Event Analytics Client for Java¶
Introduction¶
The Event Analytics Java client allows you to analyze event data. It identifies significant dependencies and helps to get a better understanding of the system's internal processes using statistical analysis.
Further implementation of the EventAnalytics SDK library has been shown in a sample project that you can download and test in local or on Insights Hub application. Please refer to this repository: industrial-iot-java-sdk-examples
Hint
Placeholders in the following samples are indicated by angular brackets < >
.
Event Operations¶
The event operations client statistically analyzes event data to identify the most frequent events.
Client name: EventOperationsClient
Find Top Events¶
This section shows two options for finding the N most frequently occurring events and returning them sorted by the number of occurrences in descending order.
// Construct the EventOperationsClient object
EventOperationsClient event_operations_client = EventOperationsClient.builder()
.mindsphereCredentials(<credentials>)
.restClientConfig(<config>)
.build();
// Create the EventsInputModelEventsMetadata object
EventsInputModelEventsMetadata events_metadata = new EventsInputModelEventsMetadata();
events_metadata.setEventTextPropertyName("text");
// Create a list of events to be analyzed
Event first_event = new Event();
first_event.setText("INTRODUCING FUEL");
first_event.setTextQc(0);
first_event.setTime("2017-10-01T12:00:00.001Z");
Event second_event = new Event();
second_event.setText("Status@Flame On");
second_event.setTextQc(0);
second_event.setTime("2017-10-01T12:02:01.001Z");
List<Event> events_list = new ArrayList<>();
events_list.add(first_event);
events_list.add(second_event);
// Create the TopEventsInputDataModel object that will be analyzed
TopEventsInputDataModel input_data_model = new TopEventsInputDataModel();
input_data_model.setNumberOfTopPositionsRequired(5);
input_data_model.setEventsMetadata(events_metadata);
input_data_model.setEvents(events_list);
TopEventOutput top_events_response = null;
try {
top_events_response = event_operations_client.topEvents(input_data_model);
} catch (MindsphereException e) {
// Exception handling
}
Alternatively, use the TopEventsRequest
model.
// Create the EventOperationsClient object as shown above
TopEventsRequest request_object = new TopEventsRequest();
request_object.setData(<input_data>);
TopEventOutput top_events_response = null;
try {
top_events_response = event_operations_client.topEvents(request_object);
} catch (MindsphereException e) {
// Exception handling
}
Filter Events¶
This section shows two options for simplifying the dataset by applying user defined filters.
// Create the EventOperationsClient object as shown above
// Create the EventsInputModelEventsMetadata object
EventsInputModelEventsMetadata events_metadata = new EventsInputModelEventsMetadata();
events_metadata.setEventTextPropertyName("text");
// Create a list of events to be analyzed
Event first_event = new Event();
first_event.setText("INTRODUCING FUEL");
first_event.setTextQc(0);
first_event.setTime("2017-10-01T12:00:00.001Z");
Event second_event = new Event();
second_event.setText("Status@Flame On");
second_event.setTextQc(0);
second_event.setTime("2017-10-01T12:02:01.001Z");
List<Event> events_list = new ArrayList<>();
events_list.add(first_event);
events_list.add(second_event);
// Create a list of filter criteria
List<String> filter_list = new ArrayList<>();
filter_list.add("Introduction fuel");
// Create the EventSearchInputDataModel object that will be analyzed
EventSearchInputDataModel input_data_model = new EventSearchInputDataModel();
input_data_model.setEvents(events_list);
input_data_model.setEventsMetadata(events_metadata);
input_data_model.setFilterList(filter_list);
EventArrayOutput filter_events_response = null;
try {
filter_events_response = event_operations_client.filterEvents(input_data_model);
} catch (MindsphereException e) {
// Exception handling
}
Alternatively, use the FilterEventsRequest
model.
// Create the EventSearchInputDataModel object as shown above
FilterEventsRequest request_object = new FilterEventsRequest();
request_object.setData(input_data_model);
EventArrayOutput filter_events_response = null;
try {
filter_events_response = event_operations_client.filterEvents(request_object);
} catch (MindsphereException e) {
// Exception handling
}
Count Events¶
This section shows two options for determining the number of events per time interval for a user defined interval length.
// Create the EventOperationsClient object as shown above
// Create the EventInputEventsMetadata object
EventInputEventsMetadata events_metadata = new EventInputEventsMetadata();
events_metadata.setEventTextPropertyName("text");
events_metadata.setSplitInterval(500);
// Create a list of events to be analyzed
Event first_event = new Event();
first_event.setText("INTRODUCING FUEL");
first_event.setTextQc(0);
first_event.setTime("2017-10-01T12:00:00.001Z");
Event second_event = new Event();
second_event.setText("Status@Flame On");
second_event.setTextQc(0);
second_event.setTime("2017-10-01T12:02:01.001Z");
List<Event> events_list = new ArrayList<>();
events_list.add(first_event);
events_list.add(second_event);
// Create the EventInput object that will be analyzed
EventInput event_input = new EventInput();
event_input.setEvents(events_list);
event_input.setEventsMetadata(events_metadata);
EventCountOutput count_events_response = null;
try {
count_events_response = event_operations_client.countEvents(event_input);
} catch (MindsphereException e) {
// Exception handling
}
Alternatively, use the CountEventsRequest
model.
