Event Tracking Search Structure Using Spacetime and Topic Hashes
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Solution Overview
Problem
Existing event tracking systems based on clustering algorithms and spatial data search algorithms become data-intensive and slow as the number of records increase to billions or trillions, failing to efficiently detect significant events using public/private data and spatial correlations.
Innovation Solution
A system utilizing spacetime hashes and topic hashes to generate a search data structure that groups events into subsets, enabling rapid query execution and correlation through a trie or bitmap data structure, allowing for efficient event tracking and detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clustering algorithms and spatial data search algorithms are used for event tracking, then event detection capability is improved, but search speed and data processing efficiency deteriorate as the number of records increases to billions or trillions
Solution Approach 1:
The patent segments the large-scale data processing task into two distinct phases: an offline phase that pre-computes and stores clustered event data structures, and an online phase that performs rapid queries against these pre-processed structures. This segmentation allows the system to maintain high event detection capability while achieving fast search speeds, as the computationally intensive clustering operations are performed only once during offline processing rather than repeatedly during online queries.
Solution Approach 2:
The patent performs preliminary action by pre-computing event clusters, generating event codes, and building searchable data structures before actual query operations. The offline phase conducts all heavy computational work including spatial clustering, temporal alignment, and correlation analysis, storing results in optimized structures that enable rapid online retrieval. This preliminary preparation eliminates the need to re-process raw data during time-critical event detection operations.
2Measurement precision
If clustering algorithms and spatial data search algorithms are used for event tracking, then event correlation accuracy is improved, but data processing resource consumption increases significantly
Solution Approach 1:
The patent divides resource-intensive processing into offline batch operations and lightweight online queries. Complex clustering algorithms, spatial calculations, and correlation analyses are executed during offline processing when resources are abundantly available, while online operations merely retrieve and display pre-computed results. This segmentation maintains high event correlation accuracy through thorough offline analysis while minimizing real-time resource consumption.
Solution Approach 2:
The patent creates compressed representations and codes of events that capture essential correlation information in compact form. Instead of storing and processing complete raw event datasets repeatedly, the system generates event codes and clustered representations that preserve correlation accuracy while occupying minimal storage and processing resources during online operations.
3Adaptability or versatility
If the number of records to search through increases to billions or trillions, then event detection coverage is improved, but existing techniques become slower and more data intensive
Solution Approach 1:
The patent introduces temporal dimension to the data processing architecture by distinguishing between offline batch processing and online real-time querying. This dimensional separation allows the system to handle billions or trillions of records efficiently: offline operations process vast datasets at relaxed time constraints using full computational resources, while online operations respond to user queries instantly by querying pre-processed structures. The temporal dimension resolves the contradiction between comprehensive coverage and processing efficiency.
Solution Approach 2:
The patent segments the monolithic data processing workflow into independent offline and online phases with distinct objectives and resource requirements. The offline phase handles comprehensive data processing for maximum event detection coverage, while the online phase provides rapid responses for user interactions. This segmentation enables the system to scale to billions or trillions of records without sacrificing productivity, as each phase operates independently with optimized resource allocation.
Data Source
AI summary
The present system pertains to event tracking, involving the acquisition of event information, generation of compound hashes, and formation of a search data structure. Event information includes time-and-geolocation data and topic data for each event. Compound hashes, comprising a spacetime hash and a topic hash for each event, are generated based on this information. A search data structure is formed using these compound hashes, enabling the grouping of spacetime hashes and structuring of topic hashes. Upon receiving a query, the system executes a search based on the query's compound hash or parameters, returning matching event content. The system can also generate semantic descriptions of relevant events using a narrative generation model and predict event relevance using a preferential learning model.