Time Weight Calculation in RDF Graphs via Inference Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In enterprise communication and collaboration, existing technologies face challenges in efficiently updating time weights in social relations represented by RDF graphs, particularly when new reference timestamps are provided, leading to inefficiencies in calculating and reevaluating the strength of social interactions over time.
Innovation Solution
A method and system that utilize an inference engine to calculate and update time weights in RDF graphs by providing triples with time information, using an exponential decay function to inversely weight elapsed time, allowing for efficient incremental updates and optimal time complexity in evaluating social network relations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to recalculate time weights from scratch whenever new timestamps are provided, then measurement precision of social relation strength is improved, but loss of time increases significantly
Solution Approach 1:
The system pre-calculates and stores base time weights for RDF triples during data ingestion, rather than calculating from scratch during queries. This preliminary computation stores the decayed weight values in the RDF graph, enabling fast retrieval and incremental updates later when new timestamps are provided.
Solution Approach 2:
The time weight calculation is segmented into base time weight (pre-calculated from epoch) and incremental adjustment (calculated only for new timestamps). This segmentation allows the system to avoid recalculating the entire time weight from scratch, instead only computing the difference caused by new timestamps and adding it to the existing base weight.
2Reliability
If complete recalculation of time weights is performed whenever new data is added, then reliability of social network analysis is improved, but productivity of the system deteriorates
Solution Approach 1:
Base time weights are pre-calculated and stored during data ingestion, establishing a reliable foundation for social network analysis. This preliminary action ensures that the core time decay computation is performed once with full accuracy, while subsequent updates only require incremental adjustments.
Solution Approach 2:
The system dynamically adapts its calculation approach based on the availability of new timestamps. When new timestamps are provided, only the incremental difference is calculated and added to the existing base weights. This dynamic approach maintains reliability by ensuring accurate time weight representation while preserving productivity through selective recalculation.
3Measurement precision
If detailed time weight calculations are performed for all triples, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The time weight calculation system is segmented into two independent components: base time weight calculation (based on epoch time) and incremental adjustment calculation (based on new timestamps). This segmentation simplifies the overall system complexity by breaking down the complex full recalculation into manageable, reusable parts that can be computed independently and combined.
Solution Approach 2:
The system uses the existing base time weight values as a template or copy, adding only the incremental adjustment from new timestamps. Instead of creating entirely new time weight calculations, the system copies the proven base weights and modifies them minimally, reducing computational complexity while maintaining precision.
Data Source
AI summary
Method to calculate time weight in an RDF graph, including: providing one or more triples of the RDF graph to an inference engine module, the one or more triples comprising a time information; providing a reference time to the inference engine; calculating an elapsed time from the reference time to the time value; and inversely weighting the time information by the elapsed time to provide a calculated time weight. Another embodiment may provide a method to update a time weight of a relation when an RDF graph has been added to or deleted from the relation. Another embodiment may provide a method to update a time weight of a relation represented by an RDF graph, when a new reference timestamp is provided. Another embodiment may provide a system to calculate time weight in an RDF graph.