harmonize product naming across the documentation

CONTRIBUTING.md

@@ -114,7 +114,7 @@ On this page is search function for icons available as well.
 |Success|Success admonitions are a type of documentation element used to highlight successful outcomes or positive results associated with a particular task, process, or feature|Graph is created successfully.|
 |Bug|A bug admonition include a description of the bug or issue, as well as steps that the user can take to avoid or work around the problem. It may also include information about when the bug will be fixed or patched, if applicable.|Users may experience issues with the file saving feature when running this software on Windows 10. To avoid data loss or corruption, be sure to save your work frequently and consider using an external backup device. Our development team is working to resolve this issue in the next software update.|
 |Example|The example admonition is typically used in instructional or educational documents to clarify complex concepts or demonstrate how to perform a specific task.|To create a new email account, click on the "Sign Up" button on the homepage and enter your name, email address, and desired password. Be sure to choose a strong password with a mix of uppercase and lowercase letters, numbers, and special characters. Once you have entered your information, click "Create Account" to complete the process.|
-|Task|The Task admonition type is used to describe a series of steps or tasks required to complete a process or action. Clear and concise descriptions of each step, along with any relevant details or instructions, should be included. It's helpful to use images or diagrams to illustrate the process. Overall, The Task admonition is useful for providing detailed instructions to readers.|Loading Data  into a CMEM involves 3 steps: </br> 1. Connect to the database. </br> 2. Choose the data file and appropriate file type. </br> 3. Run workflow to import.|
+|Task|The Task admonition type is used to describe a series of steps or tasks required to complete a process or action. Clear and concise descriptions of each step, along with any relevant details or instructions, should be included. It's helpful to use images or diagrams to illustrate the process. Overall, The Task admonition is useful for providing detailed instructions to readers.|Loading Data into eccenca Corporate Memory involves 3 steps: </br> 1. Connect to the database. </br> 2. Choose the data file and appropriate file type. </br> 3. Run workflow to import.|
 
 </details>
 

docs/automate/cmemc-command-line-interface/backup-and-restore/index.md

@@ -9,7 +9,7 @@ tags:
 
 ## Introduction
 
-cmemc can export and re-import all data which Corporate Memory manages: the knowledge graphs in the store, the projects in the workspace, and any subset of them.
+cmemc can export and re-import all data which eccenca Corporate Memory manages: the knowledge graphs in the store, the projects in the workspace, and any subset of them.
 This page describes which artifacts you can create, how to restore each of them, and - just as important - which parts of a deployment cmemc does **not** cover.
 
 !!! warning "A cmemc export is not a complete instance backup"

docs/automate/cmemc-command-line-interface/configuration/certificate-handling-and-ssl-verification/index.md

@@ -10,7 +10,7 @@ tags:
 
 ## Introduction
 
-In a reasonable production deployment, all client-accessible Corporate Memory APIs will be securely available as HTTPS endpoints.
+In a reasonable production deployment, all client-accessible eccenca Corporate Memory APIs will be securely available as HTTPS endpoints.
 This document clarifies how to deal with certificates.
 cmemc will validate the certificates of your HTTPS endpoints and indicate validation errors.
 If the certificates of your Corporate Memory deployment are based on a common and publicly available Certificate Authority (such as [Let's Encrypt](https://letsencrypt.org/)), cmemc is able to validate your certificates out of the box.

docs/automate/cmemc-command-line-interface/configuration/completion-setup/index.md

@@ -12,7 +12,7 @@ tags:
 In case you are using bash or zsh as your terminal shell, you should enable [Command-line tab completion](https://en.wikipedia.org/wiki/Command-line_completion) for cmemc.
 
 Tab completion is a powerful feature and will save you a lot of typing work.
-Furthermore, it will help you to learn the different commands, parameters and options and will auto-complete parameter values taken live from your Corporate Memory instance (such as graph IRIs, project IDs, etc.).
+Furthermore, it will help you to learn the different commands, parameters and options and will auto-complete parameter values taken live from your eccenca Corporate Memory instance (such as graph IRIs, project IDs, etc.).
 
 ![cmemc - Create Dataset](22.1-cmemc-create-dataset.gif "cmemc - Create Dataset")
 

docs/automate/cmemc-command-line-interface/configuration/environment-based-configuration/index.md

@@ -69,7 +69,7 @@ export CMEMC_CONFIG_FILE=cmemc.ini
 export CMEMC_CONNECTION=mycmem
 ```
 
-This alone allows us to save a lot of typing for a series of commands on the same Corporate Memory instance.
+This alone allows us to save a lot of typing for a series of commands on the same eccenca Corporate Memory instance.
 
