Event Sequence Indexing for Time-Constrained Query Visualization
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Solution Overview
Problem
Existing contact center systems face high computational complexity and scalability issues in filtering and querying large numbers of events or event sequences, leading to inefficient and time-consuming query operations.
Innovation Solution
Implementing optimized indices and data structures, such as B-trees, to facilitate time-constrained queries for real-time event sequence visualization, allowing for efficient filtering and querying of event sequences by leveraging forward search indices and time constraint search structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional filtering and querying methods are used on large event sequence datasets, then query operations become increasingly time-consuming and computationally complex, but the system lacks the necessary efficiency for real-time visualization
Solution Approach 1:
The patent applies preliminary action by pre-processing event sequence data during ingestion to build optimized data structures (forward search index, time constraint search structure, pattern occurrence data) that enable efficient querying. This pre-computation of indices and patterns allows the system to rapidly respond to visualization queries without performing complex filtering operations at query time, thus resolving the contradiction between query efficiency and execution time.
2Reliability
If the system processes and stores detailed event sequence data for comprehensive analysis, then the computational complexity and resource requirements increase, but the system becomes less scalable
Solution Approach 1:
The patent applies segmentation by dividing the event sequence data into distinct components: individual events, event patterns, sequences, and their relationships. The system creates separate data structures for each component (event tables, pattern occurrence data, forward search index, time constraint search structure), allowing independent processing and optimization of each segment. This segmentation reduces overall computational complexity while maintaining the ability to perform comprehensive analysis by recombining these segmented components as needed.
Data Source
AI summary
A method for performing time constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. Each event sequence includes at least two sequentially occurring events. The method further includes generating a forward index having event pattern sets that include pattern occurrence data for each event sequence positioned according to a fixed positional order. The method includes generating a time constraint search structure, such as a B−tree, B+tree, or table, that includes keys and pointer values. The method also includes inserting, into the search structure, a sequence identifier corresponding to each event sequence based on the fixed positional order and time interval data for the at least two sequentially occurring events of each event sequence. The method may also include searching the search structure to identify key values satisfying a received time interval constraint, and outputting the sequence identifiers corresponding to the identified key values.