@Article{jbd.2020.010358,
AUTHOR = {Aoran Li, Xinmeng Wang, Xueliang Wang, Bohan Li},
TITLE = {An Improved Distributed Query for Large-Scale RDF Data},
JOURNAL = {Journal on Big Data},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {157--166},
URL = {http://www.techscience.com/jbd/v2n4/41033},
ISSN = {2579-0056},
ABSTRACT = {The rigid structure of the traditional relational database leads to data 
redundancy, which seriously affects the efficiency of the data query and cannot 
effectively manage massive data. To solve this problem, we use distributed 
storage and parallel computing technology to query RDF data. In order to 
achieve efficient storage and retrieval of large-scale RDF data, we combine the 
respective advantage of the storage model of the relational database and the 
distributed query. To overcome the disadvantages of storing and querying RDF 
data, we design and implement a breadth-first path search algorithm based on the 
keyword query on a distributed platform. We conduct the LUBM query 
statements respectively with the selected data sets. In experiments, we compare 
query response time in different conditions to evaluate the feasibility and 
correctness of our approaches. The results show that the proposed scheme can 
reduce the storage cost and improve query efficiency.},
DOI = {10.32604/jbd.2020.010358}
}