Time Series Search Engine Using Time Bucketed Indexing
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Solution Overview
Problem
Current search engines are inadequate for handling time series data, as they lack efficient time-based indexing, searching, and result presentation mechanisms, which are crucial for understanding and working with large volumes of time-stamped data from sources like server logs and sensor data.
Innovation Solution
A time series search engine (TSSE) is developed, utilizing a time-based indexing and search mechanism that organizes data into time bucketed indices, allowing for efficient searching and presentation of results through a time series search language (TSSL), enabling users to specify time windows, frequency, and distribution patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing search engines are used for time series data, then keyword-based searching is available, but time-based indexing and searching capabilities are lacking
Solution Approach 1:
The patent segments time series data into discrete time buckets or time intervals, organizing events by their temporal occurrence. This segmentation enables efficient time-based querying by dividing the continuous time stream into manageable, indexable units that can be rapidly accessed without processing the entire dataset.
Solution Approach 2:
The patent introduces time as an additional dimension for indexing and organizing data, transforming traditional single-dimension (keyword-based) search into multi-dimensional search capabilities. By adding temporal dimensionality to the indexing structure, the system enables simultaneous filtering by both keywords and time ranges without sacrificing performance.
2Measurement precision
If time series data is indexed with fine-grained time resolution, then precise time-based searching is enabled, but index construction and search efficiency deteriorate
Solution Approach 1:
The patent applies different levels of time granularity to different portions of the time series data based on local requirements. Recent or frequently queried time periods are indexed with finer resolution, while historical or less-accessed periods use coarser granularity. This local differentiation optimizes both query precision and processing efficiency by matching index detail to actual usage patterns.
Solution Approach 2:
The system dynamically adjusts time bucket size and resolution parameters based on query patterns, data characteristics, and resource constraints. By changing the temporal parameter granularity adaptively, the system maintains high search efficiency while preserving necessary time precision for different query scenarios without uniformly sacrificing either precision or performance.
3Adaptability or versatility
If multiple data sources are indexed asynchronously, then comprehensive data collection is achieved, but time synchronization and ordering become difficult
Solution Approach 1:
The patent introduces a standardized time bucket framework as an intermediary layer between multiple asynchronous data sources and the indexing system. Each source's events are mapped to standardized time buckets, which serve as a common temporal reference frame. This intermediary abstraction handles the complexity of time synchronization, allowing diverse sources to be integrated without direct pairwise synchronization while maintaining consistent temporal ordering.
Solution Approach 2:
The time bucket structure serves multiple functions simultaneously: it acts as a synchronization mechanism, an organization framework, a query optimization structure, and a temporal reference system. This universal structure handles time alignment, data organization, and query processing in a unified manner, reducing overall system complexity despite the multi-source asynchronous nature of the input data.
4Speed
If time series data is organized for efficient time range access, then historical data retrieval is improved, but caching recent results becomes less beneficial
Solution Approach 1:
The patent implements dynamic caching strategies that adapt to query patterns and data characteristics. Instead of uniformly caching recent results, the system dynamically determines which time buckets and query results to cache based on access frequency, recency, and predicted future utility. This dynamic approach optimizes the balance between historical access speed and memory resource utilization, caching only when it provides measurable benefit while maintaining fast access to frequently needed historical data.
Data Source
AI summary
Methods and apparatus consistent with the invention provide the ability to organize, index, search, and present time series data based on searches. Time series data are sequences of time stamped records occurring in one or more usually continuous streams, representing some type of activity. In one embodiment, time series data is stored as discrete events time stamps. A search is received and relevant event information is retrieved based in whole or in part on the time stamp, a keyword indexing mechanism, or statistical indices calculated at the time of the search.