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Business Process Analysis - Science topic
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How to conduct effective business process analysis?
What tools could help with analysis?
What is the role and contribution of business analysis to the computerized system development process?
Will the combination of AI technology, Big Data Analytics and the high power of quantum computers allow the prediction of multi-faceted, complex macroprocesses?
Will the combination of generative artificial intelligence technology, Big Data Analytics and the high power of quantum computers make it possible to forecast multi-faceted, complex, holistic, long-term economic, social, political, climatic, natural macroprocesses?
Generative artificial intelligence technology is currently being used to carry out various complex activities, to solve tasks intelligently, to implement multi-criteria processes, to create multi-faceted simulations and generate complex dynamic models, to creatively perform manufacturing processes that require processing large sets of data and information, etc., which until recently only humans could do. Recently, there have been attempts to create computerized, intelligent analytical platforms, through which it would be possible to forecast complex, multi-faceted, multi-criteria, dynamically changing macroprocesses, including, first of all, long-term objectively realized economic, social, political, climatic, natural and other macroprocesses. Based on the experience to date from research work on the analysis of the development of generative artificial intelligence technology and other technologies typical of the current Fourth Technological Revolution, technologies categorized as Industry 4.0/5.0, the rapidly developing various forms and fields of application of AI technologies, it is clear that the dynamic technological progress that is currently taking place will probably increase the possibilities of building complex intelligent predictive models for multi-faceted, complex macroprocesses in the years to come. The current capabilities of generative artificial intelligence technology in the field of improving forecasting models and carrying out forecasts of the formation of specific trends within complex macroprocesses are still limited and imperfect. The imperfection of forecasting models may be due to the human factor, i.e., their design by humans, the determination by humans of the key criteria and determinants that determine the functioning of certain forecasting models. In a situation where in the future forecasting models will be designed and improved, corrected, adapted to changing, for example, environmental conditions at each stage by artificial intelligence technology then they will probably be able to be much more perfect than the currently functioning and built forecasting models. Another shortcoming is the issue of data obsolescence and data limitation. There is currently no way to connect an AI-equipped analytical platform to the entire resources of the Internet, taking into account the processing of all the data and information contained in the Internet in real time. Even today's fastest quantum computers and the most advanced Big Data Analytics systems do not have such capabilities. However, it is not out of the question that in the future the dynamic development of generative artificial intelligence technology, the ongoing competition among leading technology companies developing technologies for intelligent chatbots, robots equipped with artificial intelligence, creating intelligent control systems for machines and processes, etc., will lead to the creation of general artificial intelligence, i.e. advanced, general artificial intelligence that will be capable of self-improvement. However, it is important that the said advanced general advanced artificial intelligence does not become fully autonomous, does not become completely independent, does not become out of the control of man, because there would be a risk of this highly advanced technology turning against man which would involve the creation of high levels of risks and threats to man, including the risk of losing the possibility of human existence on planet Earth.
I have described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:
OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT
In view of the above, I address the following question to the esteemed community of scientists and researchers:
Will the combination of generative artificial intelligence technology, Big Data Analytics and the high power of quantum computers make it possible to forecast multi-faceted, complex, holistic, long-term economic, social, political, climatic, natural macro-processes?
Will the combination of AI technology, Big Data Analytics and high-powered quantum computers allow forecasting of multi-faceted, complex macro-processes?
And what is your opinion about it?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
The above text is entirely my own work written by me on the basis of my research.
In writing this text I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz

I want to use AI models (black box) to identify anomalies in business processes. In your opinion, what are the advantages and disadvantages compared to common approaches such as conformance checking (white box)? (Is there any existing research about this?)
Recently, several works have been published on predictive analytics:
- Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm by Gyunam Park and Minseok Song (https://ieeexplore.ieee.org/abstract/document/8786063)
- Using Convolution Neural Networks for Predictive Process Analytics by Vincenzo Pasquadibisceglie et al. (https://ieeexplore.ieee.org/document/8786066)
Besides, there is a paper on how to discover a process model using neural networks:
My questions for this discussion are:
- It seems, that the field for machine learning approaches in process mining in not limited to predictions/discovery. Can we formulate the areas of possible applications?
