Wen-Chih Peng

Developing data allocation schemes by incremental mining of user moving patterns in a mobile computing system

In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two schemes are devised: one utilizes the set level of moving patterns and the other utilizes their path level. Schemes for shared data are also developed. Performance of these schemes is comparatively analyzed.

Dynamic generation of data broadcasting programs for a broadcast disk array in a mobile computing environment

We explore in this paper the problem of generating hierarchical broadcast programs with the data access frequencies and the number of broadcast disks in a broadcast disk array given. Specifically, we first transform the problem of generating hierarchical broadcast programs into the one of constructing a channel allocation tree with variant-fanout. By exploiting the feature of tree generation with variantfanout, we develop a heuristic algorithm VF to minimize the expected delay of data items in the broadcast program. Performance of these algorithms is analyzed. It is shown by our simulation results that by exploiting the feature of variant-fanout in constructing the channel allocation tree, the solution obtained by algorithm VF is of very high quality and is in fact very close to the optimal one.

Efficient channel allocation tree generation for data broadcasting in a mobile computing environment

The research issue of broadcasting has attracted a considerable amount of attention in a mobile computing system. By utilizing broadcast channels, a server continuously and repeatedly broadcasts data to mobile users. These broadcast channels are also known as “broadcast disks” from which mobile users can retrieve data. Using broadcasting, mobile users can obtain the data of interest efficiently and only need to wait for the required data to present on the broadcast channel. The issue of designing proper data allocation in the broadcast disks is to reduce the average expected delay of all data items. We explore in this paper the problem of generating hierarchical broadcast programs with the data access frequencies and the number of broadcast disks in a broadcast disk array given. Specifically, we first transform the problem of generating hierarchical broadcast programs into the one of constructing a channel allocation tree with variant-fanout. By exploiting the feature of tree generation with variant-fanout, we develop a heuristic algorithm VFK to minimize the expected delay of data items in the broadcast program. In order to evaluate the solution quality obtained by algorithm VFK and compare its resulting broadcast program with the optimal one, we devise an algorithm OPT based on a guided search to obtain the optimal solution. Performance of these algorithms is comparatively analyzed. Sensitivity analysis on several parameters, including the number of data items and the number of broadcast disks, is conducted. It is shown by our simulation results that by exploiting the feature of variant-fanout in constructing the channel allocation tree, the solution obtained by algorithm VFK is of very high quality and is in fact very close to the optimal one resulted by algorithm OPT. Moreover, algorithm VFK is of very good scalability which is important for algorithm VFK to be of practical use to generate hierarchical broadcast programs dynamically in a mobile computing environment.

Broadcasting dependent data for ordered queries without replication in a multi-channel mobile environment

In several mobile applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data mainly consider single broadcast channel environments. In view of this, we explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs.

Mining Sequential Alarm Patterns in a Telecommunication Database

A telecommunication system produces daily a large amount of alarm data which contains hidden valuable information about the system behavior. The knowledge discovered from alarm data can be used in finding problems in networks and possibly in predicting severe faults. In this paper, we devise a solution procedure for mining sequential alarm patterns from the alarm data of a GSM system. First, by observing the features of tile alarm data, we develop operations for data cleaning. Then, we transform the alarm data into a set of alarm sequences. Note that the consecutive alarm events exist in the alarm sequences, and it is complicated to count the occurrence counts of events and extract patterns. Hence, we devise a new procedure to determine the occurrence count of the sequential alarm patterns in accordance with the nature of alarms. By utilizing time constraints to restrict tile time difference between two alarm events, we devise a mining algorithm to discover useful sequential alarm patterns. The proposed mining algorithm is implemented and applied to test against a set of real alarm data provided by a cellular phone company. The quality of knowledge discovered is evaluated. The experimental results show that the proposed operations of data cleaning are able to improve tile execution of our mining algorithm significantly and tile knowledge obtained from the alarm data is very useful from tile perspective of network operators for alarm prediction and alarm control.

Mining user moving patterns for personal data allocation in a mobile computing system

In this paper, we devise a new data mining algorithm which involves mining for user moving patterns in a mobile computing environment, and utilize the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we devise an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes for proper allocation of personal data. Two personal data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile computing system.

