A framework for modeling and supporting data transformation services over data and knowledge grids with real‐time bound constraints

Cuzzocrea, Alfredo

doi:10.1002/cpe.1648

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…Future work is oriented towards the integration of our algorithms with Cloud-inspired frameworks devoted to the distributed management of large-scale data repositories via data compression metaphors (e.g., [82,83]). …”

Section: Discussionmentioning

confidence: 99%

An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs

2016

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Nowadays, a leading instance of big data is represented by Web data that lead to the definition of so-called big Web data. Indeed, extending beyond to a large number of critical applications (e.g., Web advertisement), these data expose several characteristics that clearly adhere to the well-known 3V properties (i.e., volume, velocity, variety). Resource Description Framework (RDF) is a significant formalism and language for the so-called Semantic Web, due to the fact that a very wide family of Web entities can be naturally modeled in a graph-shaped manner. In this context, RDF graphs play a first-class role, because they are widely used in the context of modern Web applications and systems, including the emerging context of social networks. When RDF graphs are defined on top of big (Web) data, they lead to the so-called large-scale RDF graphs, which reasonably populate the next-generation Semantic Web. In order to process such kind of big data, MapReduce, an open source computational framework specifically tailored to big data processing, has emerged during the last years as the reference implementation for this critical setting. In line with this trend, in this paper, we present an approach for efficiently implementing traversals of large-scale RDF graphs over MapReduce that is based on the Breadth First Search (BFS) strategy for visiting (RDF) graphs to be decomposed and processed according to the MapReduce framework. We demonstrate how such implementation speeds-up the analysis of RDF graphs with respect to competitor approaches. Experimental results clearly support our contributions.

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Section: Discussionmentioning

confidence: 99%

An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs

2016

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“…Future work is oriented towards the integration of our algorithms with Cloud-inspired frameworks devoted to the distributed management of large-scale data repositories (e.g., [17], [18]). …”

Section: Discussionmentioning

confidence: 99%

Implementing BFS-based Traversals of RDF Graphs over MapReduce Efficiently

Coşulschi

Cuzzocrea

Virgilio

2013

2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing

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Big data describes data sets that grow so large that they become unpractical to be processed by traditional tools like database management systems, content management systems, advanced statistical analysis software, and so forth. The reason why they came into the attention of the research community is that the infrastructure to handle these data sets has become more affordable due to Cloud Computing and MapReduce based open-source frameworks. Moreover the effectiveness of analysis on such data sets is supported by Semantic Web technologies, which employ the Resource Description Framework (RDF) model to represent data via a graph-shaped representation.In this paper we present an approach for efficiently implementing traversals of RDF graphs over MapReduce that is based on the Breadth First Search (BFS) strategy for visiting (RDF) graphs to be decomposed and processed according to the MapReduce framework. We demonstrate how such implementation speedsup the analysis of RDF graphs with respect to competitor approaches. Experimental results clearly support our contribution. 1

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“…Future work is mainly oriented toward making our proposed design methodology capable of dealing with next-generation Grid Data Warehouse Environments (Cuzzocrea, 2011;Cuzzocrea et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Effectively and Efficiently Designing and Querying Parallel Relational Data Warehouses on Heterogeneous Database Clusters

Bellatreche

Cuzzocrea

Benkrid

2012

Journal of Database Management

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In this paper, a comprehensive methodology for designing and querying Parallel Rational Data Warehouses (PRDW) over database clusters, called Fragmentation & Allocation (F&A) is proposed. F&A assumes that cluster nodes are heterogeneous in processing power and storage capacity, contrary to traditional design approaches that assume that cluster nodes are instead homogeneous, and fragmentation and allocation phases are performed in a simultaneous manner. In classical approaches, two different cost models are used to perform fragmentation and allocation, separately, whereas F&A makes use of one cost model that considers fragmentation and allocation parameters simultaneously. Therefore, according to the F&A methodology proposed, the allocation phase/decision is done at fragmentation. At the fragmentation phase, F&A uses two well-known algorithms, namely Hill Climbing (HC) and Genetic Algorithm (GA), which the authors adapt to the main PRDW design problem over heterogeneous database clusters, as these algorithms are capable of taking into account the heterogeneous characteristics of the reference application scenario. At the allocation phase, F&A introduces an innovative matrix-based formalism capable of capturing the interactions among fragments, input queries, and cluster node characteristics, driving the data allocation task accordingly, and a related affinity-based algorithm, called F&A-ALLOC. Finally, their proposal is experimentally assessed and validated against the widely-known data warehouse benchmark APB-1 release II.

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A framework for modeling and supporting data transformation services over data and knowledge grids with real‐time bound constraints

Cited by 9 publications

References 40 publications

An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs

An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs

Implementing BFS-based Traversals of RDF Graphs over MapReduce Efficiently

Effectively and Efficiently Designing and Querying Parallel Relational Data Warehouses on Heterogeneous Database Clusters

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