With the advent of the Internet, we are seeing more sophisticated techniques being developed to support e-Iearning. The rapid developme nt of Web-based learning and new concepts like virtual classrooms, virtual laboratories and virtual universities introduces many new issues to be addressed. On the technical side, we need to develop effective e-technologies for supporting distance education. On the learning and management side, we need to consider issues such as new style of learning and different system set-u p requirements. Finally, the issue of standardization of e-Iearning systems should also be considered. In this special issue, our focus will be on the technical side, although other issues related to knowledge and data engineering for e-Iearning may also be considered. Topics: In this special issue, we call for original papers describing novel knowledge and data engineering techniques that support e-Iearning. Preference will be given to papers that include an evaluation of users' experience in using the proposed methods. Areas of interests include, but are not limited to: • Semantic Web technology for e-Iearning • Data modeling (eg., XML) for efficient management of course materials • Searching and indexing techniques to suppo rt effective course notes retrieval • User-centric e-Iearning systems and user interaction management • Profiling techniques to support grading and learning recommendation • Data and knowledge base suppo rt for pervasive e-Iearning • Course material analysis and understanding • Automatic generation of questions and answers • Collaborative communities for e-Iearning

Knowledge and Data Engineering for e-Learning Special Issue of IEEE Transactions on Knowledge and Data Engineering

In ad hoc networks, due to frequent network partition, data accessibility is lower than that in conventional fixed networks. In this paper, we solve this problem by replicating data items on mobile hosts. First, we propose three replica allocation methods assuming that each data item is not updated. In these three methods, we take into account the access frequency from mobile hosts to each data item and the status of the network connection. Then, we extend the proposed methods by considering aperiodic updates and integrating user profiles consisting of mobile users' schedules, access behavior, and read/write patterns. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods

/pdf/data-replication-for-improving-data-accessibility-in-ad-hoc-2od73ih5nt.pdf

Data Replication for Improving Data Accessibility in Ad Hoc Networks

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We review the related work of PSPM in details including partition-based algorithms for PSPM, apriori-based PSPM, pattern-growth-based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages, and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative/weighted/utility SPM, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.

A Survey of Parallel Sequential Pattern Mining

The continuous development of wireless networks and mobile devices has motivated an intense research in mobile data services. Some of these services provide the user with context-aware information. Specifically, location-based services and location-dependent queries have attracted a lot of interest.In this article, the existing literature in the field of location-dependent query processing is reviewed. The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and location-dependent queries are defined and classified, and different query processing approaches are reviewed and compared.

https://webdiis.unizar.es/~silarri/PUBLICATIONS/acmSurvLDQP09.pdf

Location-dependent query processing: Where we are and where we are heading

This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, consid- ering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environ- ments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation pur- poses, and by taking into account the context dimensions.

/pdf/spatial-models-for-context-aware-indoor-navigation-systems-a-5958zlohw4.pdf

Spatial models for context-aware indoor navigation systems: A survey

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.

Dynamic data management for location based services 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.

Effective location based services with dynamic data management 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.

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

Frequent item set mining(FIM) is an important research topic because it is widely applied in real world to find the frequent item sets and to mine human behavior patterns. FIM process is both memory and compute-intensive. As data grows exponentially every day, the problems of efficiency and scalability become more severe. In this paper, we propose a new distributed FIM algorithm, called Sequence-Growth, and implement it on MapReduce framework. Our algorithm applies the idea of lexicographical order to construct a tree, called "lexicographical sequence tree", that allows us to find all frequent item sets without exhaustive search over the transaction databases. In addition, the breadth-wide support-based pruning strategy is also an important factor to contribute the efficiency and scalability of our algorithm. To test the performances of our algorithm, we conduct varied aspects of experiments on MapReduce framework with large datasets. The results show the good efficiency and scalability of Sequence-Growth especially to deal with big data and long item sets. Our algorithm also proposes a new mining methodology which can be easily modified for sequential pattern mining, trajectory pattern mining and other associate rule mining algorithms. We believe that it should have a valuable contribution in the future development of association rule mining algorithms for big data.

Sequence-Growth: A Scalable and Effective Frequent Itemset Mining Algorithm for Big Data Based on MapReduce Framework

A replication scheme determines the number and location of replicas in a distributed system. Traditional static replication schemes do not perform well in mobile environments due to the assumptions of fixed hosts, relatively static access patterns, and the lack of considerations on users' behalf. For effective data management in mobile environments, we define the notion of activity-data dependency for inferring the information requirement of mobile users based on their scheduled activities. We then propose a dynamic replication scheme which employs user profiles for recording users' mobility schedules, accesses behaviors and read/write patterns, and actively reconfigures the replicas to adapt to changes in user locations, data requests, and system status. Simulation results demonstrate that the scheme can accurately predict the data requirement to facilitate effective replication, reduce response time, and increase data availability

Shiow-yang Wu

Papers

Dynamic data management for location based services in mobile environments

Effective location based services with dynamic data management in mobile environments

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

Sequence-Growth: A Scalable and Effective Frequent Itemset Mining Algorithm for Big Data Based on MapReduce Framework

A User-Centered Approach to Active Replica Management in Mobile Environments