In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.

In the age of Industry 4.0 and Cyber-Physical Production Systems (CPPSs) vast amounts of potentially valuable data are being generated. Methods from Machine Learning (ML) and Data Mining (DM) have proven to be promising in extracting complex and hidden patterns from the data collected. The knowledge obtained can in turn be used to improve tasks like diagnostics or maintenance planning. However, such data-driven projects, usually performed with the Cross-Industry Standard Process for Data Mining (CRISP-DM), often fail due to the disproportionate amount of time needed for understanding and preparing the data. The application of domain-specific ontologies has demonstrated its advantageousness in a wide variety of Industry 4.0 application scenarios regarding the aforementioned challenges. However, workflows and artifacts from ontology design for CPPSs have not yet been systematically integrated into the CRISP-DM. Accordingly, this contribution intends to present an integrated approach so that data scientists are able to more quickly and reliably gain insights into the CPPS. The result is exemplarily applied to an anomaly detection use case.

Artificial Intelligence (AI) applications in automation systems are usually distributed systems whose development and integration involve several experts. Each expert uses its own domain-specific modeling language and tools to model the system elements. An interdisciplinary graphical modeling language that enables the modeling of an AI application as an overall system comprehensible to all disciplines does not yet exist. As a result, there is often a lack of interdisciplinary system understanding, leading to increased development, integration, and maintenance efforts. This paper therefore presents a graphical modeling language that enables consistent and understandable modeling of AI applications in automation systems at system level. This makes it possible to subdivide individual subareas into domain specific subsystems and thus reduce the existing efforts.

Anyone who has been following the news on AI in 2022 knows of the high rate of AI project failures. Somewhere between 60-80% of AI projects are failing according to different news sources, analysts, experts, and pundits. However, hidden among all that doom and gloom are the organizations who are succeeding. What are those 20% of organizations doing that are setting themselves apart from the failures, leading their projects to success? Surprisingly, it has nothing to do with the people they hire or the technology or products they use.

First, let us clarify what CRISP-DM (short for cross-industry standard process for data mining) is. As its name suggests, CRISP-DM is a process model that can be employed to structure the analysis of data in different fields. The process consists of six major phases (see Figure 1 below). It is important to note that the process is highly non-linear and moving back and forth between different stages is the norm rather than an exception. On a high level, we can regard our own careers and goals as data scientists as (big) projects and use CRISP-DM to move forward as we would do with any other project.

Udemy NED Data Science- Project Management Methodology - CRISP-DM CRISP-DM has been consistently the most commonly used methodology for analytics, data mining and data science projects (per KDnuggets polls starting in ... New What you'll learn Learn about Amazing Project Management Methodology (CRISP-DM) in Handling Data Science & Artificial Intelligence Projects.Requirements Knowledge of Data Science Basics is recommended but not mandatory.Description Learners will understand about Project management methodology - CRISP-DM, in handling Data Science projects or Artificial Intelligence projects end to end. This course includes a structured approach of handling the data related projects for maximizing the success rate.Who this course is for: Data Science Beginners, Intermediate and Advanced users, Artificial Intelligence Beginners, Intermediate and Advanced users. Knowledge of Data Science Basics is recommended but not mandatory. Knowledge of Data Science Basics is recommended but not mandatory. Learners will understand about Project management methodology - CRISP-DM, in handling Data Science projects or Artificial Intelligence projects end to end.

We propose a process model for the development of machine learning applications. It guides machine learning practitioners and project organizations from industry and academia with a checklist of tasks that spans the complete project life-cycle, ranging from the very first idea to the continuous maintenance of any machine learning application. With each task, we propose quality assurance methodology that is drawn from practical experience and scientific literature and that has proven to be general and stable enough to include them in best practices. We expand on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks.

REGARDLESS of industry, artificial intelligence (AI) is a disruptive technology that is greatly sought after. Many organizations are looking to deploy AI projects at scale, in hopes of boosting performance and ultimately increasing revenues. However, many fail to see returns on their AI investments. Often, this is because AI projects are not approached in the right manner. To be AI-first, organizations need to adopt a data-first mindset.

Those that implement AI and Machine Learning project learn quickly that machine learning projects are not application development projects. Much of the value of machine learning projects rest in the models, training data, and configuration information that guides how the model is applied to the specific machine learning problem. The application code is mostly a means to implement the machine learning algorithms and "operationalize" the machine learning model in a production environment. That's not to say that application code is not necessary -- after all, the computer needs some way to operationalize the machine learning model -- but focusing a machine learning project on the application code is missing the big picture. If you want to be AI-first for your project, you need to have a data-first perspective.

Companies of all sizes are implementing AI, ML, and cognitive technology projects for a wide range of reasons in a disparate array of industries and customer sectors. Some AI efforts are focused on the development of intelligent devices and vehicles, which incorporate three simultaneous development streams of software, hardware, and constantly evolving machine learning models. Other efforts are internally-focused enterprise predictive analytics, fraud management, or other process-oriented activities that aim to provide an additional layer of insight or automation on top of existing data and tooling. Yet other initiatives are focused on conversational interfaces that are distributed across an array of devices and systems. And others have AI & ML project development goals for public or private sector applications that differ in more significant ways than these.