论文信息 - Enhancing Big Data with Semantics: The AsterixDB Approach (Poster)

Enhancing Big Data with Semantics: The AsterixDB Approach (Poster)

We explain how Apache AsterixDB, an open source Big Data platform, can help reduce the burden of extracting, storing, and exploiting semantics in Big Data. We describe its support for semi-structured data, Data Feeds, and user-defined functions (UDFs). Using these features from AsterixDB, users can easily create customized dataflows for semantic data analyses.

[1] Pete Wyckoff,et al. Hive - A Warehousing Solution Over a Map-Reduce Framework , 2009, Proc. VLDB Endow..

[2] Michael J. Carey,et al. Data Ingestion in AsterixDB , 2015, EDBT.

[3] Chen Li,et al. AsterixDB: A Scalable, Open Source BDMS , 2014, Proc. VLDB Endow..

[4] Michael J. Carey,et al. Large-scale Complex Analytics on Semi-structured Datasets using AsterixDB and Spark , 2016, Proc. VLDB Endow..

[5] Yannis Papakonstantinou,et al. The SQL++ Query Language: Configurable, Unifying and Semi-structured , 2014, 1405.3631.

[6] Scott Shenker,et al. Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[7] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.