Temporal Search Term Mapping for Machine Data Recommendations
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Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources is time-consuming and inefficient due to the varying formats and types of data, which existing systems struggle to handle effectively.
Innovation Solution
Implementing a data intake and query system with a late-binding schema that extracts and indexes data at search time, using flexible extraction rules and common field names to facilitate analysis across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is extracted and indexed at search time using late-binding schema, then adaptability to diverse data formats is improved, but processing time and computational resources increase
Solution Approach 1:
The system employs dynamic schema binding where extraction rules are not fixed at data ingestion but are flexibly applied at search time. This allows the system to adapt to various data formats dynamically based on the specific search query and data source, resolving the contradiction between adaptability and processing efficiency by making the schema binding process flexible and context-dependent rather than rigid and predetermined
Solution Approach 2:
The late-binding schema approach changes the temporal parameter of schema application from ingestion time to search time. This parameter shift allows the system to maintain adaptability for diverse formats while optimizing processing by only applying extraction rules when actually needed for specific queries, rather than pre-processing all data uniformly
2Productivity
If flexible extraction rules are applied to disparate data sources, then data retrieval effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the data processing functionality into modular extraction rules that can be independently configured and applied to different data sources. Each extraction rule handles specific format transformations, allowing the system to manage complexity through modularity while maintaining flexibility for diverse data formats, thus improving data retrieval effectiveness without overwhelming system complexity
Solution Approach 2:
The late-binding schema acts as an intermediary layer between disparate data sources and the search functionality. This intermediary handles the complexity of format variations by applying appropriate extraction rules dynamically, shielding the rest of the system from format-related complexity while enabling effective data retrieval from diverse sources
3Ease of operation
If common field names are used across diverse data sources, then ease of analysis is improved, but data normalization challenges increase
Solution Approach 1:
The system implements universal field names that serve multiple data sources with different formats. These common field names act as standardized interfaces that simplify analysis operations, while the underlying extraction rules handle the complexity of mapping various source formats to these universal fields, thus improving ease of analysis without requiring manual normalization of each data source
Data Source
AI summary
Embodiments of the present invention are directed to facilitating search input recommendations. In accordance with aspects of the present disclosure, a set of events determined from raw machine data is obtained. The events are analyzed to generate a temporal map associated with the set of events. Generally, the temporal map associates candidate terms with temporally related terms that occur within a period of time corresponding with the candidate terms. A search term input into a search field is received. Based on the input search term, the temporal map is used to identify one or more temporally related term recommendations.