Multi-tiered Fleet Management Cache for Sparse Data
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Solution Overview
Problem
Fleet asset management systems face challenges in efficiently accessing and processing sparse database information related to asset availability and reservations, which requires significant processing power and is difficult to interpret.
Innovation Solution
A system that converts sparse availability and reservation data into a dense format using a windowing module, provides a bit-vector view, and samples this data to create a static view, allowing for real-time updates and consistency across multiple application servers through distributed invalidation or journal table polling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sparse format is used to store availability and reservation data, then database size is reduced, but processing power requirements increase and data interpretability decreases
Solution Approach 1:
The patent divides the database into multiple tiers: sparse format storage for compactness, windowed data for time-specific queries, and bit-vector cache for rapid access. Each tier serves specific query patterns, segmenting the data handling workload to optimize both storage efficiency and processing speed for different operational needs.
Solution Approach 2:
The system pre-computes and stores windowed data and bit-vector representations in advance. When queries arrive, the results are already prepared in the cache tiers, eliminating the need for expensive real-time processing of sparse data and enabling rapid response to availability queries.
2Quantity of substance
If sparse format is used to store availability and reservation data, then database size is reduced, but data interpretability becomes difficult
Solution Approach 1:
The patent introduces intermediate data representations (windowed data and bit-vector cache) that act as mediators between the sparse storage format and the query interface. These intermediaries translate the compact sparse format into human-readable and easily queryable formats, making data interpretation simple while maintaining storage efficiency.
3Ease of operation
If windowed data in dense format is created from sparse data, then data interpretability improves, but processing power requirements increase
Solution Approach 1:
The system pre-computes windowed data representations and stores them in a cache tier before queries arrive. This preliminary action transforms sparse data into interpretable dense format in advance, so that when queries are executed, the processing work has already been completed, maintaining both interpretability and processing efficiency.
Solution Approach 2:
The patent applies different data formats to different query scenarios: sparse format for storage, windowed dense format for time-specific interpretability needs, and bit-vector cache for rapid boolean operations. Each format is optimized for its specific local purpose, balancing interpretability and processing efficiency for different operational contexts.
Data Source
AI summary
A method of providing reservation status relating to a group of objects includes providing a database for containing (i) availability data describing availability of at least one of the group of objects, and (ii) reservation data describing allocation of at least one of the group of objects. The availability data and the reservation data are stored in sparse form. The method further includes extracting, from the database, availability data and reservation data corresponding to a predetermined period of time, and combining the extracted availability data and reservation data to form windowed data in a dense format. The method also includes receiving the windowed data and providing the windowed data in a bit-vector view, and sampling the windowed data in the bit-vector view to produce sampled data. The sampled data provides the reservation status as a static view of the windowed data in the bit-vector view.