Big data and its technical challenges

By Hosagrahar Visvesvaraya Jagadish, Johannes Gehrke, Alexandros Labrinidis, Yannis Papakonstantinou, Jignesh M. Patel, Raghu Ramakrishnan, Cyrus Shahabi. CACM, 2014

1,115 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/8e641ea510c06485f33a61ea65555cf791efad2d

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Citations in the corpus

Transformers in Time-Series Analysis: A Tutorial (2022, methodology): “The Trafﬁc Transformer was tested with two real-world benchmark datasets, METR-LA [92] and the California Transportation Agencies Performance Measurement System ([PeMS]).”
Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations (2018, background): “The concept of ILM helps us to understand all the phases of information life cycle in business analytics architecture (Jagadish et al., 2014).”
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting (2020, neutral): “MAE RMSE MAPE(%) MAE RMSE MAPE(%) MAE RMSE MAPE(%) METR-LA [12] PEMS-BAY [4] PEMS07 [4] FC-LSTM [26] 3.”
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting (2022, background): “• METR-LA is a public traffic speed dataset collected from loop-detectors located on the LA County road network [15].”
Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics (2017, background): “The actual challenge of big data is not in collecting it, but in managing it as well as making sense of it [63].”
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (2019, methodology): “We adopt METR-LA dataset [12], which is collected from loop detectors in the highway of Los Angeles County, and then further processed and public by [14].”
Explainable artificial intelligence: a comprehensive review (2021, background): “It is challenging to conceive this type of data, because most of the datasets are complicated, suffer from the high-dimensional space problem (Jagadish et al. 2014), and humans only perceive up to three-dimensional data.”
A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020, background): “The major challenges caused by variety may include data locality, data heterogeneity, dirty and noisy data [54].”
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting (2022, background): “• METR-LA is a traffic speed dataset collected from loopdetectors located on the LA County road network [14].”
Debating big data: A literature review on realizing value from big data (2017, background): “, 2015), or when data analytics is considered a source of competition and sharing endangers the organization’s unique strategic position (Jagadish et al., 2014; Greenaway et al., 2015; Van den Broek and Van Veenstra, 2015).”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “…it is still challenging to quickly extract spatiotemporal patterns from big social data to, for example, help predict criminal activity (Heffner 2014), observe emerging public health threats and provide more effective intervention (Lampos and Cristianini 2010; Jalali, Olabode, and Bell 2012).”
Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, neutral): “We briefly describe them as follows: • METR-LA Thistrafficdatasetcontainshigh-resolutionspatio-temporal transportation data, which includes traffic speed or volume from loop-detectors located on the LA County road network [17].”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “[7] consider analytics to be the core of the Big Data revolution.”
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017, methodology): “4 EXPERIMENTS We conduct experiments on two real-world large-scale datasets: (1) METR-LA This traffic dataset contains traffic information collected from loop detectors in the highway of Los Angeles County (Jagadish et al., 2014).”
Big Data sources and methods for social and economic analyses (2017, background): “…with di ﬀ erent time frequencies), integration of data from di ﬀ erent sources, data matching, bias, availability of tools for properly analyzing such kind of data, processing complexity, privacy and legal issues, and data governance (Fan et al., 2014; Jagadish et al., 2014; Hashem et al., 2015).”