Ad Hoc Network Data Indexing for Predictive Decision-Making
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Solution Overview
Problem
Conventional systems for data analysis in rapidly changing environments are inefficient in processing and analyzing spatial, temporal, and contextual elements, leading to unmanageable information and slow decision-making processes, particularly in real-time predictive scenarios.
Innovation Solution
A computer-implemented method and system that receives and indexes data with spatial, temporal, and contextual elements, allowing for dynamic rule creation and modification, and uses a cloud computing environment with distributed nodes to facilitate predictive decision-making by identifying events that satisfy defined rules and providing real-time notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional systems store huge buffer stocks of data inventory to protect against change, then data availability is improved, but information manageability deteriorates
Solution Approach 1:
The patent extracts only the essential elements needed for predictive decision-making from the data buffer stock. Instead of managing entire datasets, the system identifies and extracts specific spatial, temporal, and contextual elements that are relevant to current predictive needs, thereby reducing information manageability complexity while maintaining data availability.
Solution Approach 2:
The system performs preliminary indexing of data elements by their spatial, temporal, and contextual characteristics before actual predictive analysis is needed. This preliminary organization allows the system to quickly retrieve and analyze only relevant data elements when predictive decisions are required, avoiding the need to manage and process entire data inventories in real-time.
2Quantity of substance
If conventional systems rely on large quantities of data to analyze rapidly changing environments, then data completeness is improved, but real-time predictive decision-making deteriorates
Solution Approach 1:
The patent applies local quality by treating different data elements with different levels of processing priority based on their relevance to predictive decision-making. Instead of uniformly processing all data, the system identifies and prioritizes specific spatial, temporal, and contextual elements that are locally relevant to current predictive needs, enabling faster real-time analysis while maintaining data completeness where necessary.
Solution Approach 2:
The system performs partial action by analyzing only the subset of data elements that are currently relevant to predictive decision-making, rather than processing the entire data inventory. The indexing mechanism allows the system to selectively apply analysis to pertinent spatial, temporal, and contextual elements, achieving real-time predictive capability without being burdened by excessive data processing.
3Loss of information
If conventional search engines store static information in databases for later queries, then information retrieval capability is improved, but real-time dynamic analysis deteriorates
Solution Approach 1:
The patent transforms the static information storage model into a dynamic one by implementing real-time indexing of data elements as they become available. Instead of storing static snapshots for later retrieval, the system continuously updates and maintains indexed structures that reflect current spatial, temporal, and contextual relationships, enabling both information retrieval and real-time dynamic analysis simultaneously.
Solution Approach 2:
The indexed data structure serves as an intermediary between raw data input and predictive analysis output. This intermediary layer pre-organizes data elements by their spatial, temporal, and contextual characteristics, allowing the system to efficiently retrieve and analyze relevant information in real-time without the bottleneck of querying static databases.
4Reliability
If conventional systems require administrative support for rule definition and modification, then system control is improved, but user flexibility and response time deteriorate
Solution Approach 1:
The patent implements self-service by enabling end-users to directly define, modify, and manage their own predictive rules without requiring administrative intervention. The system provides user-friendly interfaces and tools that allow users to create custom rules based on their specific needs, adjust parameters in real-time, and immediately apply changes to their predictive analyses, thereby achieving both system reliability and user flexibility.
Data Source
AI summary
A computer-implemented system and method of predictive decision-making in an ad hoc network. The computer-implemented method includes receiving a set of rules into the ad hoc network and identifying a data set for each rule. The computer-implemented method also includes selecting a first and second node from the ad hoc network to process a first and second rule as a function of the identified data set according to an optimizing algorithm. The computer-implemented method also selects a third node to receive the processed results from the first and second nodes. An indication is provided of the processed results by the third node.