📄️ Introduction
Visual data (images, videos) is rich in business and expert insights. These insights are now made increasingly accessible via advancements in Data Science and Machine Learning(ML). Exploiting big-visual-data requires storage and access methods designed with visual ML in mind. With the current off-the-shelf alternatives, ML engineers and data scientists are forced to glue disparate data solutions to address visual data management, accumulating technical debt as their use case scales and landing themselves with a maintenance nightmare.
📄️ Schema
The ApertureDB API provides a way to add entities in a system relevant to an application, though it is important to remark that no schema declaration is needed upfront.
📄️ API Description
The Basics
📄️ Responses
In regular responses, the JSON response is an array of objects where each member gives information about how a particular command executed. Any time there is an error, the JSON response returned will not be an array but a JSON object with a member “status”. So a check to see whether status is a member of response should reveal whether there was an error or not.
📄️ System-defined Properties
ApertureDB defines a set of properties known as system-defined properties. These properties are either used in object definitions or computed internally by the system. To distinguish them from user-defined properties, all system-defined properties start with “\_” prefix. Some properties are present in all objects (e.g., \uniqueid), while others are specific to certain object types (e.g., \label for BoundingBox, Polygons, or Descriptors). System-defined properties cannot be updated by users, except in cases where the Update command explicitly supports modification through defined parameters. However, these properties can be used as keys in constraints.
📄️ Handling of Missing Data
Purpose
📄️ Access Control
ApertureDB supports role-based access control. Roles can be created and users have one or more roles assigned to it.
📄️ QuerySemantics
The runnable Python examples create small, uniquely named fixtures, assert the results below, and remove their records and property indexes. Run them against a disposable database with the aperturedb Python package installed. Supply credentials through ADBUSER and ADBPASSWORD. For a local server without TLS:
📄️ Query and response
This page documents what goes on when a command such as "AddImage", or "FindImage" is sent to the database.