. 21

22 Process Models --- Informal and Formal (cont) The value of models is limited if too little attention is paid to the alignment of model and reality. For example, it makes no sense to conduct simulation experiments while using a model that assumes an idealized model---incorrect redesign decisions are made. It is also precarious to start a new implementation project guided by process models that hide reality. 22

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24 Fig. 1.3 The BPM life-cycle showing the different uses of process models To position process mining, we first describe the so-called BPM life-cycle using Fig The life-cycle describes the different phases of managing a particular business process. Page 24

25 Process Mining Process mining offers the possibility to truly “close” the BPM life-cycle. Data recorded by information systems can be used to provide a better view on the actual processes. Process mining is a relative young research discipline that sits between machine learning and data mining on the one hand and process modeling and analysis on the other hand. The idea of process mining is to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today’s systems. Figure 1.4 shows that process mining establishes links between the actual processes and their data on the one hand and process models on the other hand. Page 25

26 Fig. 1.4 Positioning of the three main types of process mining: discovery, conformance, and enhancement Page 26

27 Process Mining All of the PAISs just mentioned directly provide such event logs. However, most information systems store such information in unstructured form. In such cases, event data exist but some efforts are needed to extract them. Data extraction is an integral part of any process mining effort. Assume that it is possible to sequentially record events such that each event refers to an activity and is related to a particular case. Consider, for example, the handling of requests for compensation modeled in Fig The cases are individual requests and per case a trace of events can be recorded. An example of a possible trace is 《 register request, examine casually, check ticket, decide, reinitiate request, check ticket, examine thoroughly, decide, pay compensation 》. There are two decide events that occurred at different times, produced different results, and may have been conducted by different people. It is important to distinguish these two decisions. Therefore, most event logs store additional information about events. Page 27

28 Process Mining Whenever possible, process mining techniques use extra information such as the resource (i.e., person or device) executing or initiating the activity, the timestamp of the event, or data elements recorded with the event (e.g., the size of an order). Event logs can be used to conduct three types of process mining as shown in Figure 1.4. – Discovery – Conformance – Enhancement Page 28

29 Process Mining Process models such as depicted in Figs. 1.1 and 1.2 show only the control-flow. However, when extending process models, additional perspectives are added. Moreover, discovery and conformance techniques are not limited to control-flow. For example, one can discover a social network and check the validity of some organizational model using an event log. Hence, orthogonal to the three types of mining (discovery, conformance, and enhancement), different perspectives can be identified. Page 29

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31 Orthogonal: Perspectives Note that the different perspectives are partially overlapping and non-exhaustive. Nevertheless, they provide a good characterization of the aspects that process mining aims to analyze. In most examples given thus far it is assumed that process mining is done off-line, however, more and more process mining techniques can also be used in an online setting. We refer to this as operational support. This illustrates that the “process mining spectrum” is broad and not limited to process discovery. In fact, today’s process mining techniques are indeed able to support the whole BPM life-cycle shown in Fig Process mining is not only relevant for the design and diagnosis/requirements phases, but also for the enactment/monitoring and adjustment phases. Page 31

32 Table 1.1 A fragment of some event log: each line corresponds to an event Page 32

33 Analyzing an Example Log Table 1.1 illustrates the typical information present in an event log. Depending on the process mining technique used and the questions at hand, only part of this information is used. The minimal requirements for process mining are that any event can be related to both a case and an activity and that events within a case are ordered. Hence, the “case id” and “activity” columns in Table 1.1 represent the bare minimum for process mining. By projecting the information in these two columns, we obtain the more compact representation shown in Table 1.2. In this table, each case is represented by a sequence of activities also referred to as trace. For clarity, the activity names have been transformed into single-letter labels, e.g., a denotes activity register request. Page 33

34 Table 1.2 A more compact representation of log shown in Table 1.1 Page 34

35 Analyzing an Example Log Process mining algorithms for process discovery can transform the information shown in Table 1.2 into process models. Figure 1.5 shows the resulting model with the compact labels just introduced. It is easy to check that all six traces in Table 1.2 are possible in the model. Page 35

36 Fig. 1.5 The process model discovered by the α-algorithm based on the set of traces {,,, } Page 36

37 Analyzing an Example Log The Petri net shown in Fig. 1.5 also allows for traces not present in Table 1.2. This is a desired phenomenon as the goal is not to represent just the particular set of example traces in the event log. Process mining algorithms need to generalize the behavior contained in the log to show the most likely underlying model that is not invalidated by the next set of observations. One of the challenges of process mining is to balance between “overfitting” (the model is too specific and only allows for the “accidental behavior” observed) and “underfitting” (the model is too general and allows for behavior unrelated to the behavior observed). Page 37

