Smart Agent Data Normalization for Cost Analysis
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Solution Overview
Problem
Conventional cloud service operations lack an adequate mechanism for compiling and understanding real-time costs and efficiencies, relying on labor-intensive, error-prone manual methods that are often focused on single aspects, leading to discrepancies in dataset management.
Innovation Solution
An information processing system with a smart agent engine that ingests and normalizes datasets and workflows, coupled with an analytics engine using machine and deep learning algorithms to generate real-time metrics and enhanced datasets, and a decision support module for interactive cost and revenue forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual methods are used to manage and compile datasets, then flexibility and adaptability are maintained, but labor consumption increases and error rates rise
Solution Approach 1:
The system employs smart agents that automatically ingest, normalize, and manage datasets without human intervention. These agents self-service the data compilation process by collecting data from multiple sources, transforming it into standardized formats, and making it ready for analysis, thereby eliminating manual labor while maintaining data quality and accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Machine learning models and analytics engines substitute human analysts in performing data compilation, normalization, and analysis tasks, dramatically increasing productivity while reducing errors associated with manual work.
2Measurement precision
If single-aspect applications are used for data collection, then focus on specific metrics is improved, but comprehensiveness of dataset coverage deteriorates
Solution Approach 1:
The system employs a universal data collection framework that simultaneously captures multiple aspects of operational data including costs, revenues, efficiencies, and other metrics. The smart agents are designed to ingest diverse data types from various sources, ensuring comprehensive coverage while maintaining precision through specialized processing for each data category.
Solution Approach 2:
The patent merges multiple single-aspect data collection applications into a unified multi-functional system. By combining various data ingestion capabilities into a single platform with standardized normalization processes, the system achieves both comprehensive data coverage and precise metric measurement without the limitations of siloed approaches.
3Speed
If real-time data processing is implemented, then responsiveness to operational changes is improved, but system complexity increases
Solution Approach 1:
The system segments the real-time data processing function into independent smart agents, each responsible for specific data ingestion and normalization tasks. This modular architecture enables real-time processing by distributing computational loads across multiple specialized components, managing system complexity through functional decomposition while maintaining high responsiveness.
Solution Approach 2:
The smart agents perform preliminary data normalization and validation actions in real-time as data is ingested, before it reaches the analytics engines. This preliminary processing reduces the computational burden on subsequent analysis stages, enabling real-time responsiveness while managing overall system complexity through staged processing.
Data Source
AI summary
An apparatus in one embodiment comprises a processing platform that includes a plurality of processing devices each comprising a processor coupled to a memory. The processing platform is configured to implement at least a portion of at least a first cloud-based system. The processing platform further comprises a smart agent engine configured to ingest datasets and related workflows in connection with service delivery operations and normalize the ingested datasets and workflows. The processing platform further comprises an analytics engine configured to generate metrics by applying machine learning algorithms to the normalized datasets and workflows, and generate an enhanced version of the normalized datasets and/or workflows by encompassing at least one inset and/or outflow based on the metrics and one or more algorithms. Also, the processing platform further comprises a decision support module configured to output the ingested datasets, the ingested workflows, and the enhanced dataset and/or workflow.