Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In this environment, the channel bandwidth of each channel is variant with time in real cases. However, traditional schemes do not consider time-variant bandwidth of each channel to schedule data items. Therefore, the above drawback degrades the performance in generating broadcast programs. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels of time-variant bandwidth. In view of the characteristics of time-variant bandwidth, we propose an algorithm using adaptive allocation on time-variant bandwidth to generate the broadcast program to avoid the above drawback to minimize average waiting time. Experimental results show that our approach is able to generate the broadcast programs with high quality and is very efficient in a data broadcasting environment with the time-variant bandwidth.

We explore in this paper the problem of generating broadcast programs in a heterogeneous data broadcasting environment, in which disseminated data items can be of different sizes. Given the broadcast database and the number of channels, we first derive the analytical model of the heterogeneous data broadcasting to obtain the average waiting time of mobile users, and prove the allocation problem as an NP-complete problem. In order to solve such problem, we propose a two-phase architecture to perform channel allocation. Algorithm DRP (dimension reduction partitioning) is employed to perform rough allocation to derive the satisfactory solutions, whereas mechanism CDMS (cost-diminishing movement selection) is used for fine allocation to achieve local optimum solutions. In addition, we also propose algorithm GA-CDMS according to the concept of hybrid genetic algorithm for comparison purposes. GA-CDMS can perform global search more accurately and efficiently than conventional genetic algorithm GA and the suboptimum that GA-CDMS achieves will be very close to the optimal solution. In the experiments, we consider the important issues such as accuracy, scalability, diversity and the efficiency. From the experimental results, we show that the proposed two-phase channel allocation is very practical in performing an effective channel allocation with high efficiency in a heterogeneous broadcasting environment.

Recent technology advances in multimedia communication have ushered in a new era of personal communication. Users can ubiquitously access the Internet via various mobile devices. For the mobile devices featured with lower bandwidth network connectivity, transcoding can be used to reduce the object size by lowering the quality of a multimedia object. In view of the monolithic transcoders which only provide transcoding services and have limited performances due to the unknown data types and protocols in the prior research, we propose the architecture of versatile transcoding proxy (VTP). Based on the concept of the agent system, the VTP architecture can accept and execute the transcoding preference script provided by the client or the server to transform the corresponding data or protocol according to the user's specification. In order to enhance the effectiveness of the VTP architecture, we adopt the concept of dynamic cache categories and propose the scheme maximum profit replacement with dynamic cache categories (DCC-MPR). Based on the weighted transcoding graph, scheme DCC-MPR performs cache replacement according to the content in the caching candidate set, which is generated by the concept of dynamic programming. The experimental results show that the proposed architecture VTP and the corresponding scheme DCC-MPR have better performances in many aspects compared to the conventional transcoding proxy systems.

E-Learning standard (SCORM) has been extensively devel- oped in recent years. It provides the direction of extending distance edu- cation to other mediums. Multimedia Home Platform (MHP) is a promis- ing interactive digital TV standard. In order to extend the re-use of the resource database in SCORM, this paper devises a new platform, which allows the use of SCORM resource to provide educational services on the MHP platform, i.e., T-Learning. Explicitly, we develop a SCORM compati- ble T-Learning system architecture named Resource-Reuseable T-Learning (RTL), which is capable of reusing the SCORM resource package. More- over, to enhance the efficiency of parsing the T-Learning documents, we also propose a two-phase parsing algorithm, denoted by TPP. Our system architecture and the parsing algorithm makes the DVB platform flexible, extensible, and easy to be integrated with the existing E-Learning SCORM materials. We demonstrate the prototype of RTL architecture in this paper and show the efficiency of the TPP algorithm via our experimental results.

Data broadcast is a technique to realize energy saving and bandwidth utilization in a mobile computing environment. However, traditional schemes schedule data items without considering channel bandwidth. Therefore, the above drawback leads to the unfair broadcasting rate of each item of the different access frequency. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels with variant bandwidth. In view of the characteristics of variant bandwidth, we propose an algorithm using adaptive partition on bandwidth to generate broadcast program to avoid the above drawback so as to minimize the average waiting time. The empirical results show that our approach is able to produce broadcast programs of high quality and is very efficient in a data broadcasting environment with variant bandwidth.

In recent years, data broadcasting becomes a promising technique to design a mobile information system with power conservation, high scalability and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items which should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUlti-Level Service) for an information system. There are two primary stages in MULS: on-line scheduling and optimization procedure. In the first stage, we propose an On- Line Scheduling algorithm (denoted by OLS) to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure SCI, standing for Sampling with Controlled Iteration, to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (i.e., the average access time of mobile users) and efficiency (i.e., the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform online scheduling with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.

Due to the resource limitation in the data stream environments, it has been reported that answering user queries according to the wavelet synopsis of a stream is an essential ability of a Data Stream Management System (DSMS). In the literature, recent research has been elaborated upon minimizing the local error metric of an individual stream. However, many emergent applications, such as stock marketing and sensor detection, also call for the need of recording multiple streams in a commercial DSMS. As shown in our thorough analysis and experimental studies, minimizing global error in multiple-stream environments leads to good reliability for DSMS to answer the queries; in contrast, only minimizing local error may lead to significant loss of query accuracy. As such, we first study in this paper the problem of maintaining the wavelet coefficients of multiple streams within collective memory so that the predetermined global error metric is minimized. Moreover, we also examine a promising application in the multistream environment, i.e., the queries for top-k range sum. We resolve the problem of efficient top-k query processing with minimized global error by developing a general framework. For the purposes of maintaining the wavelet coefficients and processing top-k queries, several well-designed algorithms are utilized to optimize the performance of each primary component of this general framework. We also evaluate the proposed algorithms empirically on real and simulated data streams and show that our framework can process top-k queries accurately and efficiently.

