Mobile Banking Interface With Location-Based Privacy Masking
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Solution Overview
Problem
Existing visualization systems are tightly coupled with data sets, making them ineffective when data structures change, and fail to adapt to dynamic data sets or diverse device capabilities, leading to suboptimal presentation.
Innovation Solution
An intelligent data presentation system that selects presentations based on device and data set properties, generating flexible and adaptive visualizations by considering device capabilities, data set characteristics, and potential user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a visualization system is tightly coupled with a data set structure, then the visualization generation is simple and straightforward, but the system becomes ineffective when data structures change and cannot adapt to dynamic data sets
Solution Approach 1:
The system dynamically adapts visualizations to changing data structures by detecting structural changes and automatically adjusting the visualization type and configuration. The visualization system transitions from a static, tightly-coupled approach to a dynamic one that can respond to data set modifications, making the system adaptable without requiring complete reconfiguration.
Solution Approach 2:
The system changes parameters such as visualization type, data field mappings, and presentation format based on the detected data structure. By modifying these parameters dynamically rather than maintaining fixed couplings, the system achieves adaptability to different data structures while managing complexity through parameterized configurations.
2Reliability
If a visualization assumes a specific data set format, then the visualization code is simple and easy to implement, but the visualization breaks or becomes ineffective when the data format changes
Solution Approach 1:
The visualization system is designed to handle multiple data formats and structures through a unified interface. Rather than creating separate visualization code for each format, the system uses a universal approach that can adapt to different formats, improving reliability across format changes while managing complexity through a single multi-functional system.
Solution Approach 2:
The system incorporates feedback mechanisms that detect changes in data format and automatically adjust the visualization configuration. This feedback loop ensures the visualization remains reliable by continuously monitoring data structure changes and responding appropriately, rather than relying on static assumptions about data format.
3Adaptability or versatility
If new fields are added to a data set, then the data set becomes more comprehensive, but the existing visualization may break or omit the new fields
Solution Approach 1:
The system performs preliminary actions by pre-defining visualization templates and field mappings that can accommodate new data fields. When new fields are added to the data set, the system has already prepared the structural framework to integrate these fields, reducing the need for manual reconfiguration and maintaining ease of operation.
Solution Approach 2:
The visualization system segments the data handling into modular components, where each field can be independently mapped and configured. This segmentation allows new fields to be added without affecting the entire visualization system, making it easier to maintain while improving adaptability to expanding data sets.
4Adaptability or versatility
If a scatter plot visualization is used for hierarchical data, then the visualization can effectively represent multi-dimensional data, but it fails when fields are not hierarchically comparable
Solution Approach 1:
The system uses feedback mechanisms to detect the hierarchical comparability of data fields before selecting the visualization type. By monitoring data properties and providing feedback to the visualization selection logic, the system can determine whether a scatter plot is appropriate or if an alternative visualization should be used, improving adaptability without excessive complexity.
Solution Approach 2:
The system changes the visualization type parameter based on the detected data characteristics. When fields are determined to be non-hierarchical or incomparable, the system adjusts the visualization parameter from scatter plot to an alternative type, achieving adaptability to different data types while managing complexity through parameter-based switching.
Data Source
AI summary
An example embodiment includes one or more processors configured to execute computer executable instructions stored in a memory to determine a device property of a mobile device including a location of the mobile device using a geographic positioning system detection component, select a security mode for the mobile device based on a device property where a high security mode is selected when the mobile device is located in a first location, and a low security mode is selected when the mobile device is located in a second location where the second location is specified as part of a preference associated with a user account associated with the mobile device, and selectively provide a visualization on a user interface that masks a portion of the visualization in the high security mode and does not mask the portion of the visualization in the low security mode.