For maintaining historical records of patient demographics, including changes in address and contact information, Type 2 Slowly Changing Dimensions (SCD) is most suitable. It allows the creation of new records for each change, preserving the history of patient details over time without modifying the original record.

The correct term is Normalization. It is the process of organizing data to reduce redundancy and dependency, resulting in an efficient and well-structured database schema. Normalization ensures that data is stored logically and that modifications or updates to the database are less prone to errors.

K-means clustering is a commonly used technique for clustering data in a database. It is an unsupervised learning algorithm that partitions data into k clusters based on their attributes' similarity. K-means clustering is widely used for data analysis and segmentation tasks.

The primary goal of compression techniques in data storage is to reduce the amount of storage space required. Compression algorithms achieve this by encoding data in a more efficient way, eliminating redundancy, and representing information in a compact form. This is crucial for optimizing storage resources and improving overall system performance.

Indexing is often used in conjunction with denormalization to improve query performance. Indexes help speed up data retrieval by creating efficient access paths to the denormalized data, balancing the trade-off between read performance and redundancy.

A table being in Boyce-Codd Normal Form (BCNF) means that it satisfies a stricter condition regarding functional dependencies compared to Third Normal Form (3NF). BCNF is designed to eliminate certain types of anomalies and ensure better data integrity.

All factors mentioned (geographic location, frequency of user activity, and size of user data) should be considered when deciding the partitioning strategy. Geographic location helps in optimizing access times, frequency guides the granularity of partitions, and size influences storage requirements.

Denormalization introduces intentional redundancy into the database schema. This means duplicating data in order to improve query performance, especially for read-heavy operations. It's a trade-off for optimizing retrieval speed at the cost of some additional storage.

In NoSQL databases, eventual consistency ensures that if no new updates are made to a data item, all reads will eventually return the last updated value. This model prioritizes availability and partition tolerance over immediate consistency across all nodes.

Data Warehouses are designed for analytical querying and analysis. They are optimized for complex queries that involve aggregations, summaries, and historical comparisons, providing valuable insights into trends and patterns within the data.

In database management, "storage optimization" refers to minimizing redundancy and improving efficiency in how data is stored. This involves techniques such as normalization, compression, and indexing to ensure that data is stored in the most efficient manner possible, reducing storage space and improving performance.

Denormalization in this scenario involves introducing redundancy by merging the tables, reducing the need for JOIN operations. This can improve query performance for generating sales reports by minimizing the complexity of queries.