Event prediction in the big data era: A systematic survey
L Zhao - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
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 …
either our society or the nature, such as earthquakes, civil unrest, system failures …
Tensor-based anomaly detection: An interdisciplinary survey
H Fanaee-T, J Gama - Knowledge-based systems, 2016 - Elsevier
Traditional spectral-based methods such as PCA are popular for anomaly detection in a
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …
Tensor completion algorithms in big data analytics
Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …
observed tensors. Due to the multidimensional character of tensors in describing complex …
DeepCrime: Attentive hierarchical recurrent networks for crime prediction
As urban crimes (eg, burglary and robbery) negatively impact our everyday life and must be
addressed in a timely manner, predicting crime occurrences is of great importance for public …
addressed in a timely manner, predicting crime occurrences is of great importance for public …
Autoplait: Automatic mining of co-evolving time sequences
Given a large collection of co-evolving multiple time-series, which contains an unknown
number of patterns of different durations, how can we efficiently and effectively find typical …
number of patterns of different durations, how can we efficiently and effectively find typical …
FUNNEL: automatic mining of spatially coevolving epidemics
Given a large collection of epidemiological data consisting of the count of d contagious
diseases for l locations of duration n, how can we find patterns, rules and outliers? For …
diseases for l locations of duration n, how can we find patterns, rules and outliers? For …
Forecasting big time series: old and new
Time series forecasting is a key ingredient in the automation and optimization of business
processes: in retail, deciding which products to order and where to store them depends on …
processes: in retail, deciding which products to order and where to store them depends on …
Finding progression stages in time-evolving event sequences
Event sequences, such as patients' medical histories or users' sequences of product
reviews, trace how individuals progress over time. Identifying common patterns, or …
reviews, trace how individuals progress over time. Identifying common patterns, or …
Time-aware service recommendation for mashup creation
Web service recommendation has become a critical problem as services become
increasingly prevalent on the Internet. Some existing methods focus on content matching …
increasingly prevalent on the Internet. Some existing methods focus on content matching …
CRNet: modeling concurrent events over temporal knowledge graph
S Wang, X Cai, Y Zhang, X Yuan - International Semantic Web …, 2022 - Springer
Temporal knowledge graph (TKG) reasoning, which aims to extrapolate missing facts in
TKGs, is vital for many significant applications, such as event prediction. Previous studies …
TKGs, is vital for many significant applications, such as event prediction. Previous studies …