A Distributed System for Answering Range Queries on Sensor Network Data

Cuzzocrea, Alfredo; Furfaro, Filippo; Greco, Sergio; Masciari, Elio; Mazzeo, Giuseppe M.; Saccà, Domenico

doi:10.1109/percomw.2005.5

Cited by 45 publications

(33 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is used with predilection in the situation where data-analytics-based applications require a large number of computations have to be executed. Similar motivations can be found in related research efforts focused on the issue of effectively and efficiently managing large-scale data sets over Grid environments (e.g., [19][20][21]). …”

Section: Hadoop: An Overviewmentioning

confidence: 66%

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

2016

View full text Add to dashboard Cite

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.

show abstract

Section: Hadoop: An Overviewmentioning

confidence: 66%

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

2016

View full text Add to dashboard Cite

show abstract

“…Examples include an integration architecture of Cloud computing and WSNs [8], SensorWeb [9], SensorGrid [10], [11], [12], the Sensor-Cloud infrastructure [13], the BodyCloud architecture [16] and use of wireless sensors in buildings [17] etc.…”

Section: Related Workmentioning

confidence: 99%

“…Aggregate queries are the basis for achieving Online Analytical Processing (OLAP) over sensor network readings in Data Grid environments. OLAP has a number of interesting applications for eScience, covering aspects such as visualization of scientific data, multi-dimensional analysis of data streams, privacy of multi-dimensional data [10], [11], [12], etc.…”

Section: Related Workmentioning

confidence: 99%

Cloud Supported Building Data Analytics

Petri

Rana

Rezgui

et al. 2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

View full text Add to dashboard Cite

Abstract-With increasing availability of instrumented infrastructures in built environments, it is necessary to understand how such data will be stored, processed and analysed in a timely manner. Many "smart cities" applications, for instance, identify how data from building sensors can be combined together to support applications such as emergency response, energy management, etc. Enabling sensor data to be transmitted to a Cloud environment for processing provides a number of benefits, such as scalability and elastic provisioning of computational resources -as the total data size may not be known apriori. In this application-based case study, we describe the integration of an in-building sensor network (both for sensing and actuation) with a distributed Cloud environment. Energy optimisation in buildings represents a class of problems that requires significant computational resources and generally is a time consuming process. We describe the use of Cloud computing for efficiently running and deploying EnergyPlus simulations with sensor data in order to fulfil a number of energy related objectives for buildings. We describe and evaluate the establishment of such a sensor based application using a CometCloud implementation with data collection from a real building pilot. Although our focus is on a single application, the general architecture and analysis carried out can be generalised to other similar scenarios.

show abstract

“…This scheme finally allows us to efficiently support real-time Grid DT services over Grid-based RTSOA frameworks. As we focus on Grid-based RTSOA frameworks in GDWH scenarios whose Grid nodes are populated by multidimensional data cubes, compressed versions of these data cubes and their replicas (like in SensorGrid [13,14] for the case of sensor network readings high-performance management), the main intuition underlying RGDTExec consists in exploiting the data compression/approximation paradigm in support of the greedy criterion. We next describe in detail how RGDTExec works.…”

Section: Complexity Analysismentioning

confidence: 99%

“…The latter can be reasonably considered as an emerging GDWH scenario suitable for a wide spectrum of data-intensive e-science Grid applications focused on analyzing and mining multidimensional data, such as those ranging from scientific and statistical data sets to biological data sets and sensor network data repositories. On the basis of this application scenario, the set of services S(G) available in G encompasses, as a consequence, specific DWoriented DT services, such as data transportation among Grid nodes by means of range queries over data cubes [51], or materialization of snapshot OLAP views in remote Grid nodes for query and data management efficiency purposes [13,14].…”

mentioning

confidence: 99%

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

Cuzzocrea

2010

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SUMMARYData and Knowledge Grids represent emerging and attracting application scenarios for Grid Computing, and pose novel and previously unrecognized challenges to the research community. Basically, Data and Knowledge Grids are found on high-performance Grid infrastructures, and add to the latter meaningful data-and knowledge-oriented abstractions and metaphors that perfectly marry with innovative requirements of modern complex Intelligent Information Systems. To this end, Service-oriented Architectures and Paradigms are the most popular for Grids, and on the whole represent an active and widely recognized area of Grid Computing research. In this paper, we introduce the so-called Grid-based RTSOA frameworks, which essentially combine Grid Computing with real-time service management and execution paradigms, and place emphasis for novel research perspectives in data-intensive e-science Grid applications on realtime bound constraints. Grid-based RTSOA frameworks are then specialized to the particular context of Data Transformation services over Grids, which play a relevant role for both Data and Knowledge Grids. Finally, we complete the main contribution of this paper with a rigorous theoretical model for efficiently supporting Grid-based RTSOA frameworks, with particular emphasis on the context of Data Transformation services over Grids, along with its comprehensive experimental assessment and analysis.

show abstract

A Distributed System for Answering Range Queries on Sensor Network Data

Cited by 45 publications

References 8 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

Cloud Supported Building Data Analytics

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

Contact Info

Product

Resources

About