On one hand, a water ontology incorporated with Universal Description, Discovery and Integration (UDDI) based enhanced services catalog offers facilities to alleviate semantic heterogeneity and associate semantic information with the web services discovery and data retrieval process.
Cloud storage center (CSC) is a honest but curious data center, who provides information storage and data retrieval service.
In this paper, we first propose to transform the information collection and data retrieval into energy restriction and coverage-related issues.
CSC is deemed as semi-trusted, who is not only honest follow the operations specified by the scheme, but also strive to filch as much as possible information from encrypted EHR content and data retrieval request.
In other words, our digital information ecology is a remix of different forms of technology, devices, data repositories, information retrieval, information sharing, networks and communication.
The hierarchical learning and extraction of different levels of complex, data abstractions in Deep Learning provides a certain degree of simplification for Big Data Analytics tasks, especially for analyzing massive volumes of data, semantic indexing, data tagging, information retrieval, and discriminative tasks such a classification and prediction.
In addition, Big Data problems including semantic indexing, data tagging, and fast information retrieval can be addressed better with the aid of Deep Learning.
More specifically, Big Data problems such as semantic indexing, data tagging, fast information retrieval, and discriminative modeling can be better addressed with the aid of Deep Learning.
Hence, relational algebra (RA) and formal concept analysis (FCA) may be considered as useful mathematical foundations that unify data and knowledge into information retrieval systems.
Relational algebra and formal concept analysis may be considered as useful mathematical foundations that unify data and knowledge in information retrieval systems.
