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WebPIE: A Web-scale parallel inference engine using
"... The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. In th ..."
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through a set of algorithms which, combined, significantly increase performance. We have implemented WebPIE (Web-scale Inference Engine) and we demonstrate its performance on a cluster of up to 64 nodes. We have evaluated our system using very large real-world datasets (Bio2RDF, LLD, LDSR) and the LUBM
WebPIE: A Web-scale parallel inference engine using
"... The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. In th ..."
Abstract
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through a set of algorithms which, combined, significantly increase performance. We have implemented WebPIE (Web-scale Inference En-gine) and we demonstrate its performance on a cluster of up to 64 nodes. We have evaluated our system using very large real-world datasets (Bio2RDF, LLD, LDSR) and the LUBM
H.: WebPIE: A Web-Scale Parallel Inference Engine
- In: Third IEEE International Scalable Computing Challenge (SCALE2010), held in conjunction with the 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid
, 2010
"... The Semantic Web [1] extends the World Wide Web by providing well-defined semantics to information and services. Through these semantics machines can “understand ” the Web, making it possible to query and reason over Web information, treating the Web as if it were a giant semi-structured database. ..."
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Cited by 32 (5 self)
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The Semantic Web [1] extends the World Wide Web by providing well-defined semantics to information and services. Through these semantics machines can “understand ” the Web, making it possible to query and reason over Web information, treating the Web as if it were a giant semi-structured database.
OWL reasoning with WebPIE: calculating the closure of 100 billion triples
"... Abstract. In previous work we have shown that the MapReduce framework for distributed computation can be deployed for highly scalable inference over RDF graphs under the RDF Schema semantics. Unfortunately, several key optimizations that enabled the scalable RDFS inference do not generalize to the r ..."
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Cited by 63 (8 self)
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to the richer OWL semantics. In this paper we analyze these problems, and we propose solutions to overcome them. Our solutions allow distributed computation of the closure of an RDF graph under the OWL Horst semantics. We demonstrate the WebPIE inference engine, built on top of the Hadoop platform and deployed
Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce
"... The proliferation of GPS-enabled devices, and the rapid improve-ment of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a ..."
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Cited by 2 (1 self)
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will demonstrate how spatial queries are ex-pressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our sys-tem demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized
Large Scale Fuzzy pD ∗ Reasoning Using
- MapReduce’, in International Semantic Web Conference
, 2011
"... Abstract. The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD ∗ semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic da ..."
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Cited by 2 (2 self)
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Abstract. The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD ∗ semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic
American Journal of Computer Science and Engineering Survey www.pubicon.co.in Original Article Hadoop MapReduce: A Programming Model for Large Scale Data Processing
"... Hadoop is free open source framework for Cloud Computing Environment. It is used to implement Googletm MapReduce framework. Map-Reduce technique is a popular framework which is used to process and generate large data on cloud. Map-Reduce technique of Hadoop is used for large-scale data-intensive app ..."
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Hadoop is free open source framework for Cloud Computing Environment. It is used to implement Googletm MapReduce framework. Map-Reduce technique is a popular framework which is used to process and generate large data on cloud. Map-Reduce technique of Hadoop is used for large-scale data
Reasoning with Large Scale
"... Abstract—The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has successfully applied MapReduce for large scale RDFS/OWL reasoning. In this paper, we move a step forward by considering scalable reasoning on semantic data under fuzzy pD * semantics (i.e., an ..."
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Abstract—The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has successfully applied MapReduce for large scale RDFS/OWL reasoning. In this paper, we move a step forward by considering scalable reasoning on semantic data under fuzzy pD * semantics (i
A Novel Soft Computing Inference Engine Model for Intrusion Detection
, 2008
"... The main purpose of this paper is to propose a novel soft computing inference engine model for intrusion detection. Our approach is anomaly based and utilizes causal knowledge inference based fuzzy cognitive maps (FCM) and multiple self organizing maps (SOM). A set of parallel neural network classif ..."
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Cited by 2 (0 self)
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The main purpose of this paper is to propose a novel soft computing inference engine model for intrusion detection. Our approach is anomaly based and utilizes causal knowledge inference based fuzzy cognitive maps (FCM) and multiple self organizing maps (SOM). A set of parallel neural network
AN INCREMENTAL AND DISTRIBUTED INFERENCE METHOD FOR LARGE-SCALE ONTOLOGIES USING ONE-CLASS CLUSTERING TREE
"... Abstract — Reasoning on a Web scale becomes increasingly challenging because of the large volume of data involved and the complexity of the task by means of ontology mapping. Ontology mapping processes users ’ queries that can provide more correct results when the mapping process can deal with the u ..."
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to process the increasing RDF data. MapReduce is a widely-used parallel programming model that can be used to represent uncertain similarities created by both syntactic and semantic similarity algorithms. The proposed One-Class Clustering Tree (OCCT) characterizes the entities that should be linked together
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