Summary of Event Prediction in the Big Data Era: a Systematic Survey, by Liang Zhao
Event Prediction in the Big Data Era: A Systematic Survey
by Liang Zhao
First submitted to arxiv on: 19 Jul 2020
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents a comprehensive survey on event prediction technologies, applications, and evaluations in the big data era. Event prediction is crucial for anticipating occurrences that significantly impact society or nature, such as civil unrest, system failures, and epidemics. The paper categorizes existing techniques for event prediction, providing a systematic overview of methods addressing heterogeneous outputs, complex dependencies, and streaming data feeds. It also summarizes major application domains, evaluation metrics, and procedures to unify understanding among stakeholders. This study aims to facilitate domain experts’ searches for suitable techniques and help model developers consolidate research at the frontiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine being able to predict big events like natural disasters or social unrest before they happen. This paper looks at how scientists use data to forecast these kinds of events. They want to know what works best in different situations and how to measure the success of their predictions. The study brings together all the current knowledge on event prediction, making it easier for experts to find the right tools and techniques. |
