Entity Definition Creation from Search Results
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Solution Overview
Problem
Modern data centers face challenges in processing and indexing large volumes of machine-generated data due to its unstructured nature, making it difficult to apply semantic meaning and effectively monitor service-level performance.
Innovation Solution
The creation of entity definitions from search result sets allows for normalization of heterogeneous machine data, enabling flexible association of entities with services and dynamic monitoring of key performance indicators (KPIs), which simplifies the use of machine data for service monitoring and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If machine data is processed and indexed in its original unstructured form, then the volume of data that can be stored is increased, but the difficulty of applying semantic meaning and performing searching operations increases significantly
Solution Approach 1:
The patent applies parameter changes by transforming unstructured machine data into structured entity definitions with standardized parameters and attributes. This conversion process assigns semantic meaning to raw data through parameterization, enabling efficient searching and analysis while preserving the original data volume.
Solution Approach 2:
The patent introduces entity definitions as an intermediary layer between raw machine data and search operations. These entity definitions serve as structured representations that bridge the gap between unstructured data and semantic querying, making the data searchable without losing volume or detail.
2Manufacturing precision
If entity definitions are created manually from machine data, then the precision of entity representation is improved, but the time and resources required for creation increase
Solution Approach 1:
The patent applies preliminary action by automatically generating entity definitions from machine data before manual refinement is needed. This pre-processing step creates initial entity representations that capture essential structures, reducing the time required for subsequent manual work while maintaining precision through iterative refinement.
Solution Approach 2:
The system enables self-service by allowing entity definitions to be automatically created and updated from machine data without requiring manual intervention for every change. The system self-adjusts entity representations based on incoming data, maintaining precision while minimizing time investment from users.
3Ease of operation
If traditional searching methods are used on unstructured machine data, then the simplicity of the search process is maintained, but the effectiveness of retrieving meaningful results decreases
Solution Approach 1:
The patent transforms unstructured data into parameterized entity definitions, enabling searches to operate on structured parameters rather than raw text. This maintains search simplicity while dramatically improving result effectiveness by allowing precise filtering and querying based on defined parameters and relationships.
Data Source
AI summary
A processing device performs a search query to produce a search result set having entries having data items. A table, having rows and columns, is displayed in a user interface. Each data item of a particular entry appears in a respective column of the same row of the table. Each column may correspond to the ordinal position of its respective data item. User input is received designating, for each respective column, a field name and an entity definition component type to which the respective column pertains, and stores for each data item of the particular entry an element value of an entity definition. The element has the element name designated for the respective column in which the data item appeared, and is associated with an entity definition component having the type designated for the respective column in which the data item appeared.