Filter Engine for Vertically Split Product Data
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Solution Overview
Problem
The challenge lies in efficiently processing and analyzing large volumes of multi-dimensional and multi-attribute product transaction data collected from various sources, as storing and analyzing all data is burdensome and unnecessary for extracting meaningful insights, especially when data is split across vertically split attribute files.
Innovation Solution
A method involving a filter engine that receives queries with attribute conditions to identify and filter product transaction data records across vertically split attribute data files, using identified keys to selectively retain relevant data for analytics, thereby reducing data volume and focusing on meaningful insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all collected data is stored and analyzed, then complete information is available for insights, but data volume and storage burden become excessive
Solution Approach 1:
The filter engine extracts only the necessary data elements from the large collected dataset by applying attribute conditions and filters. It identifies and retains only those data records that meet specified criteria, discarding redundant information while preserving meaningful insights.
Solution Approach 2:
The system performs preliminary filtering of data before storage and detailed analysis. By applying filters and conditions in advance, the system pre-processes the data to retain only relevant records, reducing the burden on downstream storage and analytics systems.
2Ease of operation
If data is structured and consolidated for storage, then data organization is improved, but processing complexity increases
Solution Approach 1:
The filter engine processes data in manageable segments by evaluating attribute conditions on individual data records. It divides the filtering task into discrete steps: reading data, evaluating conditions, and writing filtered results, making the complex processing task more manageable and systematic.
3Loss of information
If multiple attribute and multi-dimensional data is collected from various sources, then data comprehensiveness is improved, but data processing difficulty increases
Solution Approach 1:
The filter engine is designed as a universal processing system that can handle multiple data sources, various attribute types, and different filtering conditions through a single unified interface. It processes diverse data formats and dimensions using the same core filtering mechanism, simplifying the handling of comprehensive multi-dimensional data.
Data Source
AI summary
A method for filtering a product transaction data file involves receiving, by a computer, a query including one or more groups of attribute conditions for filtering the product transaction data file. The attribute conditions in the query are applicable to product attribute data records split across a set of vertically split attribute data files. The method further includes determining which of the product attribute data records split across the set of vertically split attribute data files satisfy all of the one or more groups of attribute conditions in the query, identifying product keys associated with the product attribute data records that satisfy all of the one or more groups of attribute conditions in the query, and using the so-identified product keys to filter product data records in the transaction data file.