Geographic Indexing for Distributed Data Storage
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Solution Overview
Problem
Current systems face challenges in processing and storing large amounts of geographically-referenced data, particularly in wide area surveillance systems, due to processing and memory limitations, and inability to associate point of interest data with social context, leading to difficulties in inferring fine-grained activity patterns and effectively handling complex data queries.
Innovation Solution
A distributed computing system utilizing Hadoop and HBase technologies to process and store geographically-referenced data, integrating open source intelligence from social media platforms, and generating index values for efficient data storage and analysis, allowing for the association of point of interest data with social context and improved handling of large data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If distributed storage systems are used to handle large data volumes, then data storage capacity is improved, but system complexity increases
Solution Approach 1:
The patent segments the storage system into multiple distributed nodes that independently store portions of the geographically-referenced data. Each node operates autonomously while contributing to the overall storage capacity, allowing the system to handle large data volumes without requiring a monolithic complex architecture.
Solution Approach 2:
The distributed storage nodes are designed to perform multiple functions including data storage, geographic indexing, and query processing. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while maintaining high storage capacity.
2Loss of information
If geographically-referenced data from multiple sources is integrated, then data completeness is improved, but data processing complexity increases
Solution Approach 1:
The patent applies local quality by organizing data according to geographic regions, where each region's data is processed and stored with specific local characteristics. This geographic-based organization simplifies the integration of multi-source data by allowing localized processing rules to be applied rather than requiring uniform complex processing across all data.
Solution Approach 2:
The system introduces geographic indexing as an intermediary layer between raw multi-source data and the final integrated dataset. This intermediary structure standardizes data from different sources by mapping them to geographic coordinates, thereby completing the data while managing processing complexity through a unified spatial reference framework.
3Measurement precision
If fine-grained activity pattern analysis is performed, then analytical precision is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary geographic indexing and data organization before conducting fine-grained activity pattern analysis. By pre-organizing data spatially and temporally, the system reduces the computational burden during actual analysis while maintaining high analytical precision for detecting patterns such as anomalies and trends.
Data Source
AI summary
Embodiments of a system and method for storing and analyzing geographically-referenced data are generally described herein. In some embodiments, the system includes one or more computing devices to generate an index value for geographically referenced data. The index value may be representative of a geographic location corresponding to the geographically-referenced data. The system may also include one or more storage devices configured to store the geographically-referenced data and the index value such that the geographically-referenced data is stored contiguously with other geographically-referenced data of the geographic location based on the index value.