Modeling transition systems

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use big data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis.

The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis e. Process mining seeks the confrontation between event data i. This technology has become available only recently, but it can be applied to any type of operational processes organizations and systems.

Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models.

Hence, we refer to this as "data science in action". The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms.

These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

This course starts with an overview of approaches and technologies that use event data to support decision making and business process re design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. The course covers the three main types of process mining. The first type of process mining is discovery.

A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model a Petri net explaining the behavior recorded in the log. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.

The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model.The Systems Model of Change or Organization-Wide Change lays more emphasis on the fact that a change must be implemented organization-wide instead of implementing it in piecemeal.

This model provides a whole new dimension to the concept of organizational change and describes the role played by six interconnected or interdependent variables like people, task, strategy, culture, technology and design. All these 6 variables are the key focus of planned change. The model has been represented in the diagram below:.

People: This variable involves the individuals who work in an organization. Task: The task is related to the nature of work which an individual handles in an organization. The nature of the job may be simple or complex, repetitive or novel, unique or standardized. Design: This variable refers to the organizational structure itself and also the system of communication, authority and control, the delegation of responsibilities and accountabilities.

Strategy: The organizational strategy is the road map of action for realizing the future goals both short term and long term in nature. Strategic Planning involves identification of existing resources, a careful assessment of internal strengths and weaknesses, identifying the opportunities in the environment and threats as well for a competitive advantage.

Technology: It takes into consideration the advancements in the technology in the field of IT, automation, new methods and techniques for enhancing productivity, the introduction of new processes and best practices for remaining ahead in the competition. Culture: It takes into consideration the shared beliefs, practices, values, norms and expectations of the members of the organization.

All the six variables as per the Systems Model of Organizational Change are interrelated and interdependent. A change in a single variable will result in the one or more variables.

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This will ultimately be having an affect on the people of the organization in terms of changes in their behaviours or attitudes. Moreover, organizational redesign may result in a cultural change by either modifying or reinforcing the existing culture.

The Systems Approach of Change Management is a useful model, which helps the managers or employees in understanding that a change can never be implemented partly, rather it must be wholistic in nature by taking into consideration all the interrelated variables and their influence on each other. This Model of Change views the entire process of change from the top management perspective and considers change to be a continuous process.

modeling transition systems

Source: Armenakis et al. According to this model, certain forces trigger a need for organizational change and the top management is involved in a problem solving and a decision making process for identifying the alternatives or solutions to the problems. The top management clearly defines their goals or objectives, reforms in the processes or change in the output which is expected to be attained at the end of the process of change. During the early stages of change management, the top management may seek the support of a change agent, who will be responsible for driving the entire change effort.

The change agent may help the management in identifying and defining the problems, or the change agent may also help in generating the alternative plans of action or solutions to the problem.

The change agent may be an insider, or an outsider may be an external consultant or a representative from the Head Quarter who might not be known to the employees of the organization experiencing the process of change. Measurement, evaluation and control is the last step. During this stage, the change agent as well as the top management, evaluate the degree to which a change has been effectively implemented in an organization and how far it has yielded the desired outcomes.

The Change Agent works along with the individuals, groups, departments and various levels of management through the various phases of change process.

The Change agent implements new ideas and provides alternative approaches to the organizational members for dealing with the problems.Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Data science is the profession of the future, because organizations that are unable to use big data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis.

The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis e. Process mining seeks the confrontation between event data i. This technology has become available only recently, but it can be applied to any type of operational processes organizations and systems. Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine.

All of these applications have in common that dynamic behavior needs to be related to process models.

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Hence, we refer to this as "data science in action". The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented.

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Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. This course starts with an overview of approaches and technologies that use event data to support decision making and business process re design.

Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. The course covers the three main types of process mining.

The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model a Petri net explaining the behavior recorded in the log. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process.

modeling transition systems

Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log.

Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support.Systems engineers have always leveraged many kinds of models, including functional models to support requirements development, simulation models to analyze the behavior of systems, and other analytical models to analyze various aspects of the system such as reliability, safety, mass properties, power consumption, and cost.

However, the discipline still relies heavily on document-based artifacts to capture much of the system specification and design information, such as requirements, interface control documentation, and system architecture design descriptions. This information is often spread across many different documents including text, informal drawings, and spreadsheets.

Transitioning Systems Engineering to a Model-based Discipline

This document-based approach to systems engineering suffers from a lack of precision, inconsistencies from one artifact to another, and difficulties in maintaining and reusing the information. Model-based systems engineering MBSE Model-based systems engineering MBSE is the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing through development and later life cycle phases INCOSE A distinguishing characteristic of an MBSE approach is that the model model constitutes a primary artifact of the systems engineering process.