Dynamic leveling: adaptive data broadcasting in a mobile computing environment

The research issue of broadcasting has attracted a considerable amount of attention in a mobile computing system. By utilizing broadcast channels, a server is able to continuously and repeatedly broadcast data to mobile users. From these broadcast channels, mobile users obtain the data of interest efficiently and only need to wait for the required data to be present on the broadcast channel. Given the access frequencies of data items, one can design proper data allocation in the broadcast channels to reduce the average expected delay of data items. In practice, the data access frequencies may vary with time. We explore in this paper the problem of adjusting broadcast programs to effectively respond to the changes of data access frequencies, and develop an efficient algorithm DL to address this problem. Performance of algorithm DL is analyzed and a system simulator is developed to validate our results. Sensitivity analysis on several parameters, including the number of data items, the number of broadcast disks, and the variation of access frequencies, is conducted. It is shown by our results that the broadcast programs adjusted by algorithm DL are of very high quality and are in fact very close to the optimal ones.

Shared data allocation in a mobile computing system: exploring local and global optimization

In this paper, we devise data allocation algorithms that can utilize the knowledge of user moving patterns for proper allocation of shared data in a mobile computing system. By employing the data allocation algorithms devised, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. The data allocation algorithms for shared data, which are able to achieve local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized. Specifically, by exploring the features of local optimization and global optimization, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. In general, the mobile users are divided into two types, namely, frequently moving users and infrequently moving users. A measurement, called closeness measure which corresponds to the amount of the intersection between the set of frequently moving user patterns and that of infrequently moving user patterns, is derived to assess the quality of solutions provided by SD-local and SD-global. Performance of these data allocation algorithms is comparatively analyzed. From the analysis of SD-local and SD-global, it is shown that SD-local favors infrequently moving users whereas SD-global is good for frequently moving users. The simulation results show that the knowledge obtained from the user moving patterns is very important in devising effective data allocation algorithms which can lead to prominent performance improvement in a mobile computing system.

Binary interpolation search for solution mapping on broadcast and on-demand channels in a mobile computing environment

We explore in this paper the problem of dynamic data and channel allocations with the number of communication channels and the number of data items given. It is noted that the combined use of broadcast and on-demand channels can utilize the bandwidth effectively for data dissemination in a mobile computing environment. We first derive the an-alytical models of the expected delays when the data are requested through the broadcast and on-demand channels. Then, we transform this problem into to a guided search problem. In light of the theoretical properties derived, we devise an algorithm based on binary interpolation search, referred to as algorithm BIS, to obtain solutions of high quality efficiently. In essence, algorithm BIS is guided to explore the solution space with higher likelihood to be the optimal first, thereby leading to an efficient and effective search. It is shown by our simulation results that the solution obtained by algorithm BIS is of very high quality and is in fact very close to the optimal one. Sensitivity analysis on several parameters, including the number of data items and the number of communication channels, is conducted.

SOM: dynamic push-pull channel allocation framework for mobile data broadcasting

In a mobile computing environment, the combined use of broadcast and on-demand channels can utilize the bandwidth effectively for data dissemination. We explore in this paper the problem of dynamic data and channel allocation with the number of communication channels and the number of data items given. We first derive the analytical models of the average access time when the data items are requested through the broadcast and on-demand channels. Then, we transform this problem into a guided search problem. In light of the theoretical properties derived, we devise algorithm SOM to obtain the optimal allocation of data and channels. Algorithm SOM is a composite algorithm which will cooperate with 1) a search strategy and 2) a broadcast program generation algorithm. According to the analytical mode, we devise scheme BIS-incremental on the basis of algorithm SOM, which is able to obtain solutions of high quality efficiently by employing binary interpolation search. In essence, scheme BIS-incremental is guided to explore the search space with higher likelihood to be the optimal first, thereby leading to an efficient and effective search. It is shown by our simulation results that the solution obtained by scheme BIS-incremental is of very high quality and is in fact very close to the optimal one. A sensitivity study on several parameters, including the number of data items and the number of communication channels, is conducted. The experimental results show that scheme BIS-incremental is of very good scalability, which is particularly important for its practical use in a mobile computing environment