Semantic data access in an asymmetric mobile environment

2003 ◽  
Author(s):  
K.C.K. Lee ◽  
H.V. Leong ◽  
A. Si
Keyword(s):  
Data Access ◽  
Mobile Environment ◽  
Semantic Data

Data Broadcasting in a Mobile Environment

2011 ◽  
pp. 96-154 ◽  
Author(s):  
A.R. Hurson ◽  
Y. Jiao
Keyword(s):  
Wireless Communication ◽  
Mobile Devices ◽  
High Performance ◽  
Data Access ◽  
Data Retrieval ◽  
Mobile Environment ◽  
Retrieval Method ◽  
Shared Data ◽  
Communication Techniques ◽  
Public Data

The advances in mobile devices and wireless communication techniques have enabled anywhere, anytime data access. Data being accessed can be categorized into three classes: private data, shared data, and public data. Private and shared data are usually accessed through on-demand-based approaches, while public data can be most effectively disseminated using broadcasting. In the mobile computing environment, the characteristics of mobile devices and limitations of wireless communication technology pose challenges on broadcasting strategy as well as data-retrieval method designs. Major research issues include indexing scheme, broadcasting over single and parallel channels, data distribution and replication strategy, conflict resolution, and data retrieval method. In this chapter, we investigate solutions proposed for these issues. High performance and low power consumption are the two main objectives of the proposed schemes. Comprehensive simulation results are used to demonstrate the effectiveness of each solution and compare different approaches.

Mobile Caching for Location-Based Services

2009 ◽  
pp. 3031-3039
Author(s):  
Jianliang Xu
Keyword(s):  
Mobile Computing ◽  
Information Access ◽  
Data Access ◽  
Mobile User ◽  
Location Based Services ◽  
Dependent Data ◽  
Mobile Users ◽  
Computing Environment ◽  
Mobile Environment ◽  
Mobile Computing Environment

Location-based services (LBS) are services that answer queries based on the locations with which the queries are associate; normally the locations where the queries are issued. With a variety of promising applications, such as local information access (e.g., traffic reports, news, and navigation maps) and nearest neighbor queries (e.g., finding the nearest restaurants) (Barbara, 1999; Ren & Dunham, 2000; D. L. Lee, Lee, Xu, & Zheng, 2002; W. C. Lee, Xu, & Zheng, 2004), LBS is emerging as an integral part of daily life. The greatest potential of LBS is met in a mobile computing environment, where users enjoy unrestricted mobility and ubiquitous information access. For example, a traveler could issue a query like “Find the nearest hotel with a room rate below $100” from a wireless portable device in the middle of a journey. To answer such a query, however, three major challenges have to be overcome: • Constrained Mobile Environments: Users in a mobile environment suffer from various constraints, such as scarce bandwidth, lowquality communication, frequent network disconnections, and limited local resources. These constraints pose a great challenge for the provision of LBS to mobile users. • Spatial Data: In LBS, the answers to a query associated with different locations may be different. That is, query results are dependent on spatial properties of queries. For a query bound with a certain query location, the query result should be relevant to the query as well as valid for the bound location. This requirement adds additional complexity to traditional data management techniques such as data placement, indexing, and query processing (D. L. Lee, 2002). • User Movement: The fact that a mobile user may change its location makes some tasks in LBS, such as query scheduling and cache management, particularly tough. For example, suppose that a mobile user issues a query “Find the nearest restaurant” at location A. If the query is not scheduled timely enough on the server, the user has moved to location B when he or she gets the answer R. However, R is no longer the nearest restaurant at location B. Caching has been a commonly used technique for improving data access performance in a mobile computing environment (Acharya, Alonso, Franklin, & Zdonik, 1995). There are several advantages for caching data on mobile clients: • It improves data access latency since a portion of queries, if not all, can be satisfied locally. • It helps save energy since wireless communication is required only for cache-miss queries. • It reduces contention on the narrow-bandwidth wireless channel and off-loads workload from the server; as such, the system throughput is improved. • It improves data availability in circumstances where clients are disconnected or weakly connected because cached data can be used to answer queries. However, as discussed above, the constrains of mobile computing environments, the spatial property of location-dependent data, and the mobility of mobile users have opened up many new research problems in client caching for LBS. This chapter discusses the research issues arising from caching of location-dependent data in a mobile environment and briefly describes several state-of-the-art solutions.

