Sentence examples for fast information retrieval from inspiring English sources

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.

In addition, Big Data problems including semantic indexing, data tagging, and fast information retrieval can be addressed better with the aid of Deep Learning.

Features that users favored include communication with other people, fast information retrieval, linking with other individuals with similar interests and joining a large community of users.

Furthermore, PostgreSQL stores an XML document in its text representation, which results in fast information retrieval and adds flexibility in terms of resources' description by eliminating the need to change tables' schema whenever additional information is added to the document.

In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks.

In these discussions the students had doubts about the usability and efficiency of e-learning (with regard to costs, handling of ICT, but also learning efficiency) while they still acknowledged the possibilities of ICT support with respect to visualization, simulation, self-quizzing, and fast information retrieval from several sources such as encyclopedias or Web pages.

This facilitates faster information retrieval of the complete BIM information building element with its geometry and properties.

While a vector representation of Big Data provides faster information retrieval, Deep Learning can be used for relational understanding of the Big Data.

Therefore, much attention has been given to finding fast information-retrieval algorithms that can deal with the vast amounts of data in the archives.

We use a query-driven approach to specify database structures (schema) so that they are robust to uncertainty and dynamics of queries in a changing environment and allow fast and timely information retrieval and exchange.

While Deep Learning aids in providing a semantic and relational understanding of the data, a vector representation (corresponding to the extracted representations) of data instances would provide faster searching and information retrieval.