- Can we use process mining techniques in machine learning? Can we, for example, mine how neural networks learn (in order to better understand their predictions)?
- If you believe that the subjects are completely incompatible, then, please, share your argument. Why do you think so?
- Finally, please, share known papers in which: process mining (PM) is applied in machine learning (ML) research, ML is applied in PM research, both PM and ML are applied to solve a problem. I believe, this will be useful for any reader of this discussion.
A number of stakeholders are interested in the well being of a company. Is it possible to predict corporate bankruptcy.
Dear Researchers,
I'm currently doing a survey on the intuitive understanding of complexity of objects
and would like to ask you for 8 minutes of your time to answer a few very
simple questions from the survey[1]. This will hopefully help to better
understand and characterise the complexity of objects. The survey does
not gather or store any kind of personal information, and it does
not require any prior knowledge or skills. Your help is greatly appreciated.
With kind regards
Felix Baumann
BPO revenues is the regressor variable that I am interested in. The other multiple variables would each be a regressand. I suppose that I could run multiple regressions, one for each regressand. I was wondering if there is a more appropriate model, possibly one that would include all the variables already.
I'm looking for a dataset that contains transactional data (i. e. it must contain different cases identified by unique IDs that appear with several action and timestamps throughout the log) as well as free text.
Let me give you an example of how this could look like: incident service management, where (free text) complaints can be submitted and then be processed by several resources until resolved.
Is anyone aware of such dataset? Thanks in advance,
Tim
After an extensive search in the documentation and many failed attempts: jBPM6 does not seem to support it, although jBPM 5 HAD a human task service that could be called remotely.
I also checked Camunda BPM and Activiti.
what does measure or item in accounting statement represent , or measure for evaluating performance of firm? does evaluating performance depend on viable in SPSS program.
Hi everyone,
In order to assess complexity of processes, we are trying to define measures that can help. Inspired from the McCabe, Halstead measures for algorithmic complexity, our research work tries to set up an assessment approche in order to give objective metrics describing structural complexity of processes.
If any one has any reading, reference or idea that can help. Please feel free to participate to this discussion.
Many thanks
How and why have you been modelling this? Procurement processes describe the internal and the external data been exchanged between parties (End user - Contractor-supplier).
We would like to apply BPMN in the socialization processes
PMP= Project Management Professional
CBA= Certified Business Analyst
Bloomberg provides that list with all info about the relation between a firm and its supplier in term of the amount of selling and buying for each one but for only the current year . Also, Apple provides its supplier list in its website but without any info.
I am looking for a secondary data resource to obtain those lists for at least for five years without conducting a survey or interview.
While turf wars may play a part, the debate about Enterprise and Systems governance is rooted in a more serious argument, namely, how the divide between enterprise and systems architectures may affect decision-making, and that put knowledge in the middle.
I think the implementation of business process approach in organization will lead to the reinforcement of social capital.
I am looking for SOP's, Workflows, best practises and so on. This is to support several SME's and develop softwares for them accordingly.
Assuming that we are decomposing business capabilities, then identifying business processes. The first level of processes should be end-to-end. After this initial approach, we start drilling and making the decomposition to additional layers.
However, if we are making the decomposing considering business objects concepts, we know when to stop in the decomposition. We drilled until we found processes or sub processes using business objects (data entities) related to the process context, in a way that could be easily managed. Business objects in this way are a sort of referential to start from the bottom and organizing the processes or to start from the top until their identification. That facilitates the identification of processes and the associated activities needed to support the business objects, moving them from one state to another.
Where can I find information or a theoretical framework that could support first what is business objects? The information that I found is more related to SAP.
Some guidelines are appreciated!
I am planning to model the business process quality. What are the main parameters to consider? Any interesting methods (I have genetic algorithms and process raw data in mind)? Any opensource tools for data collection?