Shiow-yang Wu

Dynamic data management for location based services in mobile environments

We characterize the dynamic data management problem for location based services (LBS) in mobile environments and devise a cost model for servicing both location independent and location dependent data. The cost analysis leads to a set of dynamic data management strategies that employs judicious caching, proactive server pushing and neighborhood replication to reduce service cost and improve response time under changing user mobility and access patterns. Simulation results suggest that different strategies are effective for different types of data in response to different patterns of movement and information access.

Headlight Prefetching and Dynamic Chaining for Cooperative Media Streaming in Mobile Environments

Media streaming in mobile environments is becoming more and more important with the proliferation of 3G technologies and the popularity of online media services such as news clips, live sports, and hot movies. To avoid service interruptions, proper data management strategies must be taken by all parties. We propose a two-level framework and cooperative techniques for mobile media streaming. Headlight prefetching is for the cooperation of streaming access points to deal with unpredictable client movement and seamless hand-off. For each user, we maintain a virtual fan-shaped prefetching zone along the direction of movement similar to a vehicle headlight. The overlapping area and accumulated virtual illuminance of the headlight zone on a particular cell determine the degree and volume of prefetching on that cell. Dynamic chaining facilitates cooperation among users to maximize cache utilization and streaming benefit. On receiving a request from a client, the streaming access point starts a search for supplying partners before attempting to a remote media server. If a qualified partner is found, the client is chained to the partner and receives subsequent segments without server intervention. The client can itself be a supplying partner for other clients and naturally form a chain of users that are viewing and sharing the same media. Simulation results demonstrate that headlight prefetching and dynamic chaining can significantly decrease streaming disruptions, reduce bandwidth consumption, increase cache utilization and improve service response time.

Effective location based services with dynamic data management in mobile environments

With the proliferation of mobile computing technologies, location based services have been identified as one of the most promising target application. We classify mobile information service domains based on feature characteristics of the information sources and different patterns of mobile information access. By carefully examining the service requirements, we identify the dynamic data management problem that must be addressed for effective location based services in mobile environments. We then devise a general architecture and cost model for servicing both location independent and location dependent data. Based on the architecture and cost model, we propose a set of dynamic data management strategies that employs judicious caching, proactive server pushing and neighborhood replication to reduce service cost and improve response time under changing user mobility and access patterns. Detail behavior analysis helps us in precisely capturing when and how to apply these strategies. Simulation results suggest that different strategies are effective for different types of data in response to different patterns of movement and information access.

QoS-Aware Dynamic Adaptation for Cooperative Media Streaming in Mobile Environments

Media streaming is expected to be one of the most promising services in mobile environments. Effective data streaming management techniques are, therefore, in strong demand. In an earlier paper, the ideas and benefits of two-level cooperative media streaming with headlight prefetching and dynamic chaining were demonstrated. Though complementary to each other, they operate in session-wide static and distinctive modes. Moreover, users do not have control over the quality and cost levels of the streaming services. The performance degradation or cost increment can reach an unacceptable level under fast or highly unstable moving patterns. In this paper, we propose the QoS-based dynamic adaptation techniques for the flexible employment and smooth integration of headlight prefetching and dynamic chaining to continuously provide quality streaming services to mobile users. The QoS-aware dynamic headlight prefetching is for the cooperation between streaming access points to dynamically adjust the prefetching scheme in response to the fast changing moving patterns. Adaptive P2P media streaming is for the cooperation between mobile users such that multiple peers can be used as streaming sources to increase the likelihood of successful chaining. Furthermore, a QoS-based technique is developed to dynamically trigger and proportionally adjust the prefetching degree when the stability and quality of P2P streaming service vary. With extensive simulation and performance evaluation, we demonstrate that the proposed dynamic adaptation techniques significantly improve the service quality and streaming performance of cooperative media streaming in mobile environments.

Rule-based intelligent adaptation in mobile information systems

The ability to adapt to changes is among the most important issues in mobile information systems. We propose a rule-based modular framework for building self-adaptive applications in mobile environments. A common mobile event engine for all applications is employed to detect the status changes. The rule system plays the role of a reactive component for making proper decisions in response to the changes. A general database interface ensures seamless integration of the rule system with database systems for flexible information access. For efficient rule processing, we develop techniques that combine static and dynamic analysis to uncover phase structure and data access semantics of a rule program. The semantic information is used to facilitate intelligent caching and prefetching for conserving limited bandwidth and reducing rule processing cost. A distributed data management and rule execution strategy enable adaptive information services over changing environment, even under disconnection. We devise a performance model to characterize the exact condition for our approach to be superior than traditional approach. Trace-driven simulation results successfully demonstrate the feasibility and potential of our approach. The modularity of the framework permits fast implementation based on off-the-shelf rule systems and database packages. We present a prototype implementation and preliminary evaluation results to show that our techniques can be effectively materialized.

