DEX (Graph database)
|Stable release||v5.0 / 2013|
|License||Dual-licensed: personal evaluation use / commercial use|
Its development started in 2006 and its first version was available on Q3 - 2008. The fourth version is available since Q3-2010. There's a free community version, for academic or evaluation purposes, available to download (link web) limited to 1 Million nodes, no limit on edges.
DEX is a product originated by the research carried out at DAMA-UPC (Data Management group at the Polytechnic University of Catalonia). On March 2010 a spin-off called Sparsity-Technologies has been created at the UPC to commercialize and give services to the technologies developed at DAMA-UPC.
DEX changed name to Sparksee on its 5th release on February 2014.
DEX is based on a graph database model, that is basically characterized by three properties: data structures are graphs or any other structure similar to a graph; data manipulation and queries are based on graph-oriented operations; and there are data constraints to guarantee the integrity of the data and its relationships.
A DEX graph is a Labeled Directed Attributed Multigraph. Labeled because nodes and edges in a graph belong to types. Directed because it supports directed edges as well as undirected. Attributed because both nodes and edges may have attributes and Multigraph meaning that there may be multiple edges between the same nodes even if they are from the same edge type.
One of its main characteristics is its performance storage and retrieval for large graphs (in the order of billions of nodes, edges and attributes) implemented with specialized structures.
- Programming Language: C++
- API: Java, .NET, C++, Python
- OS Compatibility: Windows, Linux, Mac OS, iOS, BB10
- Persistency: Disk
- Transactions: aCiD (Dex guarantees consistency and durability but only partial isolation and atomicity)
- Recovery Manager
- Martínez-Bazan, N., Muntés-Mulero, V., Gómez-Villamor, S., Nin, J., Sánchez-Martínez, M., and Larriba-Pey, J. 2007. Dex: high-performance exploration on large graphs for information retrieval. In Proceedings of the Sixteenth ACM Conference on Conference on information and Knowledge Management (Lisbon, Portugal, November 06–10, 2007). CIKM '07. ACM, New York, NY, 573-582.
- R. Angles and C. Gutierrez. Survey of graph database models. Technical Report TR/DCC-2005-10, Computer Science Department, Universidad de Chile, October 2005.
- D. Domínguez-Sal, P. Urbón-Bayes, A.Giménez-Vañó, S. Gómez-Villamor, N.Martínez-Bazán, J.L. Larriba-Pey. Survey of Graph Database Performance on the HPC Scalable Graph Analysis Benchmark. International Workshop on Graph Databases. July 2010.