The hypernode model and its associated query language
JCIT Proceedings of the fifth Jerusalem conference on Information technology
An object-oriented data model formalised through hypergraphs
Data & Knowledge Engineering
Gram: a graph data model and query languages
ECHT '92 Proceedings of the ACM conference on Hypertext
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A graph-oriented object database model
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Typing Graph-Manipulation Operations
ICDT '03 Proceedings of the 9th International Conference on Database Theory
GraphDB: Modeling and Querying Graphs in Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Graph Query Language and Its Query Processing
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A query language for biological networks
Bioinformatics
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)
Graphs-at-a-time: query language and access methods for graph databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A query language for analyzing networks
Proceedings of the 18th ACM conference on Information and knowledge management
MTCProv: a practical provenance query framework for many-task scientific computing
Distributed and Parallel Databases
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An important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for instance, simple statistics such as the average number of links between two types of entities (relations) are easily computed using a query on a relational database and may already provide valuable information. However, for the exploration of graph data, relational databases may not be most practical and scalable. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, however, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. An important component of such databases is the language that is used to enable aggregating queries, such as shortest path queries. In this paper, we propose an extension to a previously proposed query language. This extension allows for querying and analyzing databases by using aggregates and ranking. A notable feature of our language is that it also supports probabilistic graph queries by conceiving of such queries as aggregating queries. We demonstrate its value on a simple data analysis task.