2015
DOI: 10.1007/978-3-319-23135-8_7
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A Framework for Building OLAP Cubes on Graphs

Abstract: Abstract. Graphs are widespread structures providing a powerful abstraction for modeling networked data. Large and complex graphs have emerged in various domains such as social networks, bioinformatics, and chemical data. However, current warehousing frameworks are not equipped to handle efficiently the multidimensional modeling and analysis of complex graph data. In this paper, we propose a novel framework for building OLAP cubes from graph data and analyzing the graph topological properties. The framework su… Show more

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Cited by 22 publications
(9 citation statements)
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“…However, both GraphCube and Pagrol were still limited to homogeneous graphs. The graph model was later extended with a framework for building OLAP cubes supporting heterogeneous attributed graphs and dimension hierarchies (Ghrab, Romero, Skhiri, Vaisman, & Zimányi, 2015). TSMH framework introduced the concept of relation path to guide the graph aggregation and building two new types of cubes: Entity Hyper Cube and Dimension Cube (Wang, Wu, & Wang, 2015).…”
Section: Related Workmentioning
confidence: 99%
“…However, both GraphCube and Pagrol were still limited to homogeneous graphs. The graph model was later extended with a framework for building OLAP cubes supporting heterogeneous attributed graphs and dimension hierarchies (Ghrab, Romero, Skhiri, Vaisman, & Zimányi, 2015). TSMH framework introduced the concept of relation path to guide the graph aggregation and building two new types of cubes: Entity Hyper Cube and Dimension Cube (Wang, Wu, & Wang, 2015).…”
Section: Related Workmentioning
confidence: 99%
“…Pagrol introduced the notion of Hyper Graph Cubes that extends the model of Graph Cube by in addition considering the attributes of the edges as dimensions, and introduced various optimization techniques for cubes computation and materialization. Ghrab et al [20] extended those models with a framework for building OLAP cubes on heterogeneous attributed graphs. They presented an extension of property graphs tailored for multidimensional analysis and supporting dimension hierarchies.…”
Section: Olap On Graphsmentioning
confidence: 99%
“…Conceptual modelling of graph databases is not used at all. An exception is the GRAD database model [6], which although schema-less, uses conceptual constructs occurring in E-R conceptual model and some powerful ICs. Both graph conceptual schema and graph database schema can provide effective communication medium between users of any GDB.…”
Section: Modelling and Querying Graph Databasesmentioning
confidence: 99%
“…As 6 Graph database schema with properties <!DOCTYPE teachers [ <!ELEMENT teachers(teacher*) <!ELEMENT teacher (T_ID,T_name,birth_year,teaches*,lives_in, is_born_in) <!ELEMENT teaches (day,hour,room,language)> <!ELEMENT language(name,textbook?) 7 DTD for the XML document in Fig.…”
Section: Modelling Graph Data Based On Property Graphsmentioning
confidence: 99%