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Automatic Incremental Calculation

The dynamics of big data reflects in the continuous update. Bounded incremental calculation thus needs to meet the new requirement. That is to say, when the data changes, there is no need to restart the calculation. The last calculation result and the data updates produce the new calculation result. Because the calculation result and data updates are usually much smaller than the original data, the improvement of computational efficiency should be significant. At present, the incremental programs specially designed for a specific problem face high barriers to entry. We are working on an effective and universal incremental method, using program language, compilers, and algorithm skills to build an incremental program.

Research Areas

Focus on big data incremental computing models and algorithms:
1) how to characterize the effectiveness of an incremental algorithm and whether a general method exists to achieve;
2) the boundedness of the incremental algorithm; characterize the cost of incremental calculation through the variation between input and output flow;
3) a general incremental method based on the boundedness of different incremental algorithms.

Related Publications

  • Incrementalization of Graph Partitioning Algorithms

    Authors: Wenfei Fan, Muyang Liu, Chao Tian, Ruiqi Xu, Jingren Zhou Published in: International Conference on Very Large Data Bases (VLDB 2020), Aug 31- Sept ...

    Wenfei Fan, Muyang Liu, Chao Tian, Ruiqi Xu, Jingren Zhou International Conference on Very Large Data Bases (VLDB 2020) International Conference on Very Large Data Bases (VLDB 2020), Aug 31- Sept 4, 2020, Tokyo, Japan
  • Effective and Efficient Relational Community Detection and Search in Large Dynamic Heterogeneous Information Networks

    Authors: Xun Jian, Yue Wang, Lei Chen Name of Conference: International Conference on Very Large Data Bases (VLDB 2020), Aug 31- Sept 4, 2020, Tokyo, Japan D...

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  • Fast and Accurate SimRank Computation via Forward Local Push and Its Parallelization

    Authors: Yue Wang, Yulin Che, Xiang Lian, Lei Chen, Qiong Luo Published in: IEEE Transactions on Knowledge and Data Engineering(TKDE 2020) Date of Publicat...

    Yue Wang, Yulin Che, Xiang Lian, Lei Chen, Qiong Luo IEEE Transactions on Knowledge and Data Engineering(TKDE 2020) IEEE Transactions on Knowledge and Data Engineering(TKDE 2020)
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