// Create the EventInput object as shown above
CountEventsRequest request_object = new CountEventsRequest();
request_object.setData(event_input);
EventCountOutput count_events_response = null;
try {
count_events_response = event_operations_client.countEvents(request_object);
} catch (MindsphereException e) {
// Exception handling
}
Remove Duplicate Events¶
This section shows two options for removing duplicate events. The methods detect duplicate events within a sliding window of user defined width (example 5,000 ms) and reduce the data set by aggregating duplicate events.
// Create the EventOperationsClient object as shown above
// Create the EventInputEventsMetadata object
EventInputEventsMetadata events_metadata = new EventInputEventsMetadata();
events_metadata.setEventTextPropertyName("text");
events_metadata.setSplitInterval(500);
// Create a list of events to be analyzed
Event first_event = new Event();
first_event.setText("INTRODUCING FUEL");
first_event.setTextQc(0);
first_event.setTime("2017-10-01T12:00:00.001Z");
Event second_event = new Event();
second_event.setText("Status@Flame On");
second_event.setTextQc(0);
second_event.setTime("2017-10-01T12:02:01.001Z");
List<Event> eventsList = new ArrayList<>();
eventsList.add(first_event);
eventsList.add(second_event);
// Create a list of filter criteria
List<String> filter_list = new ArrayList<>();
filter_list.add("Introduction fuel");
// Create the EventInput object that will be analyzed
EventInput event_input = new EventInput();
event_input.setEvents(eventsList);
event_input.setEventsMetadata(events_metadata);
DuplicateEventArrayOutput remove_duplicate_response = null;
try {
remove_duplicate_response = event_operations_client.removeDuplicateEvents(event_input);
} catch (MindsphereException e) {
// Exception handling
}
Alternatively, use the RemoveDuplicateEventsRequest
model.
// Create the EventInput object as shown above
RemoveDuplicateEventsRequest request_object = new RemoveDuplicateEventsRequest();
request_object.setData(event_input);
DuplicateEventArrayOutput remove_duplicate_response = null;
try {
remove_duplicate_response = event_operations_client.removeDuplicateEvents(request_object);
} catch (MindsphereException e) {
// Exception handling
}
Pattern Operations¶
The Pattern Operations client statistically analyzes the event data to identify the most frequent events.
Client name: PatternOperationsClient
Find Event Patterns¶
This section shows two options for searching for user defined patterns in a list of events. The response lists all events which match the specified pattern(s).
// Construct the PatternOperationsClient object
PatternOperationsClient pattern_operations_client = PatternOperationsClient.builder()
.mindsphereCredentials(credentials)
.restClientConfig(config)
.build();
// Construct the EventsInputModelEventsMetadata object
EventsInputModelEventsMetadata events_metadata = new EventsInputModelEventsMetadata();
events_metadata.setEventTextPropertyName("text");
// Create a list of events to be analyzed
Event first_event = new Event();
first_event.setText("INTRODUCING FUEL");
first_event.setTextQc(0);
first_event.setTime("2017-10-01T12:00:00.001Z");
Event second_event = new Event();
second_event.setText("Status@Flame On");
second_event.setTextQc(0);
second_event.setTime("2017-10-01T12:02:01.001Z");
List<Event> events_list = new ArrayList<>();
events_list.add(first_event);
events_list.add(second_event);
// Construct the EventsInputModel object
EventsInputModel events_input = new EventsInputModel();
events_input.setEvents(events);
events_input.setEventsMetadata(events_metadata);
// Construct MatchingPattern object
MatchingPattern matching_pattern_1 = new MatchingPattern();
matching_pattern_1.setEventText("INTRODUCING FUEL");
matching_pattern_1.setMaxRepetitions(2);
matching_pattern_1.setMinRepetitions(1);
MatchingPattern matching_pattern_2 = new MatchingPattern();
matching_pattern_2.setEventText("Status@Flame On");
matching_pattern_2.setMaxRepetitions(1);
matching_pattern_2.setMinRepetitions(0);
// Create a list of matching patterns
List<MatchingPattern> pattern = new ArrayList<>();
pattern.add(matching_pattern_1);
pattern.add(matching_pattern_2);
// Construct PatternDefinition object
PatternDefinition pattern_definition = new PatternDefinition();
pattern_definition.setPattern(pattern);
// Create a list of pattern definitions
List<PatternDefinition> pattern_definition_list = new ArrayList<>();
pattern_definition_list.add(pattern_definition);
// Create a list of non event strings
List<String> non_events = new ArrayList<>();
non_events.add("Error 2.. occurred");
non_events.add("STOPPING ENGINE");
// Construct the PatternMatchingInputDataModel object that will be analyzed
PatternMatchingInputDataModel input_data_model = new PatternMatchingInputDataModel();
input_data_model.setEventsInput(events_input);
input_data_model.setNonEvents(non_events);
input_data_model.setPatternsList(pattern_definition_list);
input_data_model.setMaxPatternInterval(200000);
PatternMatchingOutput match_pattern_response = null;
try {
match_pattern_response = pattern_operations_client.matchPatternsOverEvents(input_data_model);
} catch (MindsphereException e) {
// Exception handling
}
Alternatively, use the MatchPatternsOverEventsRequest
model.
// Construct the PatternMatchingInputDataModel object as shown above
MatchPatternsOverEventsRequest request_object = new MatchPatternsOverEventsRequest();
request_object.setData(input_data_model);
PatternMatchingOutput match_pattern_response = null;
try {
match_pattern_response = pattern_operations_client.matchPatternsOverEvents(request_object);
} catch (MindsphereException e) {
// Exception handling
}
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