Glossary

A collaborative partnership between Initiative D21, the Federal Ministry for Economic Affairs and Technology, and BITKOM, which has been regularly bringing together key representatives of the telecommunications industry since 2002 as a discussion platform.

This feature guarantees the presence of a duplicate instance of a Pod across a group of nodes within a cluster. It is often utilized for deploying system-level processes like log aggregators and monitoring tools that are required to operate on each Node.

The IDS-G specification includes the service of dynamically provisioning attributes, which is referred to as the Dynamic Attribute Provisioning Service. Glossary: Dynamic Attribute Provisioning Service.

Dark data refers to the digital information that organizations collect and store but do not utilize for their business operations.

The Supply Chain Operational Reference Model (SCOR) encompasses a set of interconnected processes governed by a range of planning activities. This model takes into account the comprehensive logistics chain, spanning from suppliers' suppliers to customers' customers, and encompasses procurement, production, delivery, and return processes.

[HMN15], S. 253 Hansen, H. R.; Mendling, J.; Neumann, G.: Wirtschaftsinformatik. De Gruyter, Berlin / München / Boston, 2015.

A dashboard is a consolidated display of diverse visual data, designed to present different yet interconnected information in a user-friendly format.

Any acts, facts or information that are digitally represented, as well as any collection of such acts, facts or information.

Typically, new services cannot rely on data that is inaccessible due to the absence of interfaces. Consequently, it becomes necessary to construct interfaces in order to retrieve this data. Gaining access to data stands as a major obstacle and fundamental requirement for harnessing data analytics.

excom presentation 2015-06-30 DDBM

A software development kit is available for the development of additional source/destination adapters in case the existing ones are not compatible.

Data Intelligence Hub

Data aggregation refers to the gathering and summarization of data. This process involves replacing individual data rows, often collected from multiple sources, with aggregated totals or summary statistics.

Data Analysis involves using analytical and logical reasoning to evaluate each component of the provided data. It is a critical step in research experiments, where data from different sources is collected, reviewed, and analyzed to arrive at a conclusion or finding. There are various methods used in data analysis, such as data mining, text analytics, business intelligence, and data visualization.

A person who is in charge of modeling, preparing, and cleaning data in order to extract useful insights from it.

Allows the data to be stripped of any Personally Identifiable Information (PII) before being sent to the Consumer, ensuring the compliance with the data protection regulations.

Data Intelligence Hub

A software package that is self-contained and self-descriptive, distributed through the App Store and deployed within a Connector, granting access to data and data processing capabilities. The interface of this package, also known as a Data App, is semantically described by the IDS Vocabulary.

The Data as a Service (DaaS) model is used to provide and distribute information to customers. It involves making various types of data, including text, image, sound, and video files, available over a network, usually the Internet.

The data made available for trading through Data Endpoints conform to a standardized Data Service interface. These Data Assets are anticipated to possess uniformity, coherence, and continuity over time in terms of level of detail, scope, context, organization, and conceptual categorization.

DWH (Data Warehouse) augmentation

Allows Data Providers/Consumers to manually request data stored by the Data Intelligence Hub to be backed up. Please note that this includes all metadata and data stored on the storage space allocated to the customer. Data stored on the sources that are simple linked to Data Intelligence Hub is not backed up.

Data Intelligence Hub

Automated backups on all metadata and data stored by the Data Intelligence Hub, as part of the Disaster Recovery concept. More details on the frequency and retention policies can be found in the Service Description.

Data Intelligence Hub

Data brokers, also known as information brokers or information resellers, are companies that gather personal information about individuals and then sell that information to other organizations. These brokers can gather data about consumers from a range of public and non-public sources, such as courthouse records, website cookies, and loyalty card programs. They often compile individual profiles for marketing purposes, which they sell to businesses looking to target their advertisements and promotions.

A Catalog is a manifestation of the Federation Service Federated Catalog, which displays a roster of available Service Offerings. Catalogs are fundamental components for making Participants or Service Offerings Self-Descriptions visible and discoverable.

Catena-X Automotive Network e.V.

Connectors are technologies designed to make data accessible from different systems. These systems can include traditional databases or applications as well as middleware technologies. Connectors abstract the specific characteristics of the source system and provide data and functionality through standardized interfaces (such as SQL, web services, XML, JMS messaging middleware) in a unified manner.

A participant who receives data in the form of a Data Product and utilizes it for data processing such as querying, analyzing, reporting, or other related tasks is known as a data consumer.

Allows Data Consumer to initiate contract negotiation with the Data Provider, exchanging necessary metadata to allow Data Provider to accept / reject the negotiation request based on defined policies. Successful negotiation is a requirement for initiating a data transfer from Provider to Consumer.

Data Intelligence Hub

The principle of treating data as a product aims to solve the long-standing issue of poor data quality and fragmented data storage. It involves considering the analytical data generated by different domains as a product, with the consumers of this data treated as satisfied and contented customers.

A database is a centrally managed data storage facility that is made accessible through application-independent access mechanisms.

S. 295 Hansen, H. R.; Neumann, G.: Wirtschaftsinformatik. Lucius & Lucius, Stuttgart, 2009.

Database activity monitoring (DAM) involves the tracking of actions taken within a database. This is achieved through the use of specialized tools that capture, record and monitor database events in near real-time. These tools are designed to identify and alert users about any policy violations that may occur.

Database as a Service (DBaaS) is a method of storing and managing structured data using cloud computing. Similar to traditional relational database management systems (RDBMS) such as SQL Server, MySQL, and Oracle, DBaaS offers database functionality. However, as a cloud-based solution, DBaaS is highly flexible, scalable, and on-demand, with a focus on self-service and simplified management, especially in terms of provisioning a business's own environment. Most DBaaS solutions include monitoring capabilities to track performance and usage, and to notify users of potential issues. Additionally, DBaaS products can provide data analytics to some degree.

A database management system (DBMS) oversees a centralized data storage, facilitating concurrent access by multiple application programs and users to the managed data. Additionally, the DBMS fulfills data administration responsibilities, encompassing activities like defining data types and attributes, assigning access privileges, and performing other administrative tasks.

A collection of data that is centrally managed and made accessible through application-independent access mechanisms is called a database.

S. 295 Hansen, H. R.; Neumann, G.: Wirtschaftsinformatik. Lucius & Lucius, Stuttgart, 2009.