The focus on developing, managing and controlling a model of the system is a shift from the traditional document-based approach to systems engineering, where the emphasis is on producing and controlling documentation about the system. By leveraging the system model as a primary artifact, MBSE offers the potential to enhance product quality, enhance reuse of the system modeling artifacts, and improve communications among the systems development team.

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This, in turn, offers the potential to reduce the time and cost to integrate and test the system, and significantly reduce cost, schedule, and risks in fielding a system. MBSE includes a diverse set of descriptive and analytical models that can be applied throughout the life cycle, and from system of systems SoS system of systems SoS modeling down to component modeling. Typical models may include descriptive models of the system architecture that are used to specify and design the system, and analytical models to analyze system performance, physical characteristics, and other quality characteristics such as reliability, maintainability, safety, and cost.

MBSE has been evolving for many years. Simulations have been extensively used across industry to provide high fidelity performance analysis of complex systems. A modeling formalism called the enhanced functional flow block diagram Long has been used to model many different types of systems.

Several other domain specific modeling languages have been introduced as well. System models are adapted to the application domain, and include a broad spectrum of models for representing all aspects of systems. The use of internet driven knowledge representation and immersive technologies enable highly efficient and shared human understanding of systems in a virtual environment that span the full life cycle from concept through development, manufacturing, operations, and support.

Transition system

This requires both advancements in the practice, and the need to achieve more widespread adoption of MBSE within organizations across industry sectors.

Advancing the practice requires improvements in the modeling languages, methods, and tools. The modeling languages must continue to improve in terms of their expressiveness, precision, and usability. MBSE methods, such as those highlighted in A Survey of Model-Based Systems Engineering MBSE Methodologies Estefanhave continued to evolve, but require further advancements to provide a rigorous approach to modeling a system across the full system lifecycle, while being more adaptable to a diverse range of application domains.

The modeling tools must also continue to evolve to support the modeling languages and methods, and to integrate with other multi-disciplinary engineering models and tools in support of the broader model-based engineering effort.

The movement towards increased use of modeling standards, that are more widely available in commercial tools, and rigorous model-based methodologies, increase the promise of MBSE.

Lecture 16 - Transition Systems [PoM-CPS]

However, as indicated in the SE VisionMBSE is still being applied in pockets within organizations and unevenly across industry sectors. Similar to the evolution of model-based approaches in other disciplines such as mechanical and electrical engineering, the transition occurs incrementally as the methods and tools mature. This requires organizations to provide an infrastructure that includes MBSE methods, tools, and training, and a commitment to deploy this capability to their programs.

As with any organizational change, this must be approached strategically to grow this capability and learn from their experiences. Like other engineering disciplines, the transition of systems engineering to a model-based discipline is broadly recognized as essential to meet the challenges associated with increasing system complexity and achieving the productivity and quality improvements.

The SEBoK will continue to reflect the growing body of knowledge to facilitate this transition. Estefan, J. Accessed April 13, In theoretical computer sciencea transition system is a concept used in the study of computation. It is used to describe the potential behavior of discrete systems. It consists of states and transitions between states, which may be labeled with labels chosen from a set; the same label may appear on more than one transition.

If the label set is a singletonthe system is essentially unlabeled, and a simpler definition that omits the labels is possible. Transition systems coincide mathematically with abstract rewriting systems as explained further in this article and directed graphs.

They differ from finite state automata in several ways:. A transition from state p to state qi. Labels can represent different things depending on the language of interest. Typical uses of labels include representing input expected, conditions that must be true to trigger the transition, or actions performed during the transition. Labelled transitions systems were originally introduced as named transition systems.

This can be rephrased in terms of category theory. There are many relations between these concepts. Some are simple, such as observing that a labelled transition system where the set of labels consists of only one element is equivalent to an unlabelled transition system. However, not all these relations are equally trivial.

As a mathematical object, an unlabeled transition system is identical with an unindexed abstract rewriting system.

modeling transition systems

If we consider the rewriting relation as an indexed set of relations, as some authors do, then a labeled transition system is equivalent to an abstract rewriting system with the indices being the labels. The focus of the study and the terminology are different however. In a transition system one is interested in interpreting the labels as actions, whereas in an abstract rewriting system the focus is on how objects may be transformed rewritten into others.

In model checkinga transition system is sometimes defined to include an additional labeling function for the states as well, resulting in a notion that encompasses that of Kripke structure.

From Wikipedia, the free encyclopedia. This article is about transition systems as used in operational semantics.Withdrawal symptoms from smartphone addiction A common warning sign of smartphone or Internet addiction is experiencing withdrawal symptoms when you try to cut back on your smartphone use. These may include: Restlessness Anger or irritability Difficulty concentrating Sleep problems Craving access to your smartphone or other device Are you addicted to your smartphone.

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Systems Model of Change Management and Continuous Change Process Model

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