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Query rewriting is a typically automatic transformation that takes a set of database tables, views, and/or queries, usually indices, often gathered data and query statistics, and other metadata, and yields a set of different queries, which produce the same results but execute with better performance (for example, faster, or with lower memory use). [1]
Cross-sectional data differs from time series data, in which the same small-scale or aggregate entity is observed at various points in time. Another type of data, panel data (or longitudinal data), combines both cross-sectional and time series data aspects and looks at how the subjects (firms, individuals, etc.) change over a time series. Panel ...
MarkLogic introduced bitemporal data support in version 8.0. Time stamps for Valid and System time are stored in JSON or XML documents. [2]XTDB [3] (formerly Crux) is an open source database that indexes documents using an EAV data model and provides point-in-time bitemporal SQL & Datalog queries.
In many cases, the repositories of time-series data will utilize compression algorithms to manage the data efficiently. [ 3 ] [ 4 ] Although it is possible to store time-series data in many different database types, the design of these systems with time as a key index is distinctly different from relational databases which reduce discrete ...
ISO/IEC 9075 "Information technology - Database languages - SQL" is an international standard for Structured Query Language, and is considered as specifying the minimum for what a database engine should fulfill in terms of SQL syntax, which is called Core SQL. The standard also defines a number of optional features.
Time period definitions use two standard table columns as the start and end of a named time period, with closed set-open set semantics. This provides compatibility with existing data models, application code, and tools
Presto (including PrestoDB, and PrestoSQL which was re-branded to Trino) is a distributed query engine for big data using the SQL query language. Its architecture allows users to query data sources such as Hadoop, Cassandra, Kafka, AWS S3, Alluxio, MySQL, MongoDB and Teradata, [1] and allows use of multiple data sources within a query.
The Cypher's data type system includes many of the common data types used in other programming and query languages. Supported data types include scalar value types such as boolean, string, number, integer, and floating-point numbers. It also supports temporal types like datetime, localdatetime, date, time, localtime