Mobile Caching for Location-Based Services

2011 ◽  
pp. 760-765
Author(s):  
Jianliang Xu
Keyword(s):  
Mobile Computing ◽  
Information Access ◽  
Data Access ◽  
Mobile User ◽  
Location Based Services ◽  
Dependent Data ◽  
Mobile Users ◽  
Computing Environment ◽  
Mobile Environment ◽  
Mobile Computing Environment

Location-based services (LBS) are services that answer queries based on the locations with which the queries are associate; normally the locations where the queries are issued. With a variety of promising applications, such as local information access (e.g., traffic reports, news, and navigation maps) and nearest neighbor queries (e.g., finding the nearest restaurants) (Barbara, 1999; Ren & Dunham, 2000; D. L. Lee, Lee, Xu, & Zheng, 2002; W. C. Lee, Xu, & Zheng, 2004), LBS is emerging as an integral part of daily life. The greatest potential of LBS is met in a mobile computing environment, where users enjoy unrestricted mobility and ubiquitous information access. For example, a traveler could issue a query like “Find the nearest hotel with a room rate below $100” from a wireless portable device in the middle of a journey. To answer such a query, however, three major challenges have to be overcome: • Constrained Mobile Environments: Users in a mobile environment suffer from various constraints, such as scarce bandwidth, low-quality communication, frequent network disconnections, and limited local resources. These constraints pose a great challenge for the provision of LBS to mobile users. • Spatial Data: In LBS, the answers to a query associated with different locations may be different. That is, query results are dependent on spatial properties of queries. For a query bound with a certain query location, the query result should be relevant to the query as well as valid for the bound location. This requirement adds additional complexity to traditional data management techniques such as data placement, indexing, and query processing (D. L. Lee, 2002). • User Movement: The fact that a mobile user may change its location makes some tasks in LBS, such as query scheduling and cache management, particularly tough. For example, suppose that a mobile user issues a query “Find the nearest restaurant” at location A. If the query is not scheduled timely enough on the server, the user has moved to location B when he or she gets the answer R. However, R is no longer the nearest restaurant at location B. Caching has been a commonly used technique for improving data access performance in a mobile computing environment (Acharya, Alonso, Franklin, & Zdonik, 1995). There are several advantages for caching data on mobile clients: • It improves data access latency since a portion of queries, if not all, can be satisfied locally. • It helps save energy since wireless communication is required only for cache-miss queries. • It reduces contention on the narrow-bandwidth wireless channel and off-loads workload from the server; as such, the system throughput is improved. • It improves data availability in circumstances where clients are disconnected or weakly connected because cached data can be used to answer queries. However, as discussed above, the constrains of mobile computing environments, the spatial property of location-dependent data, and the mobility of mobile users have opened up many new research problems in client caching for LBS. This chapter discusses the research issues arising from caching of location-dependent data in a mobile environment and briefly describes several state-of-the-art solutions.

An Ontology-Based Semantic Data Mining in Mobile Environment

2017 ◽  
Vol 23 (10) ◽  
pp. 10241-10245
Author(s):  
Mi-Sug Gu ◽  
Jeong-Hee Hwang
Keyword(s):  
Data Mining ◽  
Mobile Environment ◽  
Semantic Data ◽  
Semantic Data Mining

scholarly journals A Novel Query Method for Spatial Data in Mobile Cloud Computing Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Guangsheng Chen ◽  
Pei Nie ◽  
Weipeng Jing
Keyword(s):  
Spatial Data ◽  
File System ◽  
Fold Increase ◽  
Data Access ◽  
Disk File ◽  
Mobile Environment ◽  
Traffic Demand ◽  
Data Query ◽  
Distributed Query ◽  
Low Efficiency

With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.

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