An Analytics-Aware Conceptual Model for Evolving Graphs

Ghrab, Amine; Skhiri, Sabri; Jouili, Salim; Zimányi, Esteban

doi:10.1007/978-3-642-40131-2_1

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…To facilitate the use of various types of data and exploit its structure, we use a graphical representation that serves as a unified model for all the data used by a Saul program. We represent the data using two different but related graphs: data graph and model graph [Ghrab et al , 2013]. …”

Section: Data and Knowledge Representationmentioning

confidence: 99%

Systems AI: A Declarative Learning Based Programming Perspective

Kordjamshidi

Roth

Kersting

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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We present Saul, a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems. Such languages need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piecewise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints. We describe the key constructs of Saul and exemplify its use in developing applications that require relational feature engineering and structured output prediction.

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Section: Data and Knowledge Representationmentioning

confidence: 99%

Systems AI: A Declarative Learning Based Programming Perspective

Kordjamshidi

Roth

Kersting

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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“…In [6,7], we proposed GRAD, an analysis-oriented graph database model that extends property graphs with advanced graph structures, integrity constraints and a graph algebra. We use GRAD as the foundation for the OLAP cubes extraction techniques we present in this section.…”

Section: Building Olap Cubes On Gradmentioning

confidence: 99%

“…GraphQL [12]) were developed to efficiently answer graph-oriented queries. In this paper, we leveraged and extended the database model we defined on [7] for graph data warehousing.…”

Section: Related Workmentioning

confidence: 99%

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A Framework for Building OLAP Cubes on Graphs

Ghrab

Romero

Skhiri

et al. 2015

Advances in Databases and Information Systems

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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 supports the extraction and design of the candidate multidimensional spaces in property graphs. Besides property graphs, a new database model tailored for multidimensional modeling and enabling the exploration of additional candidate multidimensional spaces is introduced. We present novel techniques for OLAP aggregation of the graph, and discuss the case of dimension hierarchies in graphs. Furthermore, the architecture and the implementation of our graph warehousing framework are presented and show the effectiveness of our approach.

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“…Chronos [25,37] is an in-memory system for processing dynamic graphs, with objective of shared storage and computation for overlapping snapshots. Ghrab et al [19] provide a system of network analytics through labeling graph components. Gedik et al [17], describe a block-oriented and cache-enabled system to exploit spatio-temporal locality for solving temporal neighborhood queries.…”

Section: Related Workmentioning

confidence: 99%

Storing and Analyzing Historical Graph Data at Scale

Khurana,

Deshpande

2015

Preprint

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The work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. However, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, information diffusion, formation of online communities, and so on. Being able to find temporal interaction patterns, visualize the evolution of graph properties, or even simply compare them across time, adds significant value in reasoning over graphs. However, because of lack of underlying data management support, an analyst today has to manually navigate the added temporal complexity of dealing with large evolving graphs. In this paper, we present a system, called Historical Graph Store, that enables users to store large volumes of historical graph data and to express and run complex temporal graph analytical tasks against that data. It consists of two key components: a Temporal Graph Index (TGI), that compactly stores large volumes of historical graph evolution data in a partitioned and distributed fashion; it provides support for retrieving snapshots of the graph as of any timepoint in the past or evolution histories of individual nodes or neighborhoods; and a Spark-based Temporal Graph Analysis Framework (TAF), for expressing complex temporal analytical tasks and for executing them in an efficient and scalable manner. Our experiments demonstrate our system's efficient storage, retrieval and analytics across a wide variety of queries on large volumes of historical graph data.

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An Analytics-Aware Conceptual Model for Evolving Graphs

Cited by 10 publications

References 13 publications

Systems AI: A Declarative Learning Based Programming Perspective

Systems AI: A Declarative Learning Based Programming Perspective

A Framework for Building OLAP Cubes on Graphs

Storing and Analyzing Historical Graph Data at Scale

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