Glossary

Use this glossary for terms that have a specific meaning in Kumo Relational and the NVIDIA Kumo Relational Client. Follow the links for procedures, examples, and complete reference information.

A

Anchor timestamp. The reference time associated with a prediction or context example. Kumo Relational uses the time boundary to determine which historical events are available to the prediction. See Configuration.

C

Context example. A labeled example supplied to Kumo Relational at prediction time. Kumo Relational can derive context examples from a PQL query or accept them directly through predict_task().

Custom context. Context and prediction entities supplied as pandas DataFrames through predict_task(), instead of context examples derived from a PQL query. See Predict with Custom Context.

D

Data type. The physical representation of a column, such as integer, floating point, string, Boolean, timestamp, or list. See Data Types and Semantic Types.

E

Entity table. A table representing objects for which Kumo Relational can make predictions. An entity table typically has a stable, unique primary key.

End-time column. Table metadata identifying when a row stops being valid. For the prediction entity table, it defines the exclusive end of the row’s validity interval. See Time and End-Time Columns.

Event table. A table containing time-dependent activities or transactions related to one or more entity tables. Its time column identifies when each event became available to the prediction workflow.

Explanation. The object returned when explain=True. It contains the prediction and structured attribution, and can include cohort analysis, subgraphs, and a natural-language summary. See Prediction Explainability.

F

Fan-out. The maximum number of neighbors sampled at one graph hop. Each value in num_neighbors sets the fan-out for the corresponding hop. See Neighborhood sampling.

Foreign key. A column whose values refer to the primary key of another graph table. Kumo Relational relationships connect foreign keys to their destination entity tables.

H

Hop. One relationship traversal in the relational neighborhood around a prediction entity. The number of entries in num_neighbors determines the number of sampled hops.

I

Indices. Entity primary-key values passed through the indices argument to predict(). They select the entities to score and override entity IDs embedded in the PQL query. See Make Predictions.

Inference configuration. Task-specific model settings passed through inference_config, including ensemble and output settings. See Inference configuration.

N

Neighborhood. Relational context sampled around a prediction entity. Its depth and maximum fan-out are controlled by num_neighbors.

NVIDIA NIM. A deployed inference service that hosts Kumo Relational and exposes its endpoint. The SDK connects to the NIM and sends prediction requests; it does not deploy or run the model itself. See Deploy, Install, and Connect.

P

Prediction entity. The entity for which Kumo Relational returns a prediction. In PQL, the entity is identified by the table key in the FOR or FOR EACH clause.

Prediction horizon. The future interval covered by a temporal prediction target. The horizon is expressed by the start, end, and time unit in a PQL aggregation. See Query Kumo Relational.

Predictive Query Language (PQL). The language used to describe a predictive task over a relational graph. A runnable Kumo Relational query identifies a target and the entity or entities to score. See Write Predictive Queries.

RelationalClient. The public SDK client that owns the HTTP connection to one NIM endpoint. Bind a graph with client.relational(graph) before making Kumo Relational predictions. See Python SDK API.

Primary key. A non-null, unique column that identifies each row in a graph table. Relationships from other tables can refer to this key.

R

Relational graph. The tables, table metadata, and relationships Kumo Relational uses to sample predictive context. See Create a Graph.

Relationship. A graph connection from a foreign key in an event or junction table to the primary key of an entity table. See Define Relationships.

Run mode. A prediction preset that controls the maximum number of context examples and the default neighborhood fan-out. See Run modes and defaults.

S

Semantic type. Metadata describing how Kumo Relational should interpret a column, such as ID, numerical, categorical, text, timestamp, or sequence. See Data Types and Semantic Types.

T

Target. The value or event to predict. In PQL, the target can be a column, a future aggregation, or a condition applied to an aggregation.

Task type. The prediction category, such as classification, regression, forecasting, ranking, or link prediction. See Prediction Types.

Time column. Table metadata identifying when an event became available to the prediction workflow. A datetime column does not automatically become the table’s time column. See Time and End-Time Columns.