Glossary

M2M-Ecosystem

The M2M ecosystem considers the stakeholders and their interactions within the M2M value chain, such as M2M module producers, communication service providers like mobile network operators, software vendors, M2M module integrators, data center operators, as well as providers of M2M products and services.

Machine learning

Machine learning is a branch of artificial intelligence that focuses on enhancing statistical models by utilizing past data. This improvement process, known as training, is facilitated by a learning algorithm. Once the model has been trained, it can be utilized to address the same kind of tasks that were covered by the training data. As a result, machine learning offers a substitute for explicit coding of a function.

Machine-to-Machine

This pertains to the exchange of information among machines, such as between control units that manage business or operational processes, and sensors or actuators.

Managed C#

Managed C# is a programming language supported by Microsoft that builds on the C# syntax and is used to develop applications under the .NET Framework platform.

Managed Service

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.

Managed Service Provider

An MSP, or managed service provider, offers various network, system, application, and e-management services to numerous enterprises through a network, using a pricing model where customers pay only for what they use. A "pure play" MSP concentrates primarily on providing management services. The MSP industry also encompasses services from other providers, such as ASPs (application service providers), web hosting companies, and NSPs (network service providers), who supplement their standard services with management services.

Management plane

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.

Manufacturing as a Service

Manufacturing as a Service (MaaS) refers to the use of networked manufacturing infrastructures for producing goods on-demand. The customer initiates the production of goods by the manufacturing companies through MaaS, and the order assignment is fully automated. This allows the customer to avoid spending significant amounts of time searching for suitable suppliers with the right skills and capacities. Instead, they can use an efficient algorithm that selects the best possible supplier based on certain factors. MaaS also benefits suppliers by allowing them to focus on their core manufacturing business while handing over the administrative work of customer acquisition and order processing to MaaS. For more information, please refer to the team landing page on Manufacturing as a Service (MaaS).

Manufacturing Bill of Materials

The manufacturing bill of materials is a product of the planning process, which includes auxiliary materials. It is a comprehensive list of all required parts and their respective quantities needed for the production of a product or assembly. However, due to site-specific manufacturing variations, the list may differ slightly within the eBOM specifications for each site.

Manufacturing Execution System

A multi-layer manufacturing management system includes different layers, one of which operates close to the process.

Mapping - Logic

Mapping, also known as matching, involves aligning the semantics and attributes of capabilities with skills. This is done by comparing the requirements of the product or customer (i.e., capabilities) with the capabilities of the machine or service provider (i.e., skills). Mapping can be done at different levels of detail, and the result is a list of modules that either fit or do not fit. This list can then be further filtered and prioritized based on additional criteria, such as which modules fit best, are most cost-efficient, or offer the best quality.

Massively Parallel Processing Database

Massively parallel processing (MPP) refers to the simultaneous execution of a program by numerous processors that operate on different portions of the program, each using its own memory and operating system.

Master

The term "Master" was used in the past as a synonym for nodes that hosted the Kubernetes control plane components. Although it is a legacy term, some provisioning tools such as kubeadm and managed services still use it to label nodes with kubernetes.io/role to control the placement of control plane pods.

Megabit per Second

The transmission speed is specified in MBit/s, which stands for millions of bits per second.

Member

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.

Message Queuing Telemetry Transprt

The MQTT protocol is a straightforward method for exchanging messages between devices and components, such as smart sensors, that are limited to restricted functions. It is based on the publish-subscribe method and was developed specifically for unreliable, low-bandwidth, high-latency networks such as those found in low-power wide area networks (LPWANs). Due to its simplicity and robustness, MQTT is often used in these networks.

Metadata Management

The Metadata Repository is a result of the metadata management process, which encompasses the end-to-end procedures involved in creating, improving, and maintaining a metadata repository along with its associated processes. Metadata management involves establishing processes, organizational structures, mindsets, and capabilities to develop a metadata environment. Similar to Business Intelligence and Master Data Management, metadata management faces a significant challenge concerning business process discipline and cultural aspects.

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.

Mirror Pod

A static Pod is a pod object that is created and managed by the kubelet on a specific node, rather than by the Kubernetes API server. When a static pod is found in its configuration directory, the kubelet automatically creates a Pod object for it on the API server. This allows the pod to be visible on the API server but cannot be controlled from there. This means that removing a static pod from the API server will not stop the kubelet from running it. Static pods are often used to run system components that must be present on every node in the cluster, such as kubelet, kube-proxy, and storage drivers.

MLOps

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.

Mobile Learning

The use of mobile devices such as smartphones and tablets for the use of digital educational content has a special feature: by integrating the sensor-based data that these devices can collect, constructivist learning theories can be supported in the educational situation.

Mobile virtual network operator

Today, and presumably also in the future, mobile network operators are focusing on generic services for the mass market. For UMTS network operators in particular, it therefore makes sense to cooperate with MVNOs that focus on special communities and selected market niches.

Mobilitätsintegrator

Mobility integrators create customized travel routes and travel times for their customers and target groups by performing intermodal mobility planning. This involves incorporating all modes of transport into transportation and travel planning, which requires intelligent linking of information from different modes of transport, such as timetables and route information.

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.

Mongo DB

MongoDB, which derives its name from the word "humongous," is a document-oriented open-source database system and is one of the most widely used NoSQL databases.

Multi-cloud environement

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.

Multidimensionales Online Analytical Processing

Multidimensional online analysis procedure (MOLAP) is a variant of the online analysis procedure (OLAP), which is based on a multidimensional database. OLAP applications generally always treat data multidimensionally. It allows the user to view different views or facets of data aggregations such as sales by time, region, and product model.

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.