MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Graph Twiddling in a MapReduce World
Computing in Science and Engineering
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
The case for RAMClouds: scalable high-performance storage entirely in DRAM
ACM SIGOPS Operating Systems Review
Managing and Mining Graph Data
Managing and Mining Graph Data
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
HyperGraphDB: a generalized graph database
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
Probase: a probabilistic taxonomy for text understanding
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Efficient subgraph matching on billion node graphs
Proceedings of the VLDB Endowment
Probase: a probabilistic taxonomy for text understanding
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Efficient subgraph matching on billion node graphs
Proceedings of the VLDB Endowment
A first view of exedra: a domain-specific language for large graph analytics workflows
Proceedings of the 22nd international conference on World Wide Web companion
A distributed graph engine for web scale RDF data
Proceedings of the VLDB Endowment
Parallel processing of large graphs
Future Generation Computer Systems
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We are facing challenges at all levels ranging from infrastructures to programming models for managing and mining large graphs. A lot of algorithms on graphs are ad-hoc in the sense that each of them assumes that the underlying graph data can be organized in a certain way that maximizes the performance of the algorithm. In other words, there is no standard graph systems based on which graph algorithms are developed and optimized. In response to this situation, a lot of graph systems have been proposed recently. In this tutorial, we discuss several representative systems. Still, we focus on providing perspectives from a variety of standpoints on the goals and the means for developing a general purpose graph system. We highlight the challenges posed by the graph data, the constraints of architectural design, the different types of application needs, and the power of different programming models that support such needs. This tutorial is complementary to the related tutorial "Managing and Mining Large Graphs: Patterns and Algorithms".