Search Results estimation_cutoff_date

Overview

CS_CTR_ESTIMATE_READINGS_V is an Oracle E-Business Suite Service (CS) module view owned by the APPS schema. It exposes counter estimate readings, the calculated values the ETRM (Enterprise Territory and Resource Management) counter engine derives when actual meter readings are unavailable within a billing or usage period. Counters in Oracle Service represent cumulative meters — such as copier page counts, equipment run hours, or instrument cycles — tracked against service contracts and installed base instances. When a customer fails to report a reading by the contractually required date, the counter estimation process projects a value using historical consumption. This view surfaces those projected readings in a report-friendly, denormalized form so that field service, contract billing, and usage-verification processes can consume them without navigating the underlying transactional tables directly.

The view is defined as a simple projection over a single base object. It carries no joins, aggregations, or filters in its view text, functioning as a column-and-row pass-through that presents the stored estimation records with the full set of descriptive flexfield attributes and audit columns required for reporting and integration.

Underlying Base Objects

The view is defined exclusively over CS_CTR_ESTIMATED_READINGS, accessed through the synonym of the same name in the APPS schema. Because the definition is a straight SELECT ... FROM CS_CTR_ESTIMATED_READINGS A, every row in the view corresponds one-to-one with a row in the base table. The ROWID column is explicitly selected, preserving the physical row identifier for the base table, which is useful for updatable-view behavior and for DBA-level diagnostics. The view therefore inherits the base table's indexing strategy, which typically keys on ESTIMATED_READING_ID, COUNTER_ID, and ESTIMATION_ID. The OBJECT_VERSION_NUMBER column supports Oracle's optimistic locking model in the underlying table, and it is exposed here unchanged.

Key Columns

• ESTIMATED_READING_ID — Primary identifier of the estimated reading record; the primary key of the base table.
• COUNTER_ID — Foreign key to the counter definition to which the estimated reading belongs.
• ESTIMATION_ID — Identifier of the estimation batch or process run that produced the record.
• NUM_OF_READINGS — The number of historical actual readings used to compute the estimate. This is the column most commonly referenced when users search for "num_of_readings"; it quantifies the sample size behind an estimated value and is central to evaluating estimate reliability.
• VALUE_TIMESTAMP — The effective date/time at which the estimated reading value applies.
• ESTIMATED_METER_READING — The projected absolute meter value.
• ESTIMATED_USAGE — The projected consumption over the estimation period.
• PERIOD_START_DATE / PERIOD_END_DATE — The interval covered by the estimate.
• AVG_CALCULATION_START_DATE — The starting point of the historical window used for averaging.
• ESTIMATION_CUTOFF_DATE — The date after which actual readings would supersede the estimate.
• DESCRIPTIVE FLEXFIELD COLUMNS (ATTRIBUTE1–15, ATTRIBUTE_CATEGORY) — User-defined context captured with the estimation record.
• AUDIT COLUMNS — CREATION_DATE, CREATED_BY, LAST_UPDATE_DATE, LAST_UPDATED_BY, LAST_UPDATE_LOGIN.

Common Use Cases and Queries

Typical usage includes auditing estimate quality, reconciling estimated versus actual readings before billing, and reporting on counters where actual data is chronically missing. A representative query listing estimates with their sample size is:

• SELECT counter_id, estimation_id, estimated_meter_reading, num_of_readings, period_start_date, period_end_date FROM cs_ctr_estimate_readings_v WHERE counter_id = :p_counter_id ORDER BY value_timestamp DESC;
• SELECT estimation_id, COUNT(*) FROM cs_ctr_estimate_readings_v WHERE estimation_cutoff_date >= :p_date GROUP BY estimation_id;
• SELECT counter_id, AVG(num_of_readings) FROM cs_ctr_estimate_readings_v GROUP BY counter_id HAVING AVG(num_of_readings) < 3;

These patterns support investigation of low-confidence estimates, integration extracts feeding external billing systems, and verification that the estimation process received sufficient historical data.