What does the summarization process in streaming data aim to achieve?

The summarization process in streaming data primarily aims to provide a representation that clearly describes the time series data. This process is essential for making sense of continuous streams of information, which can be overwhelming in terms of volume and velocity. By summarizing this data, analysts can identify trends, patterns, and anomalies over time without needing to dive into the raw data points.

Effective summarization helps in highlighting significant features of the data, such as maxima, minima, averages, and other statistical summaries that provide quick insights into the behavior of the data over time. This simplified representation is valuable for further analysis and decision-making, especially in real-time applications such as monitoring system performance or detecting events.

The other choices, while they may have relevance in broader contexts of data processing or analysis, are not the primary objective of the summarization process. Creating visual graphics focuses on the presentation aspect, extracting insights is a broader goal that can be achieved through various methods, and compressing data is more about efficiency in storage rather than providing a clear representation of time series data.