Blockchain Transaction Filtering System
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Solution Overview
Problem
Users face challenges in extracting relevant information from the vast amount of data on blockchain transactions, as current monitoring solutions are limited and require manual processing to identify transactions of interest.
Innovation Solution
A system that allows users to specify filtering expressions using an expression language to match desired transactions, automatically processing blockchain transactions in real-time or batches, and notifying users of matches via notification endpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually process blockchain transactions to identify transactions of interest, then they can extract relevant information, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service by allowing users to define their own filtering criteria through filtering expressions. The automated monitoring system then independently processes transactions according to these user-defined parameters, eliminating the need for manual processing while maintaining precise information extraction.
Solution Approach 2:
The patent replaces the mechanical manual processing system with an automated computational system. The processor automatically monitors transactions, evaluates filtering expressions, and identifies matching transactions, substituting human manual analysis with machine-based automated processing that is both faster and equally precise.
2Loss of information
If users monitor all blockchain transactions to identify relevant ones, then they can find transactions of interest, but the complexity of processing vast amounts of data increases
Solution Approach 1:
The system extracts only the relevant information needed for filtering by allowing users to define specific filtering criteria. Instead of processing all transaction data, the system extracts and evaluates only the parameters specified in the filtering expressions, reducing processing complexity while maintaining information completeness for relevant transactions.
Solution Approach 2:
The monitoring system segments the vast transaction data by applying multiple filtering expressions that divide the data stream into relevant and irrelevant portions. Each filtering expression acts as a segmentation criterion, breaking down the complex data processing task into manageable evaluation steps based on user-defined parameters.
3Ease of operation
If current monitoring solutions are used, then basic transaction monitoring is possible, but they require manual processing and lack automated filtering capabilities
Solution Approach 1:
The system enables self-service by allowing users to define their own filtering criteria through filtering expressions. The automated monitoring system then independently processes transactions according to these user-defined parameters, eliminating the need for manual processing while maintaining precise information extraction.
Solution Approach 2:
The system performs preliminary action by pre-defining filtering expressions that specify the criteria for identifying transactions of interest. These filtering expressions are prepared in advance and automatically applied to incoming transactions, eliminating the need for manual analysis and enabling fully automated monitoring and identification of relevant transactions.
Data Source
AI summary
Embodiments of the present disclosure provide method, apparatus, and computer-readable medium for filtering. An exemplary method includes monitoring, by a processor, at least one of a plurality nodes of at least one of a plurality of digital distributed ledger systems, and receiving a filtering expression from a user equipment (UE), the filtering expression comprising search parameters for identifying future transactions on the at least one of a plurality of digital distributed ledger systems. The method further includes detecting a plurality of transactions on the at least one of a plurality of digital distributed ledger systems at the at least one of a plurality of nodes, determining that at least one of the plurality of transactions on the at least one of a plurality of digital distributed ledger systems at the at least one of a plurality of nodes has a correspondence with the filtering expression, and storing a first data to at least one of a plurality of UEs.