Combining Time-Series and Graph Data: A Survey of Existing Systems and Approaches
🤖 AI Summary
This work addresses the critical need for unified analysis of graph and time series data, an area currently lacking systematic organization in existing systems. It proposes the first comprehensive taxonomy that categorizes fusion architectures into four distinct classes. Through a multidimensional evaluation grounded in cross-model integration depth, maturity, and openness, the study establishes a clear classification framework via literature review, architectural analysis, and requirement mapping. This framework delineates the appropriate application scenarios and inherent design trade-offs for each architecture type, thereby offering researchers and practitioners a principled guide for system selection and identifying promising directions for future research.
Application Category
📝 Abstract
We provide a comprehensive overview of current approaches and systems for combining graphs and time series data. We categorize existing systems into four architectural categories and analyze how these systems meet different requirements and exhibit distinct implementation characteristics to support both data types in a unified manner. Our overview aims to help readers understand and evaluate current options and trade-offs, such as the degree of cross-model integration, maturity, and openness.
Problem
Research questions and friction points this paper is trying to address.
graph data
time series
data integration
cross-model integration
Innovation
Methods, ideas, or system contributions that make the work stand out.
time-series
graph data
cross-model integration
system architecture
data fusion
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