scholarly journals Research in Mobile Database Query Optimization and Processing

2005 ◽  
Vol 1 (4) ◽  
pp. 225-252 ◽  
Author(s):  
Agustinus Borgy Waluyo ◽  
Bala Srinivasan ◽  
David Taniar
Keyword(s):  
Mobile Computing ◽  
Query Processing ◽  
Query Optimization ◽  
Management Strategy ◽  
Data Dissemination ◽  
Replacement Policy ◽  
Processing Strategy ◽  
Mobile Databases ◽  
Periodic Broadcast ◽  
On Demand

The emergence of mobile computing provides the ability to access information at any time and place. However, as mobile computing environments have inherent factors like power, storage, asymmetric communication cost, and bandwidth limitations, efficient query processing and minimum query response time are definitely of great interest. This survey groups a variety of query optimization and processing mechanisms in mobile databases into two main categories, namely: (i) query processing strategy, and (ii) caching management strategy. Query processing includes both pull and push operations (broadcast mechanisms). We further classify push operation into on-demand broadcast and periodic broadcast. Push operation (on-demand broadcast) relates to designing techniques that enable the server to accommodate multiple requests so that the request can be processed efficiently. Push operation (periodic broadcast) corresponds to data dissemination strategies. In this scheme, several techniques to improve the query performance by broadcasting data to a population of mobile users are described. A caching management strategy defines a number of methods for maintaining cached data items in clients' local storage. This strategy considers critical caching issues such as caching granularity, caching coherence strategy and caching replacement policy. Finally, this survey concludes with several open issues relating to mobile query optimization and processing strategy.

Data Dissemination in Mobile Databases

2011 ◽  
pp. 914-920
Author(s):  
Agustinus Borgy Waluyo ◽  
Bala Srinivasan ◽  
David Taniar
Keyword(s):  
Response Time ◽  
Mobile Computing ◽  
Data Dissemination ◽  
Wireless Channel ◽  
Data Broadcasting ◽  
Wireless Technology ◽  
Mobile Databases ◽  
Tuning Time ◽  
Query Performance ◽  
Download Time

The development of wireless technology has led to mobile computing, a new era in data communication and processing (Barbara, 1999; Myers & Beigl, 2003). With this technology, people can now access information anytime and anywhere using a portable, wireless computer powered by battery (e.g., PDAs). These portable computers communicate with a central stationary server via a wireless channel. Mobile computing provides database applications with useful aspects of wireless technology known as mobile databases. The main properties of mobile computing include mobility, severe power and storage restriction, frequency of disconnection that is much greater than a traditional network, bandwidth capacity, and asymmetric communications costs. Radio wireless transmission usually requires a greater amount of power as compared with the reception operation (Xu, Zheng, Zhu, & Lee, 2002). Moreover, the life expectancy of a battery (e.g., nickel-cadmium, lithium ion) was estimated to increase time of effective use by only another 15% (Paulson, 2003). Thus, efficient use of energy is definitely one of the main issues. Data dissemination (can also be called data broadcasting) is one way to overcome these limitations. With this mechanism, a mobile client is able to retrieve information without wasting power to transmit a request to the server. Other characteristics of data dissemination include: scalability as it supports a large number of queries; query performance which is not affected by the number of users in a cell as well as the request rate; and effective to a high-degree of overlap in the user’s request. In this article, the terms data dissemination and data broadcasting are used interchangeably. The ultimate challenge in data dissemination is to minimize the response time and tuning time of retrieving database items. Response time is the total of elapsed time required for the data of interest to arrive in the channel and the download time, while tuning time is the amount of time that a client is required to listen to the channel, which is used to indicate its energy consumption. In some cases, the response time is equal to the tuning time. This article describes a state-of-the art development in data dissemination strategies in mobile databases. Several strategies for improving the query performance by disseminating data to a population of mobile users will be explained.

