GOOD: a graph-oriented object database system
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
BitCube: A Three-Dimensional Bitmap Indexing for XML Documents
Journal of Intelligent Information Systems
Dynamic Load Balancing on Web-Server Systems
IEEE Internet Computing
IEEE Internet Computing
Model 204 Architecture and Performance
Proceedings of the 2nd International Workshop on High Performance Transaction Systems
A graph-theoretic data model for genome mapping databases
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Précis: The Essence of a Query Answer
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Dex: high-performance exploration on large graphs for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Survey of graph database models
ACM Computing Surveys (CSUR)
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Matrix "Bit" loaded: a scalable lightweight join query processor for RDF data
Proceedings of the 19th international conference on World wide web
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Database research challenges and opportunities of big graph data
BNCOD'13 Proceedings of the 29th British National conference on Big Data
| Hi-index | 0.00 |
The increasing amount of graph like data from social networks, science and the web has grown an interest in analyzing the relationships between different entities. New specialized solutions in the form of graph databases, which are generic and able to adapt to any schema as an alternative to RDBMS, have appeared to manage attributed multigraphs efficiently. In this paper, we describe the internals of DEX graph database, which is based on a representation of the graph and its attributes as maps and bitmap structures that can be loaded and unloaded efficiently from memory. We also present the internal operations used in DEX to manipulate these structures. We show that by using these structures, DEX scales to graphs with billions of vertices and edges with very limited memory requirements. Finally, we compare our graph-oriented approach to other approaches showing that our system is better suited for out-of-core typical graph-like operations.