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Yuanyuan Tian
Researcher at IBM
Publications - 71
Citations - 4326
Yuanyuan Tian is an academic researcher from IBM. The author has contributed to research in topics: Graph (abstract data type) & Graph database. The author has an hindex of 24, co-authored 68 publications receiving 3965 citations. Previous affiliations of Yuanyuan Tian include University of Michigan.
Papers
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Proceedings ArticleDOI
A comparison of join algorithms for log processing in MaPreduce
TL;DR: Key implementation details of a number of well-known join strategies in MapReduce are described and a comprehensive experimental comparison of these join techniques on a 100-node Hadoop cluster is presented.
Proceedings ArticleDOI
Efficient aggregation for graph summarization
TL;DR: This paper introduces two database-style operations to summarize graphs, called SNAP and k-SNAP, that allow users to control the resolutions of summaries and provides the "drill-down" and "roll-up" abilities to navigate through summaries with different resolutions.
Journal ArticleDOI
From "think like a vertex" to "think like a graph"
TL;DR: This work proposes a new "think like a graph" programming paradigm, and implements this model in a new system, called Giraph++, based on Apache Giraph, an open source implementation of Pregel.
Proceedings ArticleDOI
SystemML: Declarative machine learning on MapReduce
Amol Ghoting,Rajasekar Krishnamurthy,Edwin P. D. Pednault,Berthold Reinwald,Vikas Sindhwani,Shirish Tatikonda,Yuanyuan Tian,Shivakumar Vaithyanathan +7 more
TL;DR: This paper proposes SystemML in which ML algorithms are expressed in a higher-level language and are compiled and executed in a MapReduce environment and describes and empirically evaluate a number of optimization strategies for efficiently executing these algorithms on Hadoop, an open-source mapReduce implementation.
Proceedings ArticleDOI
TALE: A Tool for Approximate Large Graph Matching
Yuanyuan Tian,Jignesh M. Patel +1 more
TL;DR: A novel indexing method that incorporates graph structural information in a hybrid index structure that achieves high pruning power and the index size scales linearly with the database size is proposed.