Data Dissemination in Mobile Databases

2005 ◽  
pp. 691-697
Author(s):  
Agustinus Borgy Waluyo ◽  
Bala Srinivasan ◽  
David Taniar
Keyword(s):  
Mobile Computing ◽  
Data Dissemination ◽  
Data Communication ◽  
Wireless Channel ◽  
Wireless Technology ◽  
Database Applications ◽  
Mobile Databases ◽  
New Era ◽  
Access Information ◽  
Portable Computers

The development of wireless technology has led to mobile computing, a new era in data communication and processing (Barbara, 1999; Myers & Beigl, 2003). With this technology, people can now access information anytime and anywhere using a portable, wireless computer powered by battery (e.g., PDAs). These portable computers communicate with a central stationary server via a wireless channel. Mobile computing provides database applications with useful aspects of wireless technology known as mobile databases.

Research on Query Optimization Technology in Distributed Database

2012 ◽  
Vol 433-440 ◽  
pp. 3335-3339
Author(s):  
Bo Zhu Wu
Keyword(s):  
Query Processing ◽  
Query Optimization ◽  
Distributed Database ◽  
Query Time ◽  
Processing Technology ◽  
Database Query ◽  
Processing Program ◽  
Depth Study ◽  
Database Query Processing ◽  
Query Efficiency

Through the in-depth study of the existing distributed database query processing technology, this paper proposes a distributed database query processing program. This program optimizes the existing query processing, stores the commonly used query results according to the query frequency, to be directly used by the subsequent queries or used as intermediate query results, thus avoiding possible transmission of a large number of data, thereby reducing the query time and improving query efficiency.

Study on Clustering-Based Location Dependent Query

2012 ◽  
Vol 532-533 ◽  
pp. 897-901
Author(s):  
Ming Jun Wei ◽  
Li Chun Xia ◽  
Jian Guo Jin ◽  
Qiu Hong Fan
Keyword(s):  
Mobile Computing ◽  
Query Processing ◽  
Location Analysis ◽  
System Efficiency ◽  
Analysis Algorithm ◽  
System Resources ◽  
Location Dependent Query ◽  
Special Case ◽  
New Location

This paper firstly analyzes the importance and necessity of location dependent query in the mobile computing. Then, it proposes a special case in the application of the location dependent query. That is as follows: Inquirers may send the same location dependent query in different but similar positions. However, the server will not deal with them together but treat them separately. Thus, it will not only cause the waste of system resources but also delay disposal of other queries. According to the principal of clustering we propose a new location Analysis Algorithms-similar merging location analysis algorithm (SMLA). By the algorithm, similar queries can be combined into the same query, so as to reduce the load on central servers, improve system efficiency and query processing performance.

Efficient Storage and Parallel Query of Massive XML Data in Hadoop

2019 ◽  
pp. 242-262
Author(s):  
Wei Yan
Keyword(s):  
Query Processing ◽  
Query Optimization ◽  
Data Transfer ◽  
Programming Model ◽  
Xml Data ◽  
Efficient Storage ◽  
Parallel Query ◽  
Parallel Query Processing

In order to solve the problem of storage and query for massive XML data, a method of efficient storage and parallel query for a massive volume of XML data with Hadoop is proposed. This method can store massive XML data in Hadoop and the massive XML data is divided into many XML data blocks and loaded on HDFS. The parallel query method of massive XML data is proposed, which uses parallel XPath queries based on multiple predicate selection, and the results of parallel query can satisfy the requirement of query given by the user. In this chapter, the map logic algorithm and the reduce logic algorithm based on parallel XPath queries based using MapReduce programming model are proposed, and the parallel query processing of massive XML data is realized. In addition, the method of MapReduce query optimization based on multiple predicate selection is proposed to reduce the data transfer volume of the system and improve the performance of the system. Finally, the effectiveness of the proposed method is verified by experiment.

Sign in / Sign up

Export Citation Format

Share Document