Machine-generated Data/ machine Data
Machine-generated data (MGD) refers to information generated by mechanical or digital devices. It commonly encompasses data produced by an organization's industrial control systems and mechanical devices specifically designed for performing a singular function.
This pertains to the exchange of information among machines, such as between control units that manage business or operational processes, and sensors or actuators.
A Managed Service refers to a software offering that is provided and maintained by a third-party provider. This can include a variety of services such as AWS EC2, Azure SQL Database, and GCP Pub/Sub, as well as other software offerings that can be utilized by an application.
One of the three levels of interoperability within Gaia-X is the Federated level, which is an extension of the common digital governance provided by the Federators of the relevant ecosystems. This level does not cover specific ecosystem governance rules as they are considered out of scope for Gaia-X.
A Kubernetes manifest is a description of a Kubernetes API object in YAML or JSON format that specifies the desired state of the object. When a manifest is applied to a Kubernetes cluster, Kubernetes will ensure that the actual state of the object matches the desired state specified in the manifest. A single configuration file can contain multiple manifests for different Kubernetes objects.
On the Internet, the term "Mashup" describes the creation of new content by integrating and processing existing content. Originally, the term comes from music. There are various software technologies for this kind of creative work.
Massive Open Online Courses
MOOCs are online courses that have a theoretically unlimited number of participants. They use a combination of traditional knowledge delivery methods such as videos and reading materials, as well as interactive forums where instructors and learners can collaborate and build communities.
Mean Time Between Failures
Mean Time Between Failures (MTBF) describes the expected amount of time a system, facility, or system component can spend in normal operation before a failure occurs.
Medical Sensor Platforms
A central data hub or marketplace for sensor data from various medical applications enables the integration and improved availability of information.
A Kubernetes community member who makes continuous contributions to the project. These members may be assigned issues and pull requests (PRs) to work on, participate in special interest groups (SIGs) through GitHub teams, and have their PRs automatically run through pre-submit tests. To maintain membership status, a member is expected to continue actively contributing to the community.
Message Queuing Telemetry Transport Protocol (MQTT)
A messaging protocol for the Internet of Things (IoT), standardized by OASIS.
Meta Data Broker
IDS-G specification "Meta Data Broker" Shortcut: IDS-MDB
The central data management of smart metering is called MDM. It is essential for processing the recorded consumption data.
In the energy industry, the term "metering point" refers to the location where the energy supplier provides its supply services to the consumer. This location is represented by a meter or a group of metering points. Each metering point is given a standardized description and a metering point number.
A non-invasive method for creating narrow trenches or slots (with a width of 4 cm to 20 cm) through cutting or milling is used to install micro-ducts, micro-cables, or fiber optic cable routes. Initial experiences show that micro and mini trenching can reduce civil engineering costs for broadband expansion in the respective deployment areas by approximately one-fourth to one-third.
Minikube is a tool that enables users to run Kubernetes clusters locally on their machines. It sets up a single-node cluster within a virtual machine (VM) on the user's computer, allowing them to experiment and learn Kubernetes in a local environment.
Minimal Viable Product
The term "Minimal Viable Product" is used in the context of Catena-X and the lean startup environment to refer to the first iteration of a product that is minimally functional, allowing the needs of customers and users to be learned. This is often in the form of a mock-up or demonstrator, which is different from the actual marketable product. The Minimal Viable Product is a basis for incremental improvement and was formerly known as the Catena-X Speedboat, which included the portal, shared services, and two business cases for circular economy and traceability.
Machine Learning Model Operationalization Management provides an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.
Modal travel shift
The act of transitioning from one mode of transportation to another, often with the aim of reducing environmental impact or enhancing efficiency.
Model (in machine learning)
In the realm of machine learning, a model is a conceptualization of real-world phenomena that has been simplified for practical purposes. By utilizing a learning algorithm, a statistical model can be optimized to generalize the training data. The model, in turn, can be utilized to process new data and calculate a function. Decision trees, regression curves, and artificial neural networks are all examples of popular model types.
Breaking down a system into modules with minimal interdependence is recommended. This means that each module should be self-contained and provide certain predefined skills, which can be hierarchically ordered. This allows for greater flexibility in making changes to the system and easier maintenance over time.
Motion Data provides geo-information-based analytics obtained from anonymized signaling data from the mobile network of Deutsche Telekom. Based on these insights, there can be multiple use cases developed around user activity movement and can assist businesses to take informed strategic decisions.
Information that provides the average time between two failures or outages (MTBF - Mean Time Between Failures).
Multi-Channel Learning Environments
A teacher provides digital teaching-learning content that can be used in an Internet-based environment for education and qualification. The learner has the option of using the content via various channels and end devices, including desktop PCs, laptops, smartphones, tablet PCs, wearables, and IPTV.
A multi-cloud strategy involves utilizing two or more cloud computing services, and a multi-cloud deployment typically refers to a combination of public infrastructure as a service (IaaS) environments, such as Amazon Web Services and Microsoft Azure. However, it can also encompass multiple software as a service (SaaS) or platform as a service (PaaS) cloud offerings.
MDX, which stands for Multidimensional Expressions, is a database language designed for analytical problem-solving. It serves as an extension to the SQL language, introduced by Microsoft to enable querying and scripting access to multidimensional data. While MDX is known for its flexibility, it can be considered relatively complex. Nevertheless, its extensive flexibility allows for the implementation of all essential solution scenarios in traditional data warehousing.
Multifunctional participation platforms
Citizens and companies have the opportunity to actively participate in planning and decision-making processes at various federal levels with the help of multifunctional participation platforms. These platforms enable cross-thematic and cross-administrative involvement.
Multimedia database management systems
Specialized databases that are designed to process multimedia content are called multimedia databases. The retrieval of media objects based on their characteristics, also known as features, plays a significant role. This search process for multimedia content is called multimedia retrieval.
Multimodal Learning Environments
It is a multidimensional learning environment, which is available both online and offline. This learning environment offers digital educational content aimed at supporting qualification, teaching and learning.
Multiple Input - Multiple Output
By using multiple antennas on both the transmitting side and the receiving side, "Multiple Input - Multiple Output" antenna technology can achieve higher data throughput.