Virtualization Search Engine for Data Center Entity Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current virtualization environments lack an efficient and unified method for searching and managing storage resources and entities, leading to inefficiencies in resource utilization and information retrieval.
Innovation Solution
The implementation of a Controller/Service VM that manages storage devices and a search-engine system for keyword-based, context-sensitive searches within virtualization environments, allowing for the identification and ranking of entity-type nodes and generation of structured queries to retrieve relevant information from various data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple data sources are used to store entity information in virtualization environments, then information completeness and reliability are improved, but information retrieval complexity and time increase
Solution Approach 1:
The system segments entity information into structured categories (entity-type nodes, activity-type nodes, property nodes) and distributes them across multiple data sources. This segmentation allows the search engine to query specific segments rather than scanning entire data sources, reducing retrieval time while maintaining information reliability through distributed storage.
Solution Approach 2:
The search engine acts as an intermediary layer between users and multiple data sources. It receives natural language queries, translates them into structured search operations, and aggregates results from various data sources. This intermediary function simplifies the complexity of accessing multiple data sources while maintaining information reliability.
2Ease of operation
If a unified search interface is implemented across distributed data sources, then ease of operation is improved, but system complexity increases
Solution Approach 1:
The search engine is designed as a universal system that handles multiple types of queries (entity search, property search, action search) across different data sources through a single interface. This multi-functionality allows users to access all entity information uniformly without needing to know the underlying data source complexity.
Solution Approach 2:
The system creates a virtual copy of the distributed data sources through entity-type nodes and activity-type nodes in the search index. This copying mechanism allows the search interface to operate on simplified representations rather than directly querying complex distributed storage systems, reducing operational complexity.
3Ease of operation
If keyword-based search is used for entity retrieval, then ease of operation is improved, but search precision and relevance decrease
Solution Approach 1:
The search system applies different quality processing to different parts of the query and data. Entity-type nodes receive exact matching processing, while property nodes and activity nodes receive fuzzy matching and ranking processing. This local quality differentiation maintains ease of keyword-based operation while improving search precision through context-aware processing.
Solution Approach 2:
The system changes the parameter of search results by ranking entity-type nodes based on multiple factors (match quality, relevance, frequency). This parameter transformation converts simple keyword matching into a ranked relevance system, improving precision while maintaining the simplicity of keyword-based input.
Data Source
AI summary
In one embodiment, a method may comprise receiving a query to search for one or more entities of the data center, the query including one or more query tokens. The method may then identify one or more entity-type nodes corresponding to the query, and rank the entity-type nodes based on a score determined for each of the identified entity-type nodes. For at least a portion of the ranked entity-type nodes, the method may generate a structured query associated with each of the entity-type nodes based at least in part of one or more activity-type nodes connected to each of the entity-type nodes. Then, the method may retrieve, based on the structured queries, results from one or more databases.