Sentence examples for textual database from inspiring English sources

It would plug into a prodigious textual database, enabling the user to generate, on demand, erudite references to centuries of place-specific fact and fiction.

Database tomography (DT) is a textual database analysis system consisting of two major components: (1) algorithms for extracting multiword phrase frequencies and phrase proximities (physical closeness of the multiword technical phrases) from any type of large textual database, to augment (2) interpretative capabilities of the expert human analyst.

DT has been used to derive technical intelligence from a variety of textual database sources, most recently the published technical literature as exemplified by the Science Citation Index (SCI) and the Engineering Compendex (EC).

We also highlight some of the difficulties faced when analyzing textual databases.

Due to the fact that unanticipated information plays a more and more dominant role, especially in highly innovative business processes, we focus our attention on textual databases since textual databases offer the best possibility to handle unanticipated free-formatted information.

In this paper we present the adaptation of a compression technique, specially designed to compress large textual databases, to the peculiarities of web search engines.

Further, an analysis tool, data mining that could be used to analyse these textual databases so that we could extract information from them quickly and hence be able to access them at the right time when needed, is presented.

Thus with the right information from the textual databases and the tools to deliver this information at the right time companies, would definitely be able to shorten development times and hence gain a competitive edge.

Therefore, we applied Knowledge Discovery in the Textual Databases (KDT) process to answer the questions, "what kinds of defects are reported while inspecting a marine structure, and which of them are closely related?" In particular, we propose a concept extraction and linkage approach as an "add-on" module for the Self-Organizing Map (SOM), a clustering algorithm for document organization.

They'll have to scan enormous databases in milliseconds, textual databases as well as structural databases.

In the paper we present the potential of the combination between new algorithms applied to large scale textual databases and the use of domain knowledge derived from engineering and physics.