Active Databases

An active Database is a database consisting of a set of triggers. These databases are very difficult to be maintained because of the complexity that arises in understanding the effect of these triggers. In such database, DBMS initially verifies whether the particular trigger specified in the statement that modifies the database is activated or not, prior to executing the statement. If the trigger is active then DBMS executes the condition part and then executes the action part only if the specified condition is evaluated to true. It is possible to activate more than one trigger within a single statement. In such situation, DBMS processes each of the trigger randomly. The execution of an action part of a trigger may either activate other triggers or the same trigger that Initialized this action. Such types of trigger that activates itself is called as ‘recursive trigger’. The DBMS executes such chains of trigger in some pre-defined manner but it effects the concept of understanding.

Features of Active Database:

1. It possess all the concepts of a conventional database i.e. data modelling facilities, query language etc.
2. It supports all the functions of a traditional database like data definition, data manipulation, storage management etc.
3. It supports definition and management of ECA rules.
4. It detects event occurrence.
5. It must be able to evaluate conditions and to execute actions.
6. It means that it has to implement rule execution.

Examples of Active Databases:

1. Real-time Databases
2. In-Memory Databases
3. Transactional Databases
4. Time-series Databases

1.Real-time Databases:

• Oracle TimesTen: A relational database that runs in memory and is intended for real-time applications that need response times of less than one millisecond.
• VoltDB: A lightning-fast in-memory database for instantaneous analytics and data processing.

2.In-Memory Databases:

• SAP HANA: A column-oriented, in-memory relational database management system for processing large amounts of data and real-time analytics.
• MemSQL: Uses in-memory processing for real-time data insights, combining analytics and transactions on a single platform.

3.Transactional Databases:

• MySQL Cluster: Offers automatic sharding and synchronous replication for high availability and real-time data access.
• Microsoft SQL Server with Always On: High availability and disaster recovery are provided by Microsoft SQL Server with Always On, which enables real-time read access to replicated databases.

4.Time-series Databases:

• InfluxDB: For time-stamped data, InfluxDB is designed to withstand heavy write and query loads. It is frequently utilized in IoT and monitoring applications.
• Prometheus: A toolkit for alerting and monitoring that keeps track of time series data and is used to analyze and monitor systems in real time.

These databases and platforms support a variety of real-time data handling requirements, including high-throughput stream processing, low-latency transaction processing, and event-driven architectures.

Advantages :

1. Enhances traditional database functionalities with powerful rule processing capabilities.
2. Enable a uniform and centralized description of the business rules relevant to the information system.
3. Avoids redundancy of checking and repair operations.
4. Suitable platform for building large and efficient knowledge base and expert systems.