Academic literature on the topic 'Caching;Prefetching;Mobile User profiles'

Network latency is one of the most critical factors for the usability of mobile SOA applications. This paper introduces prefetching and caching enhancements for an existing SOA framework for mobile applications to reduce the user perceived latency. Latency reduction is achieved by proactively sending data to the mobile device that could most likely be requested at a later time. This additional data is piggybacked onto responses to actual requests and injected into a client side cache, so that it can be used without an additional connection. The prefetching is done automatically using a sequence prediction algorithm. The benefit of prefetching and caching enhancements were evaluated for different network settings and a reduction of user perceived latency of up to 31% was found in a typical scenario. In contrast to other prefetching solutions, our piggybacking approach also allows to significantly increase battery lifetime of the mobile device.

This thesis describes a new method of calculating data priority by using adaptive mobile user and device profiles which change with user location, time of the day, available networks and data access history. The profiles are used for data prefetching, selection of most suitable wireless network and cache management on the mobile device in order to optimally utilize the device's storage capacity and available bandwidth. Some of the inherent characteristics of mobile devices due to user movements are – non-persistent connection, limited bandwidth and storage capacity, changes in mobile device's geographical location and connection (eg. connection can be from GPRS to WLAN to Bluetooth). New research is being carried out in making mobile devices work more efficiently by reducing and/or eliminating their limitations. The focus of this research is to propose, evaluate and test a new user profiling technique which specifically caters to the needs of the mobile device users who are required to access large amounts of data, possibly more than the device storage capability during the course of the day or week. This work involves the development of an intelligent user profiling system along with mobile device caching system which will first allocate weight (priority) to the different sets and subsets of the total given data based on user's location, user's appointment information, user's preferences, device capabilities and available networks. Then the profile will automatically change the data weights with user movements, history of cached data access and characteristics of available networks. The Adaptive User and Device Profiles were designed to handle broad range of the issues associated with: •Changing network types and conditions •Limited storage capacity and document type support of mobile devices •Changes in user data needs due to their movements at different times of the day Many research areas have been addressed through this research but the primary focus has remained on the following four core areas. The four core areas are : selecting the most suitable wireless network; allocating weights to different datasets & subsets by integrating user's movements; previously accessed data; time of the day with user appointment information and device capabilities.

This thesis describes a new method of calculating data priority by using adaptive mobile user and device profiles which change with user location, time of the day, available networks and data access history. The profiles are used for data prefetching, selection of most suitable wireless network and cache management on the mobile device in order to optimally utilize the device�s storage capacity and available bandwidth. Some of the inherent characteristics of mobile devices due to user movements are �non-persistent connection, limited bandwidth and storage capacity, changes in mobile device�s geographical location and connection (eg. connection can be from GPRS to WLAN to Bluetooth). New research is being carried out in making mobile devices work more efficiently by reducing and/or eliminating their limitations. The focus of this research is to propose, evaluate and test a new user profiling technique which specifically caters to the needs of the mobile device users who are required to access large amounts of data, possibly more than the device storage capability during the course of the day or week. This work involves the development of an intelligent user profiling system along with mobile device caching system which will first allocate weight (priority) to the different sets and subsets of the total given data based on user�s location, user�s appointment information, user�s preferences, device capabilities and available networks. Then the profile will automatically change the data weights with user movements, history of cached data access and characteristics of available networks. The Adaptive User and Device Profiles were designed to handle broad range of the issues associated with: �Changing network types and conditions �Limited storage capacity and document type support of mobile devices �Changes in user data needs due to their movements at different times of the day Many research areas have been addressed through this research but the primary focus has remained on the following four core areas. The four core areas are : selecting the most suitable wireless network; allocating weights to different datasets & subsets by integrating user�s movements; previously accessed data; time of the day with user appointment information and device capabilities.

Map navigation is one of the most popular applications used by mobile users. At the same time, it is also one of the time- and resource-consuming applications. Various methods such as most-recently used and first-in, first-out algorithms are used to reduce the map transmission time and delay. One of the popular methods is online mobile map prefetching and caching. However, the mobility and location features of mobile users are usually left out by these methods. Caching and prefetching maps based on a mobile user’s location would greatly reduce the transmission time and hence the battery power consumption. For example, if a user is visiting a town, prefetching the maps of nearby interesting stores and caching the maps of the visited, neighboring landmarks would help the user’s visitation experience and save the transmission time. Online mobile map prefetching or caching is useful, but is not widely employed because it involves several different subjects and developers usually are not familiar with all of them. This chapter intends to relieve the problem by introducing essential technologies for online mobile map prefetching and caching so more developers can start working on this kind of project or research. It consists of four themes: (1) green handheld computing, (2) location-based services and programming, (3) map tile system, and (4) location-aware map prefetching and caching methods. A summary is given at the end of this chapter.

Location-based services (LBS) are gaining prominence in today's environment. When a mobile user submits a location-based query in LBS, an adversary may infer the locations or other related sensitive information. Thus, an efficient location privacy preservation model (LPPM) with minimal overhead needs to be built by considering contextual understanding and analytical ability. With consideration of service efficiency and privacy, a location privacy preservation policy, namely mobility-aware prefetching and replacement (MOPAR) policy, has been proposed by the cloaking area formulation through user location, cache contribution rate, and data freshness in LBS. An incorporation of prefetching and replacement to anonymizer and consumer cache with formulation of cloak area is being deployed to protect customer sensitive information. The Markov mobility model-based next-position prediction procedure is used in this chapter for caching and formulation of cloaking area. The results of the simulation show significant enhancement in the efficiency of the location-privacy preservation model.

Discrete sequence modeling and prediction is an important goal and a challenge for Web environments, both wired and wireless. Web client’s data request forecasting and mobile location tracking in wireless cellular networks are characteristic application areas of sequence prediction in such environments. Accurate data request prediction results in effective data prefetching which combined with a caching mechanism can reduce user-preceived latencies as well as server and network loads. Also, effective solutions to the mobility tracking/prediction problem can reduce the update and paging costs, freeing the network from exceesive signaling traffic. Therefore, sequence prediction comprises a very important study and development area . This article presents information-theoretic techniques for discrete sequence prediction. It surveys, classifies, and compares the state-of-the-art solutions, suggesting routes for further research by discussing the critical issues and challenges of prediction in wired and wireless networks