Data Warehouse Temporal Segmentation for Historical Analysis
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Solution Overview
Problem
The increasing size of enterprise data warehouses leads to significant IT and personnel costs due to the need for maintaining multiple physical snapshots for OLAP applications, which often become stale and are resource-intensive.
Innovation Solution
A data warehouse system is implemented with database tables containing START_TIME and END_TIME columns, allowing for the retrieval and analysis of data at any historical point in time through SQL queries, eliminating the need for physical snapshots and enabling efficient storage and personnel cost savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical snapshots of data warehouse data are maintained for OLAP applications, then data availability and independence from running state are improved, but storage resources and personnel costs increase significantly
Solution Approach 1:
The patent segments the time dimension by introducing START_TIME and END_TIME columns to divide the data into temporal segments. This allows the system to maintain historical data integrity without creating physical snapshots, as each record is segmented by its valid time range rather than creating separate physical copies.
Solution Approach 2:
The patent adds a temporal dimension to the data warehouse schema by incorporating START_TIME and END_TIME columns into every database table. This dimensional change enables historical data retrieval without requiring physical snapshots, as time becomes an explicit dimension that can be queried directly from the normalized data structure.
2Loss of information
If multiple physical snapshots are constructed for different time points, then historical data retrieval is enabled, but IT resources and personnel costs increase
Solution Approach 1:
Instead of creating physical copies (snapshots) of the data warehouse, the patent uses a single normalized data structure with time-validation columns. The system 'copies' historical state information through the START_TIME and END Time columns rather than duplicating entire data structures, significantly reducing IT resource requirements.
Solution Approach 2:
The patent changes the parameters of data storage by adding temporal validation columns (START_TIME, END_TIME) to existing tables rather than creating separate snapshot tables. This parameter change enables historical data retrieval through modified query conditions rather than requiring separate physical datasets.
3Adaptability or versatility
If physical snapshots are taken at varying time points by individual departments, then department-specific data requirements are met, but data redundancy and staleness increase
Solution Approach 1:
The patent creates a universal data structure with START_TIME and END_TIME columns that serves all departmental needs. This single normalized structure can support any department's historical data requirements without requiring department-specific snapshot systems, eliminating redundancy while maintaining adaptability.
Solution Approach 2:
The patent merges the functionality of multiple departmental snapshots into a single unified data warehouse structure. By combining historical data from different time points and departments into one normalized schema with time-validation columns, the system eliminates redundancy while preserving all required historical information.
Data Source
AI summary
Methods and apparatus, including computer program products, implementing and using techniques for analyzing historical data in a data warehouse. A data warehouse is provided. The data warehouse includes several database tables. Every database table has a start time column and an end time column. A query is issued to the data warehouse. The query includes customized query predicates against the start time column values and end time column values of a database table. A response is received from the data warehouse. The response includes table records satisfying the query and having valid (START_TIME, END_TIME) ranges that cover a fixed time point specified by the customized query predicates of the query. Methods for updating a database table in a data warehouse, and a data warehouse system are also described.