Activity-Based Proactive Data Management in Mobile Environments

Most users in a mobile environment are moving and accessing wireless services for the activities they are currently engaged in. We propose the idea of complex activity for characterizing the continuously changing complex behavior patterns of mobile users. For the purpose of data management, a complex activity is modeled as a sequence of location movement, service requests, the cooccurrence of location and service, or the interleaving of all above. An activity may be composed of subactivities. Different activities may exhibit dependencies that affect user behaviors. We argue that the complex activity concept provides a more precise, rich, and detail description of user behavioral patterns which are invaluable for data management in mobile environments. Proper exploration of user activities has the potential of providing much higher quality and personalized services to individual user at the right place on the right time. We, therefore, propose new methods for complex activity mining, incremental maintenance, online detection and proactive data management based on user activities. In particular, we devise prefetching and pushing techniques with cost-sensitive control to facilitate predictive data allocation. Preliminary implementation and simulation results demonstrate that the proposed framework and techniques can significantly increase local availability, conserve execution cost, reduce response time, and improve cache utilization.

Headlight prefetching for mobile media streaming

Multimedia information services in mobile environments are becoming more and more important with the proliferation of 3G technologies. Media streaming, in particular, is a promising technology for providing services such as news clips, live sports, and hot movies on the fly. To avoid service interruption when the users keep moving, proper data management strategies must be employed. We propose a new headlight prefetching technique for the streaming access points to deal with the uncertainty of client movement and the requirement of seamless service hand-off. For each mobile client, we maintain a virtual fan-shaped prefetching zone along the direction of movement similar to the headlight of a moving vehicle. The overlapping area and the accumulated virtual illuminance of the headlight zone on a particular cell determines the degree and volume of prefetching to be made by the streaming access point of that cell. Headlight prefetching solves the issues of identifying the streaming access points responsible for prefetching, the timing and the amount of data to prefetch in a single mechanism which is simple and effective. Simulation results demonstrate that our techniques can significantly decrease streaming disruptions, reduce bandwidth consumption, increase cache utilization and improve service response time.

Decomposition abstraction in parallel rule languages

Decomposition abstraction is the process of organizing and specifying decomposition strategies for the exploitation of parallelism available in an application. In this paper we develop and evaluate declarative primitives for rule-based programs that expand opportunities for parallel execution. These primitives make explicit, implicit relations among the data and similarly among the rules. The semantics of the primitives are presented in a general object-based framework such that they may be applied to most rule-based programming languages. We show how the additional information provided by the decomposition primitives can be incorporated into a semantic-based dependency analysis technique. The resulting analysis reveals parallelism at compile time that is very difficult, if not impossible, to discover by traditional syntactic analysis techniques. Simulation results demonstrate scalable and broadly available parallelism.

Structured design, consistency analysis and failure reasoning of business workflows with activity-control templates and causal ordering

The design, analysis, control and diagnosis of business workflows have been major challenges for enterprise information system designers. We propose a structured framework for workflow design, formal semantics, consistency analysis, execution automation and failure reasoning targeting E-commerce applications. A business workflow is modeled by using a visual tool named activity-control (AC) diagram. Frequently occurring business procedures are captured by the adoptions of reusable AC templates. With formally defined semantics by a combination of first-order logic and happen-before causal ordering in distributed system theory, workflow consistency can be mechanically analyzed at design time while failure reasoning can be applied at execution time for problem diagnosis. A completely specified model is automatically converted to a workflow by an iterative traversal algorithm that maps an AC diagram to an XML workflow specification which can then be executed automatically by an XML workflow engine. A failure reasoning and diagnosis algorithm is devised to find all possible causes of a failed execution when problems occur. Preliminary proof-of-concept implementation and evaluation results demonstrate the feasibility and effectiveness of our framework and techniques.

Explicit parallel structuring for rule-based programming

This paper presents semantically-based explicit parallel structuring for rule-based programming systems. Explicit parallel structuring appears to be necessary since compile-time dependency analysis of sequential programs has not yielded large scale parallelism and run-time analysis for parallelism is restricted by the execution cost of the analysis. Simple language extensions specifying semantics of rules are used to define parallel execution behavior at the rule level. Type definitions for working memory elements are extended to include relationships within and among objects which define the parallelism allowed on instances of object types. The first result presented is that the algorithms implemented by commonly used benchmark rule-based programs contain scalable parallelism. The second result is that much of that parallelism can be captured by simple and modest extensions of rule-based languages which are analogies of models and constructs used for specification of parallel structures in imperative programming languages. A sketch is given for a comprehensive language system which exploits specification of semantics defining parallel structures in both object-definition and executable segments of rule-based programs.<<ETX>>