 ``` shell-session
 $ cmemc graph list --raw

docs/automate/cmemc-command-line-interface/configuration/file-based-configuration/index.md

@@ -31,7 +31,7 @@ If you start cmemc without any command or option, it outputs a general introduct
     $ cmemc
     Usage: cmemc [OPTIONS] COMMAND [ARGS]...
 
-      Eccenca Corporate Memory Control (cmemc).
+      eccenca Corporate Memory Control (cmemc).
 
       cmemc is the eccenca Corporate Memory Command Line Interface (CLI).
 

docs/automate/cmemc-command-line-interface/configuration/getting-credentials-from-external-processes/index.md

@@ -54,7 +54,7 @@ export OAUTH_CLIENT_SECRET=$(get-my-secret.sh)
 
 ## External Processes
 
-Another option, which is interesting when working with multiple Corporate Memory instances, is the configuration of an external process in your [cmemc configuration file](../file-based-configuration/index.md).
+Another option, which is interesting when working with multiple eccenca Corporate Memory instances, is the configuration of an external process in your [cmemc configuration file](../file-based-configuration/index.md).
 
 In order to get credential information from an external process you need to use the following configuration variables to set up an external executable:
 

docs/automate/cmemc-command-line-interface/finding-and-filtering-objects/index.md

@@ -58,7 +58,7 @@ The following table gives an impression of how different these name sets are:
 !!! tip
 
     With [command-line completion](../configuration/completion-setup/index.md) enabled, you do not need to remember any of this.
-    Pressing ++tab++ after `--filter` offers the filter names of the current command, and pressing ++tab++ again often completes the values as well, taken live from your Corporate Memory instance.
+    Pressing ++tab++ after `--filter` offers the filter names of the current command, and pressing ++tab++ again often completes the values as well, taken live from your eccenca Corporate Memory instance.
 
 ## Combining filters
 

docs/automate/cmemc-command-line-interface/scripting-with-cmemc/index.md

@@ -49,7 +49,7 @@ Combining `--id-only` with `--filter` is the basic building block of most cmemc
 
 ## JSON output
 
-The `--raw` option is available on more than 40 commands and outputs the JSON as it was received from the Corporate Memory APIs.
+The `--raw` option is available on more than 40 commands and outputs the JSON as it was received from the eccenca Corporate Memory APIs.
 Together with a tool such as [jq](https://jqlang.github.io/jq/), this allows for arbitrary post-processing:
 
 ``` shell-session title="IRIs of all writeable graphs"

docs/automate/cmemc-command-line-interface/troubleshooting-and-caveats/index.md

@@ -11,7 +11,7 @@ This page lists and documents possible issues and warnings when working with cme
 
 ## Proxy is in the way
 
-If you feel that your system's proxy configuration negatively impacts the communication between cmemc and Corporate Memory, you can disable using any proxy by setting this variable:
+If you feel that your system's proxy configuration negatively impacts the communication between cmemc and eccenca Corporate Memory, you can disable using any proxy by setting this variable:
 
 ``` shell-session
 export no_proxy='*'

docs/automate/continuous-integration/index.md

@@ -7,7 +7,7 @@ subtitle: and Delivery
 ## Introduction
 
 Project teams often manage crucial parts of their work assets inside of git repositories.
-This includes Corporate Memory related files such as ontologies, shapes or project configurations.
+This includes eccenca Corporate Memory related files such as ontologies, shapes or project configurations.
 Given such a project setup, it is often wanted to start activities with these Corporate Memory files.
 Continuous integration (CI) is the practice of automating the integration of changes from multiple contributors into a single project.
 Originated from software projects, CI can (and should) be applied to Knowledge Graph projects as well.

docs/automate/processing-data-with-variable-input-workflows/index.md

@@ -10,7 +10,7 @@ tags:
 
 ## Introduction
 
-This tutorial shows how you can create and use data integration workflows to process data coming from outside Corporate Memory (i.e., without registering datasets).
+This tutorial shows how you can create and use data integration workflows to process data coming from outside eccenca Corporate Memory (i.e., without registering datasets).
 This is achieved with the **Allow replacement** flag on input and/or output datasets.
 This flag is supported by most file dataset types.
 A replaceable dataset is created and used inside a workflow as an input for other tasks (e.g., a transformation).

docs/automate/scheduling-workflows/index.md

@@ -9,7 +9,7 @@ tags:
 
 ## Introduction
 
-For a time-based execution of a workflow, Corporate Memory provides the Scheduler operator. Please note that, in case you want to schedule workflows externally, [cmemc can be used for that](../cmemc-command-line-interface/workflow-execution-and-orchestration/index.md).
+For a time-based execution of a workflow, eccenca Corporate Memory provides the Scheduler operator. Please note that, in case you want to schedule workflows externally, [cmemc can be used for that](../cmemc-command-line-interface/workflow-execution-and-orchestration/index.md).
 
