Concurrent Forecasting Objects for Time Series Analysis
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Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources in data centers is challenging due to the vast amounts of different types and formats of data, which can be time-consuming and requires efficient processing techniques to extract valuable insights.
Innovation Solution
The implementation of a data intake and query system, such as the SPLUNK® ENTERPRISE system, that uses event-based processing, late-binding schema, and parallel query execution across multiple indexers to facilitate fast keyword searching and report generation, enabling the storage and analysis of minimally processed machine data for later retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used to analyze massive quantities of machine-generated data from diverse sources, then data storage and retrieval are possible, but the analysis process becomes time-consuming and inefficient
Solution Approach 1:
The patent segments the data processing system into multiple independent indexers that can process different portions of data in parallel. Each indexer maintains its own data structures and can independently execute search operations, allowing the system to divide the large-scale data analysis task into smaller, concurrent sub-tasks that complete faster
Solution Approach 2:
The patent performs preliminary actions by pre-processing and indexing machine data before it is needed for analysis. Data is collected, parsed, and indexed in advance using event-based processing, so that when queries are executed, the system can retrieve and analyze pre-processed data rather than processing raw data in real-time
2Measurement precision
If multiple time series data sets are analyzed sequentially, then each data set can be processed thoroughly, but the overall analysis time increases significantly
Solution Approach 1:
The patent creates separate analysis objects for each time series data set, allowing each to be processed independently and in parallel. This segmentation enables multiple predictive analyses to occur simultaneously without interfering with each other, maintaining accuracy while reducing total execution time
Solution Approach 2:
The patent implements continuous concurrent execution of multiple analysis objects, where each object continuously processes its assigned time series data set. Rather than completing one analysis before starting the next, the system maintains continuous useful action across all data sets simultaneously through parallel object execution
3Adaptability or versatility
If massive quantities of machine data are stored for later retrieval and analysis, then data availability is improved, but the complexity of processing and searching the data increases
Solution Approach 1:
The patent introduces an event-based processing intermediary layer that sits between data storage and analysis operations. This intermediary layer includes event parsers and indexers that translate raw machine data into a standardized event format with consistent schemas, making the data more accessible and easier to query without increasing the complexity of the underlying storage system
Data Source
AI summary
Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.