Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn...
Transforming relational data into XML, as known as XML publishing, is often necessary when one wants to exchange data residing in databases or to create an XML interface of a tradi...
Map-Reduce is a programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. Through ...
Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, Dougl...
In response to the widespread use of the XML format for document representation and message exchange, major database vendors support XML in terms of persistence, querying and inde...
We enunciate the need for watermarking database relations to deter data piracy, identify the characteristics of relational data that pose unique challenges for watermarking, and de...
This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure th...
Relational data appear frequently in many machine learning applications. Relational data consist of the pairwise relations (similarities or dissimilarities) between each pair of i...
Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip...
Relational database management systems are constantly being extended and augmented to accommodate data in different domains. Recently, with the increasing use of ontology in vario...
Annotation is the process of supplementing data with additional information that was not part of the actual observation, but reflects post-facto comments and associations made by a...