 ## Create a scheduler
 

docs/build/active-learning/index.md

@@ -15,7 +15,7 @@ Active learning infuses expert knowledge and creates new relationships between p
 
 > Active learning is a special case of machine learning in which a learning algorithm interactively queries a user to label new data points with the desired outputs. [[wikipedia]](https://www.wikiwand.com/en/Active_learning_(machine_learning)#introduction)
 
-In Corporate Memory we apply this approach to the process of learning a linking rule by interactively label records from the configured source and target dataset.
+In eccenca Corporate Memory we apply this approach to the process of learning a linking rule by interactively label records from the configured source and target dataset.
 Labeling in this case means to indicate if the pair of resources (from source and target) should be connected with the configured property.
 The labeling process creates reference links against which the linking rule can be created and further refined as more input is given by the user.
 

docs/build/cool-iris/index.md

@@ -28,7 +28,7 @@ The following list of example IRIs demonstrate the broad scope of this concept:
 - `telnet://192.0.2.16:80/`
 - `urn:oasis:names:specification:docbook:dtd:xml:4.1.2`
 
-## Best practices in Corporate Memory
+## Best practices in eccenca Corporate Memory
 
 A good IRI is unique, stable, simple and manageable.
 

docs/build/evaluate-template/index.md

@@ -56,7 +56,7 @@ The tutorial consists of the following steps, which are described in detail belo
 
     The vocabulary contains the classes and properties needed to map the source data into entities in the Knowledge Graph.
 
-1. In Corporate Memory, click **Knowledge Graphs** in the navigation under **Explore** on the left side of the page.
+1. In eccenca Corporate Memory, click **Knowledge Graphs** in the navigation under **Explore** on the left side of the page.
 
     ![Menu entry EXPLORE > Knowledge Graphs](menu-explore-knowledge-graphs.png){ class="bordered" width="40%" }
 

docs/build/extracting-data-from-a-web-api/index.md

@@ -91,7 +91,7 @@ As we are only interested in the _HTTP Message Body_ which holds the JSON reposi
 
 ## 3 Create a JSON Dataset
 
-To create a JSON-to-RDF-mapping within Corporate Memory, we have to first register an example response from the API (repos.json). Based on the schema of the response, we can then define step-by-step the mappings, which are used to build the Knowledge Graph.
+To create a JSON-to-RDF-mapping within eccenca Corporate Memory, we have to first register an example response from the API (repos.json). Based on the schema of the response, we can then define step-by-step the mappings, which are used to build the Knowledge Graph.
 
 1. Click the **Create** button (top right) in the data integration workspace and select the type **JSON**.
 

docs/build/index.md

@@ -34,7 +34,7 @@ The Build stage turns your source data—across files, databases, APIs, and stre
     - [Extracting data from a Web API](extracting-data-from-a-web-api/index.md) --- Build a Knowledge Graph based on input data from a Web API.
     - [Incremental Database Loading](loading-jdbc-datasets-incrementally/index.md) --- Load data incrementally from a JDBC Dataset (relational database Table) into a Knowledge Graph.
     - [Active learning](active-learning/index.md) --- Advanced workflows that improve results iteratively by incorporating feedback signals.
-    - [Connect to Snowflake](snowflake-tutorial/index.md) --- Connect Snowflake as a scalable cloud warehouse and lift/link its data in Corporate Memory to unify it with your other sources in one knowledge graph.
+    - [Connect to Snowflake](snowflake-tutorial/index.md) --- Connect Snowflake as a scalable cloud warehouse and lift/link its data in eccenca Corporate Memory to unify it with your other sources in one knowledge graph.
     - [Build Knowledge Graphs from Kafka Topics](kafka-consumer/index.md) --- Consume Kafka topics and lift event streams into a Knowledge Graph.
     - [Evaluate Jinja Template and Send an Email Message](evaluate-template/index.md) --- Template and send an email after a workflow execution.
     - [Link Intrusion Detection Systems to Open-Source INTelligence](tutorial-how-to-link-ids-to-osint/index.md) --- Link IDS data to OSINT sources.

docs/build/introduction-to-the-user-interface/index.md

@@ -59,7 +59,7 @@ A dataset represents an abstraction over raw data. In order to work with data, y
 
 There is a range of different dataset types for different kinds of source data. Important dataset types include:
 
-- **Knowledge Graph** - Read RDF from or write RDF to a Knowledge Graph embedded in Corporate Memory.
+- **Knowledge Graph** - Read RDF from or write RDF to a Knowledge Graph embedded in eccenca Corporate Memory.
 - **CSV** - Read from or write to an CSV file.
 - **XML** - Read from or write to an XML file.
 - **JSON** - Read from or write to a JSON file.

docs/build/kafka-consumer/index.md

@@ -13,7 +13,7 @@ tags:
 [Apache Kafka](https://kafka.apache.org/) is a distributed event store and stream-processing platform.
 Kafka is widely used in enterprises for data pipelines, streaming analytics, data integration and other applications.
 
