XMark: A Benchmark for XML Data Management
By Albrecht Schmidt, F. Waas, Martin Leopold Kersten, Michael J. Carey, Ioana Manolescu, Ralph Busse. Very Large Data Bases Conference, 2002
895 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/185f703c41c4ba86f44f8fd2501901de61032d9f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tim Kraska
- Philippe Cudré-Mauroux
- Peter A. Boncz
- Donald Kossmann
- Djellel Eddine Difallah
- Stefan Manegold
- Raghu Ramakrishnan
- Maurice van Keulen
In Computer Networks and Communications
- Michael Schmidt
- Carlo Curino
- Andrew Pavlo
- Philippe Cudré-Mauroux
- Russell Sears
- Peter A. Boncz
- Donald Kossmann
- Djellel Eddine Difallah
In Signal Processing
- Michael Schmidt
- Peter A. Boncz
- Donald Kossmann
- Carlo Curino
- Stefan Manegold
- Maurice van Keulen
- Tim Kraska
- J. Rittinger
Citations in the corpus
- Benchmarking cloud serving systems with YCSB (2010, background): “It is for similar reasons that new benchmarks have been developed for other non-traditional database systems (such as XMark for XML systems [28] and Linear Road for stream systems [14]).”
- OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases (2013, background): “More recently, the emergence of new application domains has led to a proliferation of new benchmark specifications that target specific subfields, such as XML data stores [31], streaming data [7], key-value stores [16], table stores [24], hybrid OLAP/OLTP databases [18], dynamic websites [28], P2P s”
- SP^2Bench: A SPARQL Performance Benchmark (2008, background): “, XMark [20]) have found broad acceptance, in particular in the research community.”
- ORDPATHs: insert-friendly XML node labels (2004, positive): “For the XMark benchmark [7], which models an auction scenario, we used scale 0.”
- An evaluation of alternative architectures for transaction processing in the cloud (2010, background): “, OO7 for object-oriented databases [6], Bucky for object-relational databases [7], XMark for XML databases [21], and Sequoia for scientific databases [25].”
- MonetDB/XQuery: a fast XQuery processor powered by a relational engine (2006, methodology): “Figure 14 shows that XMark performance on the 110MB document is boosted by a factor 2 using these order optimizations.”