Data Processing and Analytics

Bindings

Bindings, as the name implies, allow elements to be connected to one another, and update in response to changes in other connected elements. Bindings establish dependencies between various platform elements, which enables the execution of functions, firing events, or making variable changes based on certain conditions or events.

For example, by binding a variable in a chart to a device variable, you can create a report which updates whenever device data changes.

Historical Data

Any changes in Iotellect can be recorded and stored, such as variable values over time, or data from events and alerts. Raw historical data, meaning the exact variable values at any point in time, can be accessed, as well as aggregated to create statistical summaries, from simple averages to complex functions incorporating historical values from multiple variables.

Validity

Use resource validity to manage the relationships between various contexts. Although different contexts use resource validity in different ways, the fundamental idea is setting rules for a context to determine where it will be utilized. For example, creating a relative dashboard which is valid for certain devices will allow that dashboard to be opened in the context menu of those devices. Similarly, resource validity can be used with relative models to “attach” variables, functions, and events to other contexts.

Groups

Groups provide a way to combine devices and other system resources in order to manage large sets of resources and perform batch operations. With groups, it is possible to apply similar settings to all members, replicate configuration changes between them, and execute an operation over all objects in a group.

Events

Events in Iotellect are actions or occurrences within a context, such as a device or user interaction, or simply a change within a context. Anything from a variable being updated, to receiving a notification from a hardware device, can trigger an associated event. Each event in Iotellect has a structure called a Data Table associated with it, which holds data specific to that event. For example, the login event may contain a record with a username field that contains the name of the newly logged-in user. Events can be used to trigger bindings, or simply stored for later reference, report creation or statistical aggregation.

Event Filters

Use filters to help monitoring Iotellect events and prioritizing important ones by hiding non-significant events. Filters can be customized based on various criteria such as event source, type, level, parameters, and expressions, allowing users to focus on relevant information.

Event Correlators

Correlators listen to input streams, correlate events, and generate events as output. Detect complex patterns in events based on defined rules. Event correlators in Iotellect can be configured to publish events from either the correlator context itself or a different context, and extensions can be installed to enhance their functionality.

Alerts

Alerts are custom notifications that can be set up based on specific conditions or events to inform users that some part of the system needs human interaction. When an alert is triggered, users can be notified through many different means, like pop-up messages, emails, or SMS.

Models

Models can be used to create a ‘digital twin’ in Iotellect of any device, process, or object which isn’t already included in the platform. Models can hold custom variables, functions, and events. Relative models allow additional data, functions, or events to be added to existing contexts, and instantiable models allow for similar model instances to be created based off a model definition.

Scheduled Jobs

Automated actions that can be executed based on a user-defined time schedule. Perform tasks like device status checks, purging memory, or sending reports. The scheduler supports simple and advanced schedules, allowing for fixed intervals or complex execution patterns. Users have their own set of scheduled jobs, which can be administered and tracked using the Scheduled Jobs context.

Queries

Queries can be used for tasks like viewing/editing properties of multiple resources/devices, finding/filtering data and triggering alerts, building reports, exporting data, and sorting/filtering tabular data. The Iotellect Query Language is similar to SQL, but also allows references to Iotellect contexts.

Machine Learning

The machine learning context enables the creation and management of machine learning models. Models can be created based on data in Iotellect and a number of included algorithms. Data from trained models, such as inferences, are available in other contexts.

Scripts

For use cases that go beyond the capabilities of the Iotellect expression language or query language, the Scripts context allows code written in Java, R, or Python to be utilized by Iotellect. Scripts can access and manipulate Iotellect contexts and data, enabling them to trigger alerts, reports, and events.

Classes

Classes store similar objects in relational or graph databases. Standard relations between objects such as one-to-one, one-to-many, and many-to-many can be created. Classes can be utilized through dashboards to render and modify fields, display instance lists, and have configure states and lifecycles.

Workflows

Workflows are pieces of business logic that allow for complex interactions between the user and the system. Workflows are created in an intuitive graphical interface, but allow the full utilization of server functions and data.

Timezones

Iotellect is a distributed system that can comprise elements in multiple timezones. Correctly configuring timezones will allow all devices and contexts to create the correct timestamps during their operation.