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Overview
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AI-generated from documented ETRM metadata — verify critical details on the linked pages.

Overview

BIV_CLO_SUM_MV is a materialized-view-backed summary table owned by the APPS schema in Oracle E-Business Suite, catalogued under the FND — Application Object Library product. It is a denormalized aggregation object that stores pre-computed service-request closure metrics, primarily counts and time-to-close figures, grouped across a fixed set of business dimensions such as time, incident type, severity, product, customer, owner group, creation channel, and resolution code. Rather than forcing concurrent reporting queries to scan transactional service-request and incident data, EBS refresh processes populate this object so that downstream dashboards and analytics can retrieve closure trends efficiently.

The metadata classifies BIV_CLO_SUM_MV heuristically as a standalone Data Vault structure. As a modeling observation rather than a definitive rule, this suggests treating it as neither a pure hub, link, nor satellite, but a consolidated reporting artifact assembled from multiple source entities. In practice it behaves like a pre-aggregated fact summary whose grain is defined by the concatenation of its dimensional foreign keys.

Key Information Stored

The table exposes 41 documented columns. The operational grain is enforced through the unique index I_SNAP$_BIV_CLO_SUM_MV, which applies SYS_OP_MAP_NONNULL to a composite of dimensional identifiers including the multiple time keys (MV_ENT_YEAR_ID, MV_ENT_QTR_ID, MV_PERIOD_ID, MV_WEEK_ID, MV_DAY_ID), INCIDENT_TYPE_ID, INCIDENT_SEVERITY_ID, VBH_CATEGORY_ID, PRODUCT_ID, CUSTOMER_ID, OWNER_GROUP_ID, SR_CREATION_CHANNEL, RESOLUTION_CODE, and MV_GROUPING_ID. These columns therefore function as the composite business-key candidates rather than a single surrogate key.

The most significant measures are CLOSED_COUNT, which tallies closures for the grouping, and TOTAL_TIME_TO_CLOSE, the aggregate elapsed closure duration. The bucketed distributions TIME_TO_CLOSE_B1 through TIME_TO_CLOSE_B10 provide histogram-style banding of closure durations. Their mirrored MV_ equivalents (MV_TOTAL_TIME_TO_CLOSE, MV_TIME_TO_CLOSE_B1 through B10, and MV_COUNT) hold parallel or materialized-view-maintained versions of the same metrics. Note that TIME_ID, PERIOD_TYPE_ID, GRP_ID, and MV_GROUPING_ID carry the grouping context, while the MV_ day/week/period/quarter/year identifiers drive time-basis slicing.

Common Use Cases and Queries

The primary use case is service-request closure analytics: average and total time-to-close, closure volume by severity or product, and trend lines across time periods. A representative aggregation query groups by a time dimension and measure columns.

  • SELECT mv_period_id, incident_severity_id, SUM(closed_count), SUM(total_time_to_close) FROM biv_clo_sum_mv GROUP BY mv_period_id, incident_severity_id;
  • Filter by PRODUCT_ID or CUSTOMER_ID to isolate closure performance for a portfolio or account.
  • Use TIME_TO_CLOSE_B1 through B10 to build distribution reports showing how many closures fell into each duration band.
  • Compare MV_TOTAL_TIME_TO_CLOSE against TOTAL_TIME_TO_CLOSE to reconcile the materialized view against the base aggregation.
  • Join to a date dimension via MV_DAY_ID or MV_PERIOD_ID for calendar-aware reporting.

Because the object is a summary MV, ad-hoc queries should avoid duplicate COUNT operations and instead rely on the pre-summed MV_COUNT and CLOSED_COUNT values.

Related Objects

The documented foreign keys link BIV_CLO_SUM_MV to several reference tables, enabling dimensional enrichment.

These join columns correspond directly to the unique index components, so any enrichment join must preserve the composite grouping grain. Consumers should treat the three referenced tables as the authoritative lookup sources and validate that MV refresh schedules for BIV_CLO_SUM_MV remain aligned with changes in the underlying service-request closure data.