Analysing Big Social Graphs Using a List-based Graph Folding Algorithm

In this paper, we explore the ways to represent big social graphs using adjacency lists and edge lists. Furthermore, we describe a list-based algorithm for graph folding that makes possible to analyze conditionally infinite social graphs on resource constrained mobile devices. The steps of the algorithm are (a) to partition, in a certain way, the graph into clusters of different levels, (b) to represent each cluster of the graph as an edge list, and (c) to absorb the current cluster by the cluster of the next level. The proposed algorithm is illustrated by the example of a sparse social graph

A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools

Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review tool focused on Computer Science, to implement evidence-based systematic review processes in other disciplines.This research was funded by the Spanish Research Agency (Agencia Estatal de Investigacion) with ERDF funds grant number TIN2017-85797-R (VISAIGLE project). The research stay of T. Person in the SFU was funded by Erasmus+ KA107 grant number 2017-1-ES01-KA107-037422. The APC was funded by the VISAIGLE project

Sustainable Smart Cities: Convergence of Artificial Intelligence and Blockchain

Recently, 6G-enabled Internet of Things (IoT) is gaining attention and addressing various challenges of real time application. The artificial intelligence plays a significant role for big data analytics and presents accurate data analysis in real time. However, designing big data analysis through artificial intelligence faces some issues in terms of security, privacy, training data, and centralized architecture. In this article, blockchain-based IoT framework with artificial intelligence is proposed which presents the integration of artificial intelligence and blockchain for IoT applications. The performance of the proposed architecture is evaluated in terms of qualitative and quantitative measurement. For qualitative measurement, how the integration of blockchain and artificial intelligence addresses various issues are described with the description of AI oriented BC and BC oriented AI. The performance evaluation of proposed AI-BC architecture is evaluated and compared with existing techniques in qualitative measurement. The experimental analysis shows that the proposed framework performs better in comparison with the existing state of art techniques

Sustainable Smart Cities: Convergence of Artificial Intelligence and Blockchain

Recently, 6G-enabled Internet of Things (IoT) is gaining attention and addressing various challenges of real time application. The artificial intelligence plays a significant role for big data analytics and presents accurate data analysis in real time. However, designing big data analysis through artificial intelligence faces some issues in terms of security, privacy, training data, and centralized architecture. In this article, blockchain-based IoT framework with artificial intelligence is proposed which presents the integration of artificial intelligence and blockchain for IoT applications. The performance of the proposed architecture is evaluated in terms of qualitative and quantitative measurement. For qualitative measurement, how the integration of blockchain and artificial intelligence addresses various issues are described with the description of AI oriented BC and BC oriented AI. The performance evaluation of proposed AI-BC architecture is evaluated and compared with existing techniques in qualitative measurement. The experimental analysis shows that the proposed framework performs better in comparison with the existing state of art techniques

Analysing Big Social Graphs Using a List-based Graph Folding Algorithm

In this paper, we explore the ways to represent big social graphs using adjacency lists and edge lists. Furthermore, we describe a list-based algorithm for graph folding that makes possible to analyze conditionally infinite social graphs on resource constrained mobile devices. The steps of the algorithm are (a) to partition, in a certain way, the graph into clusters of different levels, (b) to represent each cluster of the graph as an edge list, and (c) to absorb the current cluster by the cluster of the next level. The proposed algorithm is illustrated by the example of a sparse social graph