Multidimensional Hierarchical Semantic Indexing for Big Data Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for indexing big data stores, such as those using Resource Description Framework (RDF) triples, face inefficiencies in search and retrieval due to the lack of effective embedding of semantic information and the inability to efficiently handle auxiliary information like space-time tagging, leading to unwieldy data sets that are difficult to process.
Innovation Solution
A method that combines multidimensional indexing with ontological information embedded in RDF to create a semantic index, using a hierarchical-indexing scheme that represents each RDF element as a hierarchical-index value and combines them into a multi-dimensional index, which is then converted into an efficient one-dimensional index, incorporating space-time tagging for enhanced search capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in a traditional database format without semantic indexing, then the database structure remains simple, but search and retrieval efficiency deteriorates as data grows into billions of elements
Solution Approach 1:
The patent segments the indexing process into multiple dimensions: semantic dimension (using RDF triples and ontologies), spatial dimension (using Z-order curves), and temporal dimension (using time stamps). Each dimension is indexed separately and then combined, allowing efficient multi-criteria search without creating an overly complex monolithic index structure
Solution Approach 2:
The patent transforms the traditional single-dimensional index into a multi-dimensional index by adding semantic dimensions (subject, predicate, object types from ontologies) and spatial dimensions (using Z-order curve encoding). This dimensional expansion enables efficient filtering and retrieval across multiple criteria simultaneously, resolving the contradiction between search efficiency and structural complexity
2Measurement precision
If semantic information is embedded in the index using ontology-based hierarchical indexing, then search precision improves, but the indexing process becomes more complex
Solution Approach 1:
The patent performs preliminary action by pre-processing data into standardized RDF triples and pre-establishing ontology hierarchies before indexing. The Z-order curve encoding is pre-computed for spatial coordinates. These preliminary steps organize the data in a way that enables precise semantic search without requiring complex real-time processing during query execution
Solution Approach 2:
The patent introduces an intermediary layer (the semantic index structure based on ontologies) that mediates between the raw data and the search queries. This intermediary translates natural language or high-level search criteria into precise ontology-based queries, improving search precision while keeping the overall system architecture manageable through clear separation of concerns
3Adaptability or versatility
If auxiliary information like space-time tagging is incorporated into the index, then retrieval capabilities are enhanced, but the index size and processing requirements increase
Solution Approach 1:
The patent merges multiple types of information (spatial coordinates, time stamps, semantic attributes) into a unified multi-dimensional index structure. By combining these elements into a single composite index rather than maintaining separate indexes, the system enhances retrieval capabilities across multiple criteria while avoiding the overhead of managing multiple separate index structures
Solution Approach 2:
The patent adds spatial and temporal as additional dimensions to the semantic index, creating a multi-dimensional indexing space. This dimensional expansion enables versatile retrieval operations (spatial queries, temporal queries, combined spatio-temporal queries) without requiring separate index structures for each type of auxiliary information, thus managing index size efficiently
Data Source
AI summary
A method for indexing semantic, non-transitory, computer-stored data comprising the following steps: storing the data in a database; representing the data in a structured framework having at least three elements derived from an ontology; expressing each element as a hierarchical-index value based on an ontology such that semantic information is embedded therein; combining the elements in a multi-dimensional index; and converting the multi-dimensional index into a one-dimensional index.