(Researach) Define Domains. Events. Subdomains

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Tasks

• Define Domain Events. What triggers them?
• Define workflows that are triggered by these events.
• Define Commands. Associate commands with events.
• Partition the domain into subdomains and define "Bounded Contexts"

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Domain Events

Processes-related

• Process created
• Process changed
• Process started
• Process canceled
• Process finished

Monitoring

• Watcher Enabled
• Watcher Disabled
• Watcher Changed

Commands

Processes

• Create/Define process
• Edit process
• Start/Run process
• Cancel process

Monitoring

• Create/Define watcher
• Edit watcher
• Activate/deactivate watcher
• Delete watcher

Identity-related:

• Create user
• Edit user
• Delete user

[andrii-itdev]

Domain Events

Data Sets

• Data Set created
• Data Set changed
• Data Set deleted

Records

• Record created
• Record changed
• Record deleted

Commands

Data Sets

• Create "Data Set"
• Delete "Data Set"

Records

• Create "Record"
• Update "Record"
• Delete "Record"

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Bounded Contexts

• Data Management (Data Sets, Record)
• Processes (Data transferring, Data manipulation processes)
• Monitoring (Watchers)
• Identity (Users and permissions)

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Workflows

Common Workflows For the following domains:

• Data Manipulation
• Monitoring and Alerting
• Identity Management

A user creates a new DataSet (e.g., FontTheorySet). The user adds a record (e.g. "Aesthetics of Letters"). The user adds a record (e.g. "BeyondTheSerif"). The user creates a new DataSet (e.g., "AdvancedDesignPrinciplesSet"). The user establishes a relationship between related DataSets (e.g., FontTheorySet and AdvancedDesignPrinciplesSet). The user shares the FontTheorySet with another user. Another user who has access updates "Aesthetics of Letters" record. "Owner" user receives a notification. Another user who has access updates "BeyondTheSerif" record. "Owner" user receives a notification.

[andrii-itdev]

Desired features for corresponding domains

Identity Management (IDM), a.k.a. Identity and Access Management (IAM)

• Authentication Multi-Factor Authentication (MFA): Supporting multiple authentication factors (passwords, tokens, biometrics) to enhance security. Single Sign-On (SSO): Allowing users to log in once and access multiple applications without re-authenticating.

• Authorization (Identity Federation) Role-Based Access Control (RBAC): Assigning permissions based on predefined roles and responsibilities. Attribute-Based Access Control (ABAC): Granting access based on specific attributes of the user, environment, or resource. Supporting industry standards for integration, such as Security Assertion Markup Language (SAML) or OAuth. Identity Federation. Trust is established between the IdP and SP, allowing the SP to rely on the authentication and assertions provided by the IdP (Research is planned) #14

• User Lifecycle Management User Provisioning: Automated creation, and modification of user accounts across systems and applications. User Deprovisioning: Ensuring proper removal or deactivation of user accounts when no longer needed.

• Password Management: Enforcing password policies (length, complexity, expiration) to enhance security. Password reset and recovery mechanisms to assist users in case of forgotten passwords.

• Directory Services: Integration with directories such as Lightweight Directory Access Protocol (LDAP) or Microsoft Active Directory.

• User Profile/Configuration Management Providing users with self-service capabilities for tasks such as password resets, profile updates, and access requests.

• Policy Enforcement Enforcing security policies related to user authentication, access control, and data protection. Adhering to organizational policies and industry best practices.

Data Ingestion features

• Manual Input/Modification/Deletion.
• Web Scraping. Users can extract data from websites by parsing HTML.

Low-priority:

• File-Based Ingestion
• API-Based Ingestion
• Direct Database Connections
• Message Queues and Publish-Subscribe Systems

Types

• Batch Processing
• Real-Time (Streaming) Processing
• Complex Event Processing (CEP)

Data Storage, Management, and Transformation (Core Domain)

Data Storage

• Data Versioning: Support for versioning to track changes over time. Ability to roll back to previous versions and audit trail for data modifications.

• Scalability: Ability to handle large volumes of master data efficiently.

Data Management

• Real-time Processing Support real-time data manipulation, such as manual changes and modifications to Data Sets. Records purging (deletion) from data sets.

• Data Mapping and Integration: Mapping capabilities to define relationships between different data records ("datums").

• Data Standardization: Standardization of data formats, units, and values. Correct (Automatic) conversion of the data according to the data types.

Data Tranformation

• ETL Processes Extract, Transform, Load (ETL) processes for moving and transforming data between systems.

Data Monitoring and Alerting

• Monitoring data quality, system performance, and processing workflows.
• Setting up alerts to notify users of changes, issues, or anomalies in the Data Sets or Processes.

Data Enrichment

• Ability to enhance master data with additional information from external sources.
• Integration with external data providers for supplementary data.

Data Cataloging and Discovery

• Building and maintaining Data Sets that Organize.
• Maintaining data indexes of the collected data.
• Enabling users to discover and access relevant data resources.

Data Collaboration and Sharing

• Facilitating collaboration by enabling teams to share and collaborate on datasets.
• Implementing access controls and permissions for secure data sharing.

Data Analysis Consist of the following subdomains:

• Data Exploration
• Descriptive Statistics Calculating and summarizing basic statistical measures (mean, median, mode, standard deviation, etc.).
• Hypothesis Testing Formulating and testing hypotheses to make inferences about a population based on a sample.
• Correlation and Regression Analysis Examining relationships between variables, including the strength and direction of correlations. Identify dependencies and understand how changes in one variable may be associated with changes in another.
• Predictive Modeling Building statistical or machine learning models to make predictions about future outcomes.
• Cluster Analysis Grouping similar data points based on certain characteristics. Identify natural groupings or patterns within the data, aiding segmentation and classification.
• Root Cause Analysis Investigating the underlying factors contributing to a specific outcome or problem. Uncover the reasons behind observed patterns or issues, facilitating informed decision-making.
• Time Series Analysis Analyzing data points collected over time to identify trends, seasonality, and patterns. Understand temporal patterns and make predictions based on historical data.
• Statistical Significance Assessing the reliability of observed patterns and differences in the data. Determine if results are statistically significant and not likely to have occurred by random chance.

Data Integration

• Combining data from different sources to provide a unified view.
• Ensuring data consistency and coherence across various systems.

Data Governance

• Establishing policies, standards, and processes to ensure the proper management and use of data.

**Data Visualization and Reporting:

• Creating visual representations of data to facilitate understanding and decision-making.
• Developing dashboards and reports for communicating insights to stakeholders.

Data Migration

• Transferring data from one system to another, often during system upgrades or replacements.
• Ensuring that data retains its integrity and accuracy during the migration process.

Data Archiving and Purging

• Archiving historical data for compliance, reporting, or analytical purposes.
• Implementing data purging strategies to remove outdated or irrelevant data.

Data Security and Privacy

• Implementing measures to protect sensitive data from unauthorized access.
• Ensuring compliance with data privacy regulations and policies

Machine Learning and Predictive Analytics