Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
A universal modular ACTOR formalism for artificial intelligence
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Marvin: Distributed reasoning over large-scale Semantic Web data
Web Semantics: Science, Services and Agents on the World Wide Web
Scalable Distributed Reasoning Using MapReduce
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Parallel Materialization of the Finite RDFS Closure for Hundreds of Millions of Triples
ISWC '09 Proceedings of the 8th International Semantic Web Conference
YARS2: a federated repository for querying graph structured data from the web
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
OWL reasoning with WebPIE: calculating the closure of 100 billion triples
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
HipG: parallel processing of large-scale graphs
ACM SIGOPS Operating Systems Review
An intermediate algebra for optimizing RDF graph pattern matching on MapReduce
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Application and evaluation of inductive reasoning methods for the semantic web and software analysis
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
QueryPIE: backward reasoning for OWL horst over very large knowledge bases
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
WebPIE: A Web-scale Parallel Inference Engine using MapReduce
Web Semantics: Science, Services and Agents on the World Wide Web
GraphX: a resilient distributed graph system on Spark
First International Workshop on Graph Data Management Experiences and Systems
Scale-up graph processing: a storage-centric view
First International Workshop on Graph Data Management Experiences and Systems
Overcoming limitations of term-based partitioning for distributed RDFS reasoning
Proceedings of the Fifth Workshop on Semantic Web Information Management
GPS: a graph processing system
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
The family of mapreduce and large-scale data processing systems
ACM Computing Surveys (CSUR)
Efficient query evaluation on distributed graphs with Hadoop environment
Proceedings of the Fourth Symposium on Information and Communication Technology
Fast iterative graph computation with block updates
Proceedings of the VLDB Endowment
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The Semantic Web graph is growing at an incredible pace, enabling opportunities to discover new knowledge by interlinking and analyzing previously unconnected data sets. This confronts researchers with a conundrum: Whilst the data is available the programming models that facilitate scalability and the infrastructure to run various algorithms on the graph are missing. Some use MapReduce - a good solution for many problems. However, even some simple iterative graph algorithms do not map nicely to that programming model requiring programmers to shoehorn their problem to the MapReduce model. This paper presents the Signal/Collect programming model for synchronous and asynchronous graph algorithms. We demonstrate that this abstraction can capture the essence of many algorithms on graphs in a concise and elegant way by giving Signal/Collect adaptations of various relevant algorithms. Furthermore, we built and evaluated a prototype Signal/Collect framework that executes algorithms in our programming model. We empirically show that this prototype transparently scales and that guiding computations by scoring as well as asynchronicity can greatly improve the convergence of some example algorithms. We released the framework under the Apache License 2.0 (at http://www.ifi.uzh.ch/ddis/research/sc).