About Oracle IoT Cloud Service Stream Exploration

The stream exploration feature of Oracle IoT Cloud Service allows you to process and analyze data messages that are sent from your devices to Oracle IoT Cloud Service. This feature is now deprecated and it will be removed in future releases.

Note:

This feature is now deprecated and will be removed in a future release.

The stream exploration feature provides a business focused visual approach to real-time streaming event analytics. It is based on the Oracle Stream Analytics tool and exposes some of the features that the tool offers. Using the Oracle IoT Cloud Service Management Console, you create an exploration by using one of your previously defined exploration sources that you want to analyze further. You can also annotate the data message being sent with device-specific metadata. Doing so will give you additional identifying criteria to use during your data analysis. See Understanding Explorations in Using Oracle Stream Analytics and Add a Message Exploration to an Application for more information.

Once an exploration is published, it becomes available as one of the message formats to choose from when you’re configuring your IoT Application’s integration with external services, such as Oracle Business Integration (BI) Cloud Service, Oracle Mobile Cloud Service, JD Edwards EnterpriseOne IoT Orchestrator, Oracle Storage Cloud Service, or an enterprise application.

Some of the limitations of the explorations feature in Oracle IoT Cloud Service include the following:

There is no support for references.
Publishing explorations to targets outside of Oracle IoT Cloud Service is not supported.
There is no support for creating data streams from external sources.

The image below shows the workflow of the real-time analytics application.

Description of iotcs-rt-analytics-workflow.jpg follows

Description of the illustration iotcs-rt-analytics-workflow.jpg

Streams

Streams in the above diagram represent the streams of data being sent to the stream explorer tool. Using the Data and Explorations page within the scope of an IoT Application, you create exploration using previously defined exploration sources data or alert messages that you want to further explore. See Add a Message Exploration to an Application for more information.

Explorations

Oracle IoT Cloud Service Stream Explorer explorations are visual representation of real time disparate event data flows for insightful data interrogation and to quickly apply sophisticated business intelligence. They allow you to define business criteria for managing data. They are the visual representation of the data streamed from Oracle IoT Cloud Service devices. These explorations allow you to perform the following tasks:

Correlate data or alerts from multiple device data streams
Filter and group data.
Specify the time/event-based windows for aggregation functions.
Specify aggregation functions to be used in summaries.
Review incoming events (before you apply the logic) and resulting events (after you apply the logic) in tabular and graph forms

Patterns

A Stream Explorer pattern provides you with a simple way to explore event Streams based on common business scenarios. A pattern is a template of an Oracle Stream Analytics application that already has the business logic built into it. The visual representation of the stream varies from one pattern type to another based on the key fields in the stream you choose to use.

The following table lists some of the patterns available in Stream Explorer.

Pattern Name	Description
Top N	First N events in the specified time window
Bottom N	Last N events in the specified time window
Up Trend	Detects when the up trend starts and continues to grow. E.g., use this pattern to identify when the temperature value from a sensor device starts increasing continuously
Down Trend	Detects when a numeric event field shows a change in a specified trend, lower in value. E.g., to identify when the temperature value from a sensor device starts continuously decreasing
Fluctuation	detects when an event data field value changes in a specific upward or downward fashion within a specific time. E.g., to identify the variable changes in pressure values within acceptable ranges
Eliminate Duplicates	Eliminates duplicate events in a specific event stream
Detect Duplicates	Detects when an event data field has duplicate values within a specified period of time
Detect Missing Event	Detects when an expected event does not occur within a specified time window. e.g, use this pattern in circumstances when the next heartbeat event is missing
W pattern	Detects when an event data field value rises and falls in a W fashion over a specified time period
Inverse W	Detects the inverses of W
Spatial General	Analyzes streams containing geo-location data and determine how events relate to pre-defined geo-fences in your maps.