Enterprise Data Warehouse for Network Capacity Planning

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Solution Overview

Problem

Current systems for managing and planning telecommunications network capacity are inefficient, relying on manual data collection and analysis, leading to inaccurate forecasting and suboptimal deployment of network resources, especially in dynamically changing environments.

Innovation Solution

A method and system for implementing standardized enterprise warehouse processes that extract and standardize data from source systems, transforming it into a logical data model for loading into a physical data model, enabling automated capacity planning and resource provisioning based on real-time trunk group usage data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If manual data collection and analysis methods are used for network capacity planning, then system complexity is reduced, but measurement precision and productivity deteriorate

Engineering Contradiction:

Improvesystem complexityVSAvoidforecasting accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent introduces an automated data collection and analysis system that acts as an intermediary between network elements and capacity planning processes. This automated intermediary collects usage data from trunks and trunk groups, processes it through standardized enterprise warehouse processes, and generates accurate capacity forecasts, thereby achieving high measurement precision without requiring complex manual intervention.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual mechanical data collection and analysis processes with automated electronic systems. The automated system collects usage data from network elements, transforms it through standardized processes, and generates capacity plans electronically, eliminating the need for manual data gathering while significantly improving forecasting accuracy and productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated data collection and analysis systems are implemented, then measurement precision and productivity improve, but device complexity increases

Engineering Contradiction:

Improvecapacity planning efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements standardized enterprise warehouse processes that serve multiple functions: collecting usage data from various network elements, transforming and standardizing the data, storing it in a unified repository, and generating capacity forecasts. This multi-functional approach improves productivity while managing system complexity by using a universal process framework rather than separate specialized systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent transforms raw usage data into standardized parameters through automated processes. By changing the state of data from unstructured network element outputs to standardized warehouse parameters, the system achieves high productivity in capacity planning while managing complexity through parameter standardization and automated transformation rules.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If manual capacity planning methods are used, then ease of operation is maintained, but loss of time increases

Engineering Contradiction:

Improveoperational simplicityVSAvoiddata collection and analysis time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The patent implements automated data collection and preprocessing actions that occur continuously in the background before capacity planning is needed. Usage data is collected, standardized, and stored in the enterprise warehouse in advance, so when capacity planning is required, the analysis can proceed quickly using pre-prepared data, thereby reducing time loss while maintaining operational simplicity through automated workflows.

Inventive Principle:

Principle #10Preliminary action

4Measurement precision

If standardized enterprise warehouse processes are implemented for data transformation, then measurement precision and productivity improve, but device complexity increases

Engineering Contradiction:

Improvedata accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the data transformation process into distinct standardized stages: data collection from network elements, data cleaning and validation, data transformation into warehouse format, and data loading into the enterprise warehouse. This segmentation improves data accuracy through systematic processing at each stage while managing complexity by breaking down the overall process into manageable, standardized components.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS7747571B2Methods, systems, and computer program products for implementing logical and physical data models

Publication Date: 2010.06.29 BELLSOUTH INTELLECTUAL PROPERTY CORPORATION(US)

AI summary

Exemplary embodiments include a method for implementing standardized enterprise warehouse system processes, including: extracting content from one or more source systems that provide a feed for the content; loading extracted content into one or more standardized data layout tables defined by the data control structure and based upon the meta-data and rules, wherein the extracted content in condition for transformation and data warehouse loading and the standardized data layout tables comprise a logical data model; and propagating the extracted content into a physical data model.