38 Fig. 1.6 The process model discovered by the α-algorithm based on Cases 1 and 4, i.e., the set of traces {, } Page 38

39 Another example This model only allows for two traces and these are exactly the ones in the small event log. b and d are modeled as being concurrent because they can be executed in any order. For larger and more complex models, it is important to discover concurrency. Not modeling concurrency typically results in large “Spaghetti-like” models in which the same activity needs to be duplicated. The α-algorithm is just one of many possible process discovery algorithms. For real-life logs, more advanced algorithms are needed to better balance between “overfitting” and “underfitting” and to deal with “incompleteness” (i.e., logs containing only a small fraction of the possible behavior due to the large number of alternatives) and “noise” (i.e., logs containing exceptional/infrequent behavior that should not automatically be incorporated in the model) Page 39

40 Fig. 1.4 Positioning of the three main types of process mining: discovery, conformance, and enhancement Page 40

41 Table 1.3 Another event log: Cases 7, 8, and 10 are not possible according to Fig. 1.5 Page 41

42 Analyzing an Example Log As explained in Sect. 1.3, process mining is not limited to process discovery. Event logs can be used to check conformance and enhance existing models. Moreover, different perspectives may be taken into account. Conformance checking techniques aim at discovering such discrepancies. Note that conformance can be viewed from two angles: (a) the model does not capture the real behavior (“the model is wrong”) and (b) reality deviates from the desired model (“the event log is wrong”). The original event log shown in Table 1.1 also contains information about resources, timestamps and costs. Such information can be used to discover other perspectives, check the conformance of models that are not pure control-flow models, and to extend models with additional information. Page 42

43 Analyzing an Example Log For example, one could derive a social network based on the interaction patterns between individuals. The social network can be based on the “handover of work” metric, i.e., the more frequent individual x performed an activity that is causally followed by an activity performed by individual y, the stronger the relation between x and y is. Figure 1.7 illustrates the way in which a control-flow oriented model can be extended with the three other main perspectives mentioned in Sect Page 43

44 Fig. 1.7 The process model extended with additional perspectives Page 44

45 Extension Techniques for organizational process mining will discover such organizational structures and relate activities to resources through roles. By exploiting resource information in the log, the organizational perspective can be added to the process model. Similarly, information on timestamps and frequencies can be used to add performance related information to the model. Figure 1.7 sketches that it is possible to measure the time that passes between an examination (activities b or c) and the actual decision (activity e). If this time is remarkably long, process mining can be used to identify the problem and discover possible causes. If the event log contains case-related information, this can be used to further analyze the decision points in the process. Page 45

46 Extension Using process mining, the different perspectives can be cross-correlated to find surprising insights. Examples of such findings could be: “requests examined by Sean tend to be rejected more frequently”, “requests for which the ticket is checked after examination tend to take much longer”, “requests of less than € 500 tend to be completed without any additional iterations”. Moreover, these perspectives can also be linked to conformance questions. For example, it may be shown that Pete is involved in relatively many incorrectly handled requests. These examples show that privacy issues need to be considered when analyzing event logs with information about individuals (see Sect ). Page 46

47 Play-in, Play-out, and Replay One of the key elements of process mining is the emphasis on establishing a strong relation between a process model and “reality” captured in the form of an event log. we use the terms Play-in, Play-out, and Replay to reflect on this relation. Page 47

48 Play-out can be used both for the analysis and the enactment of business processes. A workflow engine can be seen as a “Play-out engine” that controls cases by only allowing the “moves” allowed according to the model. Hence, Play-out can be used to enact operational processes using some executable model. Page 48

49 Play-in is the opposite of Play-out, i.e., example behavior is taken as input and the goal is to construct a model. Play-in is often referred to as inference. The α-algorithm and other process discovery approaches are examples of Play-in techniques. Most data mining techniques use Play-in, i.e., a model is learned on the basis of examples. However, traditionally, data mining has not been concerned with process models. Unfortunately, it is not possible to use conventional data mining techniques to Play-in process models. Only recently, process mining techniques have become readily available to discover process models based on event logs. Page 49

50 Replay uses an event log and a process model as input. The event log is “replayed” on top of the process model. 50

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