Event Prediction Based on Causality Reasoning

Lei, Lei; Ren, Xuguang; Franciscus, Nigel; Wang, Junhu; Stantić, Bela

doi:10.1007/978-3-030-14799-0_14

Cited by 12 publications

(10 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other measurements such as the area under the ROC curves are also commonly used [20]. This approach can be extended to include other items such as multi-class precision/recall, and Precision/Recall at Top K [2,86,105,213].…”

Section: Metrics Of Effectivenessmentioning

confidence: 99%

“…This requires the similarity between the query event and all the historical events to be calculated. To achieve this, Lei et al [105] utilized context information, including event time, location, and other environmental and descriptive information. For methods requiring event generalization, the first step is to traverse the abstraction tree starting from the root that corresponds to the most general event rule.…”

Section: Causality-based Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Event Prediction in the Big Data Era

Zhao

2021

ACM Comput. Surv.

View full text Add to dashboard Cite

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.

show abstract

Section: Metrics Of Effectivenessmentioning

confidence: 99%

Section: Causality-based Predictionmentioning

confidence: 99%

Event Prediction in the Big Data Era

Zhao

2021

ACM Comput. Surv.

View full text Add to dashboard Cite

show abstract

“…This requires the similarity between the query event and all the historical events to be calculated. To achieve this, Lei et al [123] utilized context information, including event time, location, and other environmental and descriptive information. For methods requiring event generalization, the first step is to traverse the abstraction tree starting from the root that corresponds to the most general event rule.…”

Section: Semantic Predictionmentioning

confidence: 99%

“…To achieve this, they usually leverage the immense historical news and knowledge base in order to learn the association and causality among events, which is then applied to forecast events when given current events. Some studies have even directly generated textual descriptions of future events by leveraging NLP techniques such as sequence to sequence learning [57,97,123,153,168,170,190,195,221].…”

Section: 2mentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

View full text Add to dashboard Cite

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

show abstract

“…Other measurements such as the area under the ROC curves are also commonly used [26]. This approach can be extended to include other items such as multi-class precision/recall, and Precision/Recall at Top K [2,103,123,252]. • Quality of Matched Predictions.…”

Section: Event Prediction Evaluationmentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

View full text Add to dashboard Cite

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

show abstract

Event Prediction Based on Causality Reasoning

Cited by 12 publications

References 25 publications

Event Prediction in the Big Data Era

Event Prediction in the Big Data Era

Event Prediction in Big Data Era: A Systematic Survey

Event Prediction in Big Data Era: A Systematic Survey

Contact Info

Product

Resources

About