Object Time Series for Non-Numerical Data Visualization
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Solution Overview
Problem
Traditional time series systems struggle to effectively represent and analyze non-numerical data objects, as they are typically stored as metadata, making it difficult to visualize and manipulate data about objects like events, persons, or documents associated with time series, requiring inefficient referencing and mapping processes.
Innovation Solution
The introduction of Object Time Series, which directly associates time with any type of object, allowing for the storage and visualization of rich information by treating objects as first-class citizens, enabling direct access and visualization of object properties at different points in time, and facilitating efficient slicing and sampling of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-numerical data objects are stored as metadata in traditional time series systems, then data storage is possible, but data accessibility and visualization become difficult requiring inefficient referencing and mapping processes
Solution Approach 1:
The patent merges the time series data structure with object-oriented data models, allowing non-numerical data objects to be stored directly as values in the time series rather than as separate metadata. This integration eliminates the need for complex referencing and mapping between metadata and time series data, directly improving data accessibility while reducing system complexity.
Solution Approach 2:
The patent creates a universal time series data structure that can handle both numerical and non-numerical data types uniformly. By making the time series system multi-functional to accommodate diverse data types (events, persons, documents, etc.) as first-class citizens, it eliminates the need for special metadata handling and simplifies data access operations.
2Adaptability or versatility
If traditional time series systems store only numerical data, then data processing is efficient, but the ability to represent and analyze non-numerical data objects is limited
Solution Approach 1:
The patent fundamentally changes the data type parameter of the time series system from exclusively numerical to accepting any data type including non-numerical objects. This parameter change enables the system to represent diverse data types (events, persons, documents) while maintaining processing efficiency through direct object storage and access without metadata conversion.
3Ease of operation
If non-numerical data objects are stored as metadata, then storage is possible, but visualization and manipulation of object data over time becomes inefficient
Solution Approach 1:
By merging object data directly into the time series values, the patent enables direct manipulation and visualization of non-numerical data objects over time. This eliminates the time-consuming process of retrieving and mapping metadata to time series data, as objects are stored and accessed directly in their temporal context.
Data Source
AI summary
Systems and methods are presented for representing non-numerical data objects in an object time series. An object time series of can be created by establishing one or more associations, each association including a mapping of at least one point in time with one or more objects that include properties and values. Visual representation of an object time series may include displaying non-numerical values associated with objects in the object time series in association with respective points in time.