Temporal Data Management involves techniques and practices for handling data related to time, including recording, querying, and managing data that changes over time. It is crucial for applications requiring historical data, time-based analysis, and tracking changes in data over periods.

• Valid Time: This represents the time period during which a fact is true in the real world. For example, a record of a person's employment might include the start and end dates of their employment, reflecting the valid time.
• Transaction Time: This denotes the time period during which a fact is stored in the database. It tracks when data was inserted, updated, or deleted in the database system.

• Bitemporal Model: This model integrates both valid time and transaction time, allowing databases to handle and query data with respect to both the real world and the database changes. It supports advanced querying capabilities for historical data analysis.
• Temporal Relational Model: An extension of the traditional relational model, this model incorporates time into the structure of relational databases. It adds time dimensions to tables, enabling effective storage and retrieval of temporal data.

• 2011: An extension of the SQL (Structured Query Language) standard that includes temporal features. It allows users to perform complex queries involving time-based conditions, such as retrieving historical data or tracking changes over time.
• Temporal Query Languages: Specialized query languages or extensions for querying temporal data. These languages provide constructs and functions designed specifically for handling time-based data queries.

• Financial Systems: Temporal data management is essential in financial systems for tracking transactions, auditing, and managing time-sensitive data such as stock prices and trading history.
• Healthcare: In healthcare, temporal data management is used to track patient records, treatment histories, and clinical trials over time, supporting better patient care and compliance.

• Data Volume: Managing large volumes of temporal data can be challenging, especially when dealing with high-frequency updates and extensive historical data. Efficient storage and retrieval mechanisms are required.
• Complexity: The complexity of temporal data management increases with the need to handle multiple time dimensions and ensure consistency across various time-based queries and operations.

• NoSQL Databases: Some modern NoSQL databases, such as MongoDB and Cassandra, offer support for temporal data management, providing flexible schemas and high performance for handling time-based data.
• Big Data Technologies: Advances in big data technologies, like Apache Hadoop and Apache Spark, are improving the capabilities for processing and analyzing large-scale temporal data efficiently.

Temporal Data Management is a critical field for effectively handling and analyzing time-based data. It encompasses various models, querying techniques, and applications, and continues to evolve with advancements in database technology and big data processing.

• https://en.wikipedia.org/wiki/Temporal_database
• https://www.researchgate.net/publication/220673167_Temporal_Data_Management
• https://www.ibm.com/docs/en/i/7.4?topic=ssw_temporal