Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distin…
Tensors have been applied extensively in engineering and data analytics. Pertinent problems include dimensionality reduction, data mining, data modeling, data learning, etc. Recent research attention has been paid to solve tensor equations. Existing so…
Relation extraction (RE) extracts the semantic relations among entities in a sentence, which converts the unstructured text into structured and easy-to-understand information. Although RE has been studied over decades, it still faces two kinds of resea…
In this article, a novel approach is presented to mitigate the issue of domain shift observed in big-data classification. Since little information is available about the shift, we introduce a ”distortion model”, and obtain additional data…
Networks are known as perfect tools for modeling various types of systems. In the literature of network mining, frequent subgraph mining is considered as the essence of mining network data. In this problem, the dataset is composed of networks represent…
Large scale machine learning (ML) and deep learning (DL) platforms face challenges when integrated with deduplication enabled storage clusters. In the quest to achieve smart and efficient storage utilization, removal of duplicate data introduces bottle…
Outsourcing data to the cloud has become popular due to the big data challenges. However, security concerns compel the outsourced data to be encrypted before sending them to the cloud, which lowers their utility and efficiency. The range query plays a …
In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of…
Driving behaviour understanding is of vital importance in improving transportation safety and promoting the development of Intelligent Transportation Systems (ITS). As a long-standing research topic in driving behaviour analysis, driving style identifi…
Dimensionality reduction is commonly used for identifying and analyzing patterns in the visual analysis of multi-dimensional datasets. The selection of subspaces is a core building block in projecting high-dimensional data to low-dimensional space, whi…