Showing papers by "Bin Yao published in 2014"
01 Oct 2014
TL;DR: A unified framework is proposed that addresses three variants of OL queries that find important applications in practice, and it is extended to efficiently monitor the OLs when locations for facilities and/or clients have been updated.
Abstract: Optimal location (OL) queries are a type of spatial queries that are particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an $$L_p$$ L p space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their $$L_p$$ L p distance. Motivated by the deficiency of the existing techniques, this paper presents a comprehensive study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We further extend our framework to efficiently monitor the OLs when locations for facilities and/or clients have been updated. Our dynamic update methods lead to efficient answering of continuous optimal location queries. We demonstrate the efficiency of our solutions through extensive experiments with large real data.
24 citations
TL;DR: A sharing system called LSShare is developed using the proposed Lineage-Signature approach to efficiently solve the MQO problem in a recurring query set situation in the cloud.
Abstract: Multiple query optimization (MQO) in the cloud has become a promising research direction due to the popularity of cloud computing, which runs massive data analysis queries (jobs) routinely. These CPU/IO intensive analysis queries are complex and time-consuming but share common components. It is challenging to detect, share and reuse the common components among thousands of SQL-like queries. Previous solutions to MQO, heuristic or genetic based, are not appropriate for the large growing query set situation. In this paper, we develop a sharing system called LSShare using our proposed Lineage-Signature approach. By LSShare, we can efficiently solve the MQO problem in a recurring query set situation in the cloud. Our system has been prototyped in a distributed system built for massive data analysis based on Alibaba's cloud computing platform ( http://www.alibaba.com/ ). Experimental results on real data sets demonstrate the efficiency and effectiveness of the proposed approach.
10 citations
01 Dec 2014
TL;DR: Results demonstrate that the JRCA approach significantly outperforms related proposals in terms of average e2e delay and a heuristic algorithm joints routing and channel assignment is designed to solve the J RCA problem.
Abstract: Channel interference and node mobility cause significant performance degradation to wireless networks. In multichannel multi-flow mobile cognitive ad hoc networks, it becomes even worse due to both unexpected primary user activities and potential interference among multiple flows. In this paper, we propose a Joint Routing and Channel Assignment (JRCA) approach based on delay prediction. Firstly, it formulates the JRCA problem with the objective of delay minimization. Next, a delay prediction model is proposed based on the channel collision probability. Then, a heuristic algorithm joints routing and channel assignment is designed to solve the JRCA problem. the JRCA algorithm can find out the path with minimal end- toend (e2e) delay. NS2-based simulation results demonstrate that the JRCA approach significantly outperforms related proposals in terms of average e2e delay.
8 citations
01 Sep 2014
TL;DR: It is proved theoretically that RT-HCN is both query-efficient and space-efficient, by which each server will only maintain a tolerable number of indices while a large number of users can concurrently process queries with low routing cost.
Abstract: Cloud storage system such as Amazon’s Dynamo and Google’s GFS poses new challenges to the community to support efficient query processing for various applications. In this paper we propose RT-HCN, a distributed indexing scheme for multi-dimensional query processing in data centers, the infrastructure to build cloud systems. RT-HCN is a two-layer indexing scheme, which integrates HCN-based routing protocol and the R-Tree based indexing technology, and is portionably distributed on every server. Based on the characteristics of HCN, we design a special index publishing rule and query processing algorithms to guarantee efficient data management for the whole network. We prove theoretically that RT-HCN is both query-efficient and space-efficient, by which each server will only maintain a tolerable number of indices while a large number of users can concurrently process queries with low routing cost. We compare our design with RT-CAN, a similar design in traditional P2P network. Experiments validate the efficiency of our proposed scheme and depict its potential implementation in data centers.
5 citations