Search results
Results from the WOW.Com Content Network
A very large database, (originally written very large data base) or VLDB, [1] is a database that contains a very large amount of data, so much that it can require specialized architectural, management, processing and maintenance methodologies.
XLDB (eXtremely Large DataBases) was a yearly conference about databases, data management and analytics held from 2007 to 2019. The definition of extremely large refers to data sets that are too big in terms of volume (too much), and/or velocity (too fast), and/or variety (too many places, too many formats) to be handled using conventional solutions.
International Conference on Very Large Data Bases or VLDB conference is an annual conference held by the non-profit Very Large Data Base Endowment Inc. While named after very large databases, the conference covers the research and development results in the broader field of database management. The mission of VLDB Endowment is to "promote and ...
Big data very often means 'dirty data' and the fraction of data inaccuracies increases with data volume growth." Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed. [ 85 ]
Very large database (VLDB) – contains an extremely high number of tuples (database rows), or occupies an extremely large physical filesystem storage space. Virtual private database (VPD) – masks data in a larger database so that security allows only the use of apparently private data.
Very large databases; VLDB conference, an annual database conference This page was last edited on 31 July 2019, at 16:19 (UTC). Text is available under the ...
ISBL is a query language for PRTV, one of the earliest relational database management systems; Jaql is a functional data processing and query language most commonly used for JSON query processing; jq is a functional programming language often used for processing queries against one or more JSON documents, including very large ones;
DAI systems do not require all the relevant data to be aggregated in a single location, in contrast to monolithic or centralized Artificial Intelligence systems which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large datasets.