-By using the [cmem-plugin-kafka](https://pypi.org/project/cmem-plugin-kafka/) [Python Plugin](../../develop/python-plugins/index.md), you can consume messages from Apache Kafka and use them inside Corporate Memory Workflows.
+By using the [cmem-plugin-kafka](https://pypi.org/project/cmem-plugin-kafka/) [Python Plugin](../../develop/python-plugins/index.md), you can consume messages from Apache Kafka and use them inside eccenca Corporate Memory Workflows.
 
 ## Installation
 

docs/build/lift-data-from-tabular-data-such-as-csv-xslx-or-database-tables/index.md

@@ -120,7 +120,7 @@ The vocabulary contains the classes and properties needed to map the data into t
 
 === "JDBC"
 
-    Instead of uploading the [services.csv](services.csv) sample file into Corporate Memory, you can also load it into a SQL database and access it from Corporate Memory using the JDBC protocol.
+    Instead of uploading the [services.csv](services.csv) sample file into eccenca Corporate Memory, you can also load it into a SQL database and access it from Corporate Memory using the JDBC protocol.
 
     1. Click again on **Create new** :eccenca-item-add-artefact: in the top right corner. In the **Create new item** window, select **JDBC endpoint** type and click **Add**.
 

docs/build/loading-jdbc-datasets-incrementally/index.md

@@ -33,7 +33,7 @@ This tutorial walks you through the process of loading data incrementally from a
 
 ## 1 Create the JDBC dataset
 
-To extract data from a relational database, you need to first register a **JDBC endpoint** in Corporate Memory. This tutorial assumes that you have access to the relational database from the Corporate Memory instance.
+To extract data from a relational database, you need to first register a **JDBC endpoint** in eccenca Corporate Memory. This tutorial assumes that you have access to the relational database from the Corporate Memory instance.
 
 1. In Corporate Memory, click Projects under **BUILD** in the navigation on the left side of the page.
 
@@ -82,13 +82,13 @@ Now that we have the JDBC endpoint IRI, we will build the Metadata Graph to stor
 
 The following three RDF triples hold the (minimal) necessary information we need for this tutorial:
 
-1. The first triple imports the CMEM DI Project graph into our Metadata Graph to enable access to the LIMIT property defined earlier and to additional information we may need in the future.
+1. The first triple imports the **CMEM DI Project** graph into our Metadata Graph to enable access to the LIMIT property defined earlier and to additional information we may need in the future.
 2. The second triple defines a label for the Graph.
 3. The third triple defines the <...**lastOffset**> property we need for this tutorial. As a default, we set it to 0 to start with the first row in the table.
 
 For your project:
 
-1. adjust the CMEM DI Project IRI and
+1. adjust the **CMEM DI Project** IRI and
 2. the JDBC endpoint IRI.
 
 **Import the Graph** in the Exploration tab → Graph (menu) → Add new Graph → Provide Graph IRI + Select file.

docs/build/spark/index.md

@@ -4,11 +4,11 @@ tags:
     - Introduction
     - Explainer
 ---
-# Apache Spark within Corporate Memory Build
+# Apache Spark within eccenca Corporate Memory Build
 
 ## Introduction
 
-This documentation provides an overview of Apache Spark and its integration within Corporate Memory’s Build component.
+This documentation provides an overview of Apache Spark and its integration within eccenca Corporate Memory’s Build component.
 The goal is to provide a conceptual understanding of Spark, its purpose in Build, and how workflows leverage Spark-aware datasets for efficient, distributed data processing.
 
 The documentation is structured in two parts:

docs/build/tutorial-how-to-link-ids-to-osint/define-the-interfaces/index.md

@@ -38,7 +38,7 @@ Here, the figure 3 is nice but before this first schema during the project, ther
 ## Technical feasibility with the available information
 
 It's not really technical to check if the data is available or not, but without technical knowledge, it's hard to evaluate the difficulty to link each id to their instance in your RDF knowledge graph.
-In this tutorial, we learn to use Corporate Memory of Eccenca to transform these IDs to IRI to import properly these ID with the other data necessary to build these interface.
+In this tutorial, we learn to use eccenca Corporate Memory to transform these IDs to IRI to import properly these ID with the other data necessary to build these interface.
 
 After research and one meeting with analysts, we have chosen the datasets of Mitre Attack, the datasets of IoC rules (Sigma and Hayabusa) in Github and of course, the IoCs data already in the Splunk indexes.