A Framework for Building OLAP Cubes on Graphs

Ghrab, Amine; Romero, Óscar; Skhiri, Sabri; Vaisman, Alejandro; Zimányi, Esteban

doi:10.1007/978-3-319-23135-8_7

Cited by 22 publications

(9 citation statements)

References 13 publications

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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%

TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

et al. 2020

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Graphs are a fundamental structure that provides an intuitive abstraction for modeling and analyzing complex and highly interconnected data. Given the potential complexity of such data, some approaches proposed extending decision-support systems with multidimensional analysis capabilities over graphs. In this paper, we introduce TopoGraph, an end-to-end framwork for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints. This results in defining three new types of graph cubes: property graph cubes, topological graph cubes, and graph-structured cubes. Afterwards, we define the algebraic OLAP operations for such novel cubes. We implement and experimentally validate TopoGraph with different types of real-world datasets.

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Section: Related Workmentioning

confidence: 99%

TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

et al. 2020

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“…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%

Graph BI & Analytics: Current State and Future Challenges

Ghrab

Romero

Jouili³

et al. 2018

Big Data Analytics and Knowledge Discovery

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In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.

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“…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%

Functional querying in graph databases

Pokorný

2017

Vietnam J Comput Sci

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The paper is focused on a functional querying in graph databases. We consider labelled property graph model and mention also the graph model behind XML databases. An attention is devoted to functional modelling of graph databases both at a conceptual and data level. The notions of graph conceptual schema and graph database schema are considered. The notion of a typed attribute is used as a basic structure both on the conceptual and database level. As a formal approach to declarative graph database querying a version of typed lambda calculus is used. This approach allows to use a logic necessary for querying, arithmetic as well as aggregation function. Another advantage is the ability to deal with relations and graphs in one integrated environment.

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A Framework for Building OLAP Cubes on Graphs

Cited by 22 publications

References 13 publications

TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

Graph BI & Analytics: Current State and Future Challenges

Functional querying in graph databases

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