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International Journal of Data Science and Analytics

International Journal of Data Science and Analytics OnlineFirst articles

21.05.2019 | Regular Paper

Causal tree with instrumental variable: an extension of the causal tree framework to irregular assignment mechanisms

This paper provides a link between causal inference and machine learning techniques—specifically, Classification and Regression Trees—in observational studies where the receipt of the treatment is not randomized, but the assignment to the …

15.05.2019 | Regular Paper Open Access

dLSTM: a new approach for anomaly detection using deep learning with delayed prediction

In this paper, we propose delayed Long Short-Term Memory (dLSTM), an anomaly detection method for time-series data. We first build a predictive model from normal (non-anomalous) training data, then perform anomaly detection based on the prediction …

20.04.2019 | Regular Paper

A benchmarking study of classification techniques for behavioral data

The predictive power of increasingly common large-scale, behavioral data has been demonstrated by previous research. Such data capture human behavior through the actions and/or interactions of people. Their sparsity and ultra-high dimensionality …

11.04.2019 | Regular Paper

Resampling-based predictive simulation framework of stochastic diffusion model for identifying top-K influential nodes

We address a problem of efficiently estimating the influence of a node in information diffusion over a social network. Since the information diffusion is a stochastic process, the influence degree of a node is quantified by the expectation, which …

01.04.2019 | Applications

Compressing unstructured mesh data from simulations using machine learning

The amount of data output from a computer simulation has grown to terabytes and petabytes as increasingly complex simulations are being run on massively parallel systems. As we approach exaflop computing in the next decade, it is expected that the …

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Data-driven scientific discovery is a key emerging paradigm driving research innovation and industrial development in domains such as business, social sci­ence, the Internet of Things, and cloud computing. The field encompasses the larger ar­eas of data analytics, machine learning, and managing big data, while related new sci­entific chal­lenges range from data capture, creation, storage, search, sharing, analysis, and vis­ualization, to integration across heterogeneous, interdependent complex resources for real-time decision-making, collaboration, and value creation. The journal welcomes experimental and theoretical findings on data science and advanced analytics along with their applications to real-life situations.

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