An Improved Distributed Query for Large-Scale RDF Data

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.

Learned Distributed Query Optimizer:Architecture and Challenges

The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.

Distributed GEP query optimization on grid service

In order to better solve the problem of distributed query optimization, a query optimization algorithm on gene expression programming （GEP） （QO-GEP） is presented. On the basis of QO-GEP, distributed GEP query optimization on grid service （DGEPQO-GS） is proposed which combines grid service. Simulated experiments show that with the increment of the number of query relation, query time which QO-GEP carries out query decreases apparently. Meanwhile, with the increase of the number of grid nodes, the average querying success rate of DGEPQO-GS increases significantly.

Effcient Distributed Skyline Queries for Mobile Applications

In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites （database servers） which are interconnected through a high-speed wired network, and queries are issued by mobile units （laptop, cell phone, etc.） which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing （EDS-MC）. In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.