Managing and efficiently analysing the vast amounts of data produced by a huge variety of data sources is one of the big challenges in computer science. The development and implementation of algorithms and applications that can extract information diamonds from these ultra-large, and often distributed, databases is a key challenge for the design of future data management infrastructures. Today’s data-intensive applications often suffer from performance problems and an inability to scale to high numbers of distributed data sources. Therefore, distributed and parallel databases have a key part to play in overcoming resource bottlenecks, achieving guaranteed quality of service and providing system scalability. The increased availability of distributed architectures, clusters, Grids and P2P systems, supported by high performance networks and intelligent middleware provides parallel and distributed databases and digital repositories with a great opportunity to cost-effectively support key everyday applications. Further, there is the prospect of data mining and knowledge discovery tools adding value to these vast new data resources by automatically extracting useful information from them.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:86469 |
| Date | 17 July 2023 |
| Creators | Valduriez, Patrick, Lehner, Wolfgang, Talia, Domenico, Watson, Paul |
| Publisher | Springer |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-540-37784-9, 10.1007/11823285_34 |
Page generated in 0.0025 seconds