Smart Public Alert System for Contextual Personalization
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Solution Overview
Problem
Current public alert systems in smart cities face challenges in delivering personalized and contextual alerts in real-time, as citizens receive irrelevant alerts due to dynamic location changes and the need for efficient processing of vast amounts of unstructured data from various sources, leading to high processing times and memory overhead.
Innovation Solution
A smart public alert system that processes user profile and contextual data to pre-filter and extend background knowledge, converting it into a structured format for stream reasoning to determine relevant alerts, reducing unnecessary data loading and enhancing processing speed and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes all unstructured data from various sources in real-time, then the alert system can detect all events, but the processing time and memory overhead increase significantly
Solution Approach 1:
The system performs preliminary processing by converting unstructured data into structured knowledge format before it enters the stream reasoning module. This pre-processing step organizes and cleans the data, reducing the processing burden on subsequent components and enabling faster real-time analysis without losing detection completeness.
Solution Approach 2:
The data processing pipeline is segmented into distinct stages: data collection, unstructured to structured conversion, knowledge extension, and stream reasoning. Each stage handles specific portions of the data, allowing efficient processing and reducing overall memory overhead while maintaining comprehensive event detection capability.
2Loss of information
If the system provides personalized alerts based on user location preferences, then alert relevance improves, but citizens receive irrelevant alerts due to dynamic location changes
Solution Approach 1:
The system dynamically updates user profiles and alert preferences based on real-time location data and contextual information. Instead of relying on static location preferences, the system continuously adapts to user movements and contextual changes, ensuring alerts remain relevant without requiring users to manually update their preferences.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor user interactions with alerts and adjust the alerting strategy accordingly. By analyzing user behavior patterns and alert engagement, the system refines its personalization algorithm to better predict user needs and reduce irrelevant notifications.
3Productivity
If the system uses stream reasoning to process knowledge streams, then real-time alert generation improves, but the system complexity increases
Solution Approach 1:
The system introduces a knowledge converter module as an intermediary between the raw data sources and the stream reasoning engine. This intermediary component transforms unstructured data into structured knowledge representations, simplifying the reasoning process and reducing the complexity burden on the stream reasoning module while maintaining high alert generation speed.
4Loss of information
If the system extends background knowledge using reasoning, then alert contextualization improves, but processing overhead increases
Solution Approach 1:
The system applies partial reasoning by selectively extending background knowledge only for the specific context and user profile relevant to the current alert. Instead of processing all possible knowledge extensions, the system focuses reasoning efforts on the most pertinent knowledge graphs and relationships, reducing processing overhead while maintaining comprehensive contextualization.
Data Source
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AI summary
Disclosed is a method and system for dynamically generating a customized, personalized and contextual alert for a user based upon personalized, contextual and background knowledge associated with the profile of the user. The system comprises a profile updater module configured to update the profile data of the user extracted either from the social web or from the user. Further, a reasoning module is configured to derive refined background knowledge in context with the updated profile data of the user. A monitoring module is configured to monitor the events sensed by a sensing module. A context extractor module is configured for extracting the context of the events and the context of the user. A knowledge converter module is configured to convert the extracted context into structured format. Finally, the refined background knowledge is steam reasoned to determine whether the events received are relevant to the user and accordingly transmitted.