Search Results cause_short_name

Overview

BIS_CAUSE_EFFECT_V is a view owned by the FND (Application Object Library) product schema in Oracle E-Business Suite. It exposes cause-and-effect relationships between Oracle Balanced Scorecard indicators, presenting a consolidated list of cause indicator and effect indicator short names together with sequencing information. The view is part of the BIS/BSC family of objects used by the Oracle Balanced Scorecard module, which itself is a component of the broader Enterprise Performance Management (EPM) stack embedded in EBS 12.1.1 and 12.2.2.

The name references the underlying relationship table BSC_KPI_CAUSE_EFFECT_RELS, which stores the configured linkages between KPI indicators. BIS_CAUSE_EFFECT_V presents a de-duplicated, aggregated projection of those relationships, combining both seeded (dataset-level) relationships and custom relationships defined by the user. It is intended primarily for reporting, integration, and lookup purposes rather than for direct transactional maintenance.

Per the ETRM metadata, the view is documented as "Not implemented in this database," meaning it is a metadata-only entry in the Applications Object Library and does not necessarily exist in every EBS instance. This is consistent with the fact that the Balanced Scorecard features are only active when the relevant products have been licensed and installed.

Underlying Base Objects

The view is defined over the following documented base objects:

• BIS_INDICATORS — the master indicator definition table, referenced twice in the view (once as CAUSEINFO and once within the EFFECTINFO subquery). Each row carries a DATASET_ID and a SHORT_NAME describing the indicator.
• BSC_KPI_CAUSE_EFFECT_RELS — the seeded relationship table mapping cause indicators to effect indicators, with CAUSE_INDICATOR, EFFECT_INDICATOR, CAUSE_LEVEL, and EFFECT_LEVEL columns.
• BIS_CUSTOM_CAUSE_EFFECT_RELS — the user-defined relationship table, containing CAUSE_SHORT_NAME, EFFECT_SHORT_NAME, CAUSE_SEQUENCE, and EFFECT_SEQUENCE columns used to express custom cause-effect chains with explicit ordering.

The view logic is built around a UNION and MINUS construct. The first block derives dataset-level relationships (where either CAUSE_LEVEL or EFFECT_LEVEL equals 'DATASET') and removes those that have been deliberately suppressed in BIS_CUSTOM_CAUSE_EFFECT_RELS with the sentinel sequence values -1/-1. The second block then adds all custom relationships whose CAUSE_SEQUENCE and EFFECT_SEQUENCE are not -1. The combined result set is grouped by CAUSE_SHORT_NAME and EFFECT_SHORT_NAME, using MAX on the sequence columns to produce one row per cause-effect pair.

Key Columns

• CAUSE_SHORT_NAME — the short (unique) identifier of the cause indicator, joined from BIS_INDICATORS or supplied directly by BIS_CUSTOM_CAUSE_EFFECT_RELS.
• EFFECT_SHORT_NAME — the short identifier of the effect indicator, resolved through the relationship tables.
• CAUSE_SEQUENCE — a numeric ordering value for the cause side. Seeded rows contribute 0; custom rows contribute the user-defined sequence or -1 when suppressed.
• EFFECT_SEQUENCE — the corresponding ordering value for the effect side, with the same semantics as CAUSE_SEQUENCE.

Together, these four columns allow downstream consumers to reconstruct the direction and priority of each cause-effect linkage and to distinguish seeded relationships (sequence 0) from user-defined ones (non-zero sequences).

Common Use Cases and Queries

Typical uses include validating which cause-effect paths exist before deploying scorecard strategy maps, generating reports of KPI dependencies, and reconciling seeded relationships against user overrides. A basic query is:

• SELECT cause_short_name, effect_short_name, cause_sequence, effect_sequence FROM bis_cause_effect_v WHERE cause_short_name = :cause;
• SELECT cause_short_name, effect_short_name FROM bis_cause_effect_v ORDER BY cause_short_name, effect_short_name;
• SELECT * FROM bis_cause_effect_v WHERE cause_sequence = 0 AND effect_sequence = 0; — returns only seeded dataset-level linkages.

Because the view deduplicates via MAX and GROUP BY, it is well suited for read-only reporting layers, BI Publisher data templates, and integration extracts that require a clean cause-effect listing without duplicate entries.