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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 50. Chapters: Database, Relational database, Entity-attribute-value model, Temporal database, Database model, Conjunctive query, Database design, V-optimal histograms, Nested set model, Big data, View, Chase, Codd's 12 rules, Functional dependency, Triplestore, Least number bits, Range searching, Database storage structures, Rollback, In-database processing, Transitive dependency, Probabilistic database, Data item, Data event, Recursive join, Data cube, Principle of Orthogonal Design, Exclusive relationship, Persistent Object Identifier, Database catalog, Nonpositive data, Symposium on Principles of Database Systems, Attribute domain, Full table scan, Single Table Inheritance, Dependency theory, Bidirectionalization, Materialized path, Relational classification, Monotonic query. Excerpt: Entity-attribute-value model (EAV) is a data model to describe entities where the number of attributes (properties, parameters) that can be used to describe them is potentially vast, but the number that will actually apply to a given entity is relatively modest. In mathematics, this model is known as a sparse matrix. EAV is also known as object-attribute-value model, Vertical Database Model and open schema. This data representation is analogous to space-efficient methods of storing a sparse matrix, where only non-empty values are stored. In an EAV data model, each attribute-value pair is a fact describing an entity, and a row in an EAV table stores a single fact. EAV tables are often described as |long and skinny| |long| refers to the number of rows, |skinny| to the few columns. Data is recorded as three columns: One example of EAV modeling in production databases is seen with the clinical findings (past history, present complaints, physical examination, lab tests, special investigations, diagnoses) that can apply to a patient. Across all specia...