Application Type Identification via Column Keyword Mapping
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Solution Overview
Problem
Traditional data recovery processes are inefficient in analyzing the data formats of unknown applications, especially with the rapid growth of mobile device applications, leading to difficulties in recovering data from unanalyzed applications.
Innovation Solution
The method identifies the application type of unknown data by recognizing keywords commonly used by specific applications, such as 'author' or 'from' in chat applications, to map column identifiers to corresponding data fields, allowing for data recovery even if the application type is unanalyzed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data recovery processes are used to analyze unknown application data, then the process is simple and straightforward, but the efficiency is low and cannot keep up with the rapid growth of mobile applications
Solution Approach 1:
The system performs preliminary analysis by examining column identifiers and data patterns before full data recovery. It pre-identifies application types by scanning for keywords and structural characteristics in database columns, preparing classification information in advance to accelerate the overall recovery process without requiring complete analysis of all data first
Solution Approach 2:
The patent introduces an intermediary classification layer that sits between raw unknown data and final recovery output. This intermediary system uses column identifier analysis and keyword matching to create an intermediate representation that bridges the gap between unfamiliar application formats and standard recovery procedures, enabling efficient processing without direct complex analysis of every application type
2Measurement precision
If keyword-based identification is used to determine application type, then the identification accuracy improves, but the analysis time increases
Solution Approach 1:
The identification process is segmented into multiple stages: first examining column identifiers for keywords, then analyzing data patterns only in columns that show promise, and finally performing full verification only on candidate matches. This segmentation allows the system to achieve high identification accuracy by focusing detailed analysis only where needed, rather than uniformly analyzing all data with maximum scrutiny
Solution Approach 2:
The system performs partial keyword matching and pattern recognition on subsets of columns rather than exhaustive analysis of all columns. By applying keyword searches to column identifiers and selective sampling of data patterns, the system achieves sufficient identification accuracy with less than complete analysis, reducing time loss while maintaining precision through targeted examination of critical indicators
Data Source
AI summary
The present embodiments relate generally to a computer device, system and method of identifying an application type of unknown data. The method may include: determining that the unknown data corresponds to database information, the database information comprising at least one table with at least one column; for a column of a table in the database information, determining if a column identifier of the column comprises a keyword associated with a particular application type; and if the column identifier comprises the keyword, identifying data stored in the database as belonging to an application that is of the particular application type.