Due to the resource limitation in the data stream environment, it has been reported that answering user queries according to the wavelet synopsis of a stream is an essential ability of a Data Stream Management System (DSMS). In this paper, motivated by the fact that a user may be interested in an arbitrary range of the data streams, we investigate two important types of range-constrained queries in time series streaming environments: the distance queries (which aim at obtaining the Euclidean distance between two streams) and the kNN queries (which aim at discovering k nearest neighbors to a reference stream). To achieve high efficiency in processing these two types of queries, we propose procedure RED (standing for Range-constrained Euclidean Distance) and algorithm EKS (standing for Enhanced KNN Search). Compared to the existing methods in the prior research, the advantageous features of our approaches are in two folds. First, our approaches are capable of processing the queries directly from the wavelet synopses retained in the main memory without using IDWT to reconstruct the data cells. This feature allows us to save the complexity in both memory and time. Moreover, our approaches enable the users to query the DSMS within their range of interest. Unlike the conventional methods which only support the full-range query processing, this feature will enhance the flexibility at the client side. We evaluate procedure RED and algorithm EKS on live and synthetic datasets empirically and show that the proposed approaches are efficient in similarity search and kNN discovery within arbitrary ranges in the time series streaming environments.

Recently, data broadcasting becomes a promising technique to design a mobile information system with power conservation, high scalability and high bandwidth utilization. To model the data access in advanced wireless networks, the diverse data broadcasting environment allows mobile users to access the items of different sizes. Given the number of broadcast channels, a broadcast program needs to be generated according to the profile which stores the information of probabilities and item sizes. In reality, the access probability of each data object may vary as time goes on. We address in this paper the problem of generating a broadcast program dynamically in the diverse data broadcasting environment. We propose an efficient algorithm MTS, standing for Merging Then Splitting. Compared to other competitive approaches , algorithm MTS achieves high quality and low complexity. To assess the performance of algorithm MTS, we analyze several important system issues empirically and show that algorithm MTS is best suited for dynamic dissemination in a diverse data broadcasting environment.

In this paper, we discuss the cache replacement policy in a multimedia transcoding proxy. Unlike the cache replacement for conventional Web objects, to replace some elements with others in the cache of a transcoding proxy, we should further consider the inter-relationship among the cached items. To maintain the inter-relationship and to perform cache replacement, we propose in this paper the RESP framework (standing for replacement with shortest path). The RESP framework contains two primary components, i.e., procedure MASP (standing for minimum aggregate cost with shortest path) and algorithm EBR (standing for exchange-based replacement). Procedure MASP maintains the interrelationship using a shortest path table, whereas algorithm EBR performs cache replacement according to an exchanging strategy. The experimental results show that the RESP framework can approximate the optimal cache replacement with much lower execution time for processing user queries

In recent years, data broadcasting becomes a promising technique to design a mobile information system with power conservation, high scalability and high bandwidth utilization. However, prior reseach topics in data broadcasting are mainly based on the assumption that the disseminated data items are of the same size. We explore in this paper the problem of generating broadcast programs in a diverse data broadcasting environment, in which disseminated data items can be of different sizes. Given the broadcast database and the channel number, we propose algorithms DRP (Dimemsion Reduction Partitioning) to perform the channel allocation for each data item. Moreover, a Cost-Diminishing Selection mechanism is also used to help DRP achieve the local optimum with low complexity. With the capability of generating effective broadcast programs efficiently, the proposed mechanism can be practically used in a diverse data broadcasting environment.

Recent technology advances in multimedia communication have ushered in a new era of personal communication. Users can ubiquitously access the Internet via various mobile devices. For the mobile devices featured with lower-bandwidth network connectivity, transcoding can be used to reduce the object size by lowering the quality of a multimedia object. In this paper, we focus on the cache replacement policy in a transcoding proxy, which is a proxy server responsible for transcoding the object and reducing the network traffic. Based on the architecture in prior works, we propose a maximum profit replacement algorithm, abbreviated as MPR. MPR performs cache replacement according to the content in the caching candidate set, which is generated by the concept of dynamic programming. Experimental results show that the the proposed MPR outperforms the prior scheme in terms of the cache hit ratio.

Hybrid data dissemination, which combines the push-based (i.e., broadcast) and pull-based (i.e., on-demand) data delivery, is the most common technique to deliver information in a mobile computing system. Most of the prior works in hibrid dissemination are based on the assumption that each delivered data item is of the same size. However, in the modern communication environment in which various information is delivered, the conventional dissemination schemes suffer from the efficiency issues. In this paper, we consider a general model of hybrid data dissemination, in which each data item is allowed to have an arbitrary size. The analytical model MGBC (Model of General Broadcast Channels) and MGOD (Model of General On-demand Channels) are first proposed to describe the broadcast and on-demand channels, respectively. In addition, the scheme GDS (General Dissemination Scheme) is adopted to perform the channel allocation and the data classification. Experimental results show that the proposed approach gives a near-optimal solution in achieving the minimun access time in the general dissemination environment.

Data dissemination by the use of periodic broadcast is an important scalable data delivery mechanism due to the explosion of wireless information services. Consider that the recent emergence of a multichannel data dissemination model has attracted much research attention. In this paper, we devise an on-line multichannel traffic awareness (MCTA) mechanism by exploiting the notion of client impatience against traffic dynamics in a multichannel data dissemination environment. The design of MCTA performs a cyclical estimation of dynamic access frequency distribution in response to dynamic traffic changes. The experimental results show that MCTA is not only able to attain an estimated distribution of high accuracy, but also of low complexity and robustness without performance degradation.