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Correlating Events to Find a Trend (Sequence)

This page explains how you can use sequences to detect trends in events that arrive in a specific order. There are two types of sequences:

Count and Match Multiple Events for a Single Trend​

Counting and matching multiple events over a given period is done with sequences when you need to identify trends in events that occur in a specific order. To understand how this is done, consider a scenario where the temperature is read from a sensor and you need to identify the peaks in temperature. If an event (i.e., a single reading) is a peak, then it should report a temperature greater than that reported by the event that occurred immediately before it as well as the event that occurred immediately after it.

Single Trend Stream Worker​

@App:name("TemperaturePeaksApp")
@App:qlVersion("2")

-- Input stream to capture the temperature readings.
CREATE STREAM TempStream(deviceID long, roomNo int, temp double);

-- Output stream to report the peaks once they are identified.
CREATE SINK PeakTempStream WITH (type='stream', stream.list='TemperaturePeak]:') (initialTemp double, peakTemp double);

-- Query to identify the peaks.
INSERT INTO PeakTempStream
SELECT e1.temp AS initialTemp, e2[last].temp AS peakTemp
FROM every e1=TempStream, e2=TempStream[e1.temp <= temp]+, e3=TempStream[e2[last].temp > temp];

Single Trend Query Explanation​

  • every indicates that all the events in the TempStream must be checked for the given conditions.
  • e2 is the reference for the event identified as the peak temperature. The e2=TempStream[e1.temp <= temp]+ condition specifies that to be identified as an event reporting a peak temperature, an event should have one or more preceding events that reports a lower or an equal temperature.
  • The e3=TempStream[e2[last].temp > temp] condition specifies a condition for e3 which is the event that follows e2. It indicates that e2, the peak temperature event should be the last event before e3, and that the temperature reported by e2 must be greater than the temperature reported by e3.
  • The temperature reported by e2 event is selected to be output as peakTemp because it is greater than the temperatures reported by events occurring before and after e2. The temperature reported by the event immediately before e2 is selected as initialTemp.

Logical sequences are used to identify logical relationships between events that occur in a specific order. To understand this, consider a scenario where an application is able to notify the state only when the event that notifies that the regulator is switched on is immediately followed by two other events to report the temperature and humidity.

Multi-Trend Stream Worker​

@App:name("RoomStateApp")
@App:qlVersion("2")

-- Input streams to capture information about the state of the regulator, the temperature, and humidity.
CREATE STREAM RegulatorStream (deviceID long, isOn bool);
CREATE STREAM TempStream (deviceID long, temp double);
CREATE STREAM HumidStream (deviceID long, humid double);

-- Output stream to publish the temperature and humidity.
CREATE SINK StateNotificationStream WITH (type='stream', stream.list='RoomState]:') (temp double, humid double);

-- Query to apply the logical sequence to derive the output.
INSERT INTO StateNotificationStream
SELECT e2.temp, e3.humid
FROM every e1=RegulatorStream, e2=TempStream AND e3=HumidStream;

Multi-Trend Query Explanation​

  • The unique references e1, e2, and e3 are assigned to the first, second, and third events respectively. e1 must arrive at the RegulatorStream stream, e2 must arrive at the TempStream stream, and e3 must arrive at the HumidStream stream in that order. The output event is generated only after all three of these input events have arrived.
  • To generate the output event, the value for the temp attribute must be taken from the e2 (second) event, and the value for the humid attribute must be taken from the e3 (third) event.