Real-Time Data Aggregation for Transaction Trend Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional data processing systems are inefficient in handling large volumes of data, leading to memory and computing resource waste, as well as network bottlenecks, due to their inability to effectively analyze and transmit relevant information to users.
Innovation Solution
A system that aggregates data from multiple sources, identifies relevant trends, and sends targeted notifications to specific user groups based on demographics and transaction history, reducing unnecessary data processing and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional systems store and process large volumes of raw data, then data analysis capability is improved, but memory space and computing power are excessively consumed
Solution Approach 1:
The patent extracts only the essential and relevant features from raw data during the indexing phase, rather than storing complete raw data. The system identifies and extracts key attributes that are sufficient for search and analysis purposes, discarding redundant information. This extraction principle resolves the contradiction by maintaining data analysis capability while significantly reducing memory space consumption.
Solution Approach 2:
The patent performs preliminary data processing, indexing, and filtering before actual data analysis operations. By pre-processing data to extract essential features and organize them in an optimized structure, the system reduces the computational burden during analysis. This preliminary action allows efficient data analysis without requiring excessive memory and computing power during operational phases.
2Loss of information
If traditional systems transmit raw data over the network, then data completeness is improved, but network bandwidth is excessively consumed
Solution Approach 1:
The patent extracts only the necessary data elements required for specific analysis tasks or user needs before transmission. Rather than transmitting complete raw datasets, the system identifies and transmits only relevant extracted features and indexed information. This maintains sufficient data completeness for analysis purposes while dramatically reducing network bandwidth consumption.
3Loss of information
If traditional systems send data reports to all users, then information coverage is improved, but network resources are wasted on irrelevant data
Solution Approach 1:
The patent applies local quality by customizing data transmission according to specific user profiles, preferences, and relevance criteria. Different users receive different subsets of data tailored to their individual needs and characteristics. This ensures each user receives appropriate information coverage without wasting network resources on irrelevant data that would not be utilized.
Solution Approach 2:
The patent implements partial action by sending only the necessary portion of data to each user based on their specific needs, rather than transmitting complete datasets to all users. The system determines the optimal subset of information for each recipient, providing sufficient coverage for their requirements while avoiding the excessive resource consumption of universal data distribution.
4Loss of information
If traditional systems process all incoming data, then processing thoroughness is improved, but system performance decreases due to increased burden
Solution Approach 1:
The patent extracts only the essential processing steps and data elements necessary for achieving analysis objectives. By identifying and processing only critical features and attributes, the system maintains thoroughness in processing relevant information while eliminating redundant processing operations that would degrade system performance.
Solution Approach 2:
The patent performs preliminary filtering, indexing, and data preparation before main processing operations. This pre-processing reduces the volume and complexity of data requiring intensive processing, thereby maintaining processing thoroughness for essential elements while improving overall system performance by reducing the computational burden.
Data Source
AI summary
A system for performing data trend analysis is disclosed. The disclosed system categorizes a number of transaction records based on their associated merchant IDs. For each of the merchant ID, the system determines whether the determined number of transaction records exceeds a pre-determined threshold. In response to identifying a merchant ID with the determined number of transaction records that exceeds the pre-determined threshold, the system identifies a location associated with the merchant ID and generates a trending notification indicating a transaction burst. The system then identifies a set of users located in the location that have a transaction history with the merchant ID and sends the trending notification to the set of users.