Similarity Model

The Similarity model in Maximo IT is an AI-driven service that analyses incoming incidents and identifies historical records with closely related content.

Once trained and integrated, the model quietly works behind the scenes—suggesting matches and boosting productivity at the point of need.

Defined configuration

The following configuration for Object structures, query templates, invocation channels, etc. are already configured for Maximo IT. Users can modify the query clause as per their requirement before training the MCC model.

Note: The values for various fields shown below are just for refrence. Users can define their own values and choose attributes according to their requirements.

Object Structures:

  1. Go to the Object Structures application. (Open menu > Integration > Object Structures)
  2. Open the MXAPIINCIDENTDET object. Type MXAPIINCIDENTDET in the Object Structure field and click on the record.
    Note: This is the default Object structure already created in Maximo IT.
  3. Scroll down to Source Objects for MXAPIINCIDENTDET table and the following objects with corresponding values are added:
    Table 1.
    Object Parent Object Object location path Relationship Object order User definition
    Incident   Incident   1 On
    Persongroup Incident Incident/Persongroup Ownergroup 1 On
    Longdescription Incident Incident/Longdescription Description/Longdescription 2 On
  4. Go to More Actions > Query Template.
  5. In the Query Templates for MXAPIINCIDENTDET, following two templates have been added,
    Table 2.
    Template Name Page Size User for
    AITRAININCIDENTSIMILAR 1000 Training
    AIINFERENCEINCIDENTSIMILAR 10 Inferencing
  6. In the INCIDENTTRAINING template, scroll down to Query Template Attributes for INCIDENTTRAINING. Following attributes have been added:
    1. Description
    2. Description_longdescription
    3. Reportdate
    4. Ticketuid
    Note: Description and Description_longdescription are feature channel and Persongroup/Ownergroup are inferenced/predicted by AI for the incident.
  7. In the AIINFERENCEINCIDENTSIMILAR template, scroll down to Query Template Attributes for AIINFERENCEINCIDENTSIMILAR. Following three attributes are added:
    1. Description
    2. Description_longdescription
    3. Ticketuid
  8. Now, go to More Actions > Query Definition.
  9. In the Queries to be assignedtable, two training filter records have been added - one for incident training and another for inference. You can modify the Query clause according to your requirement:
    Table 3.
    Query Clause Name Query Type Query Clause Is Public?
    INCIDENTTRAINSIMOBJ osclause Status=’NEW’ and ownergroup=’NETWORK’ Yes
    INCIDENTINFSIMOBJ osclause Status in (‘NEW’) and reportdate >= (CURRENT TIMESTAMP – 4 MINUTES) Yes
  10. Save the Object structure by clicking on the Save icon after modifying the Query clause.

Invocation Channels:

  1. Go to the Invocation Channels application. (Open menu > Integration > Invocation Channels).
  2. Two channels are created – one for training and another for inference.
  3. The first invocation channel is for AI Training for incident (AITRAININCIDSIMILAROBJ).
  4. To map the object structure and Query templates created in previous steps, following values are added:
    • Request object structure: MXAPIINCIDENTDET
    • Request template: AITRAININCIDENTSIMILAR
    • Request Processing Class: com.ibm.tivoli.maximo.ai.AITrainReqExit
    • Response Processing Class: com.ibm.tivoli.maximo.ai.AITrainRespExit
  5. Another invocation channel is for AI inference for incident (AIINFERENCEINCDSIMILAROBJ).
  6. To map the object structure and Query templates created in previous steps, following values are added:
    • Request object structure: MXAPIINCIDENTDET
    • Request template: AIINFERENCEINCIDENTSIMILAR
    • Request Processing Class: com.ibm.tivoli.maximo.ai.AIInferenceReqExit
    • Response Processing Class: com.ibm.tivoli.maximo.ai.AIInferenceRespExit