Professional development for school administrators : computer applications in data communication

This action research project set out to develop the competence of senior personnal from a private vocational college in Thailand in the use of administrative computer systems. The findings demonstrate the critical significance of progressive incremental learning that is tailored to the professional personal needs of learners. Learner competence was found to be dependent upon the creation of an environment promoting learner confidence.

Data monitoring sensor network for bignet research testbed

Equipped with recent advances in electronics and communication, wireless sensor networks gained a rapid development to provide reliable information with higher Quality of Service (QoS) at lower costs. This paper presents a realtime tracking system developed as a part of the ISSNIP BigNet Testbed project. Here a GPS receiver was used to acquire position information of mobile nodes and GSM technology was used as the data communication media. Moreover, Google map based data visualization software was developed to locate the mobile nodes via Internet. This system can be used to accommodate various sensors, such as temperature, pressure, pH etc., and monitor the status of the nodes.

A parallel random forest algorithm for big data in a spark cloud computing environment

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability.

A simulation model for video traffic performance via ATM over TCP/IP

Although TCP has emerged as the standard in data communication, the introduction of ATM technology has raised numerous problems regarding the effectiveness of using TCP over A TM networks, especially when video traffic performance is considered. This paper presents a simulation model for transmission performance of video traffic via ATM over TCP/IP. The interactivity between TCP/IP and ATM, generation of MPEG traffic and evaluation of traffic performance are implemented in the model. The design and implementation details of the model are carefully described. The experiments conducted using the model and experimental results are briefly introduced, revealing the capability of our model in simulating network events and in evaluating potential solutions to performance issues.

Mining inconsistent secure messages toward analyzing security protocols

Traditional approaches such as theorem proving and model checking have been successfully used to analyze security protocols. Ideally, they assume the data communication is reliable and require the user to predetermine authentication goals. However, missing and inconsistent data have been greatly ignored, and the increasingly complicated security protocol makes it difficult to predefine such goals. This paper presents a novel approach to analyze security protocols using association rule mining. It is able to not only validate the reliability of transactions but also discover potential correlations between secure messages. The algorithm and experiment demonstrate that our approaches are useful and promising.

S-Kcore : a social-aware Kcore decomposition algorithm in pocket switched networks

The key nodes in network play the critical role in system recovery and survival. Many traditional key nodes selection algorithms utilize the characters of the physical topology to find the key nodes. But they can hardly succeed in the mobile ad hoc network due to the mobility nature of the network. In this paper we propose a social-aware Kcore selection algorithm to work in the Pocket Switched Network. The social view of the network suggests the social position of the mobile nodes can help to find the key nodes in the Pocket Switched Network. The S-Kcore selection algorithm is designed to exploit the nodes' social features to improve the performance in data communication. Experiments use the NS2 shows S-Kcore selection algorithm workable in the Pocket Switched Network. Furthermore, with the social behavior information, those key nodes are more suitable to represent and improve the whole network's performance.

Digital business reporting standards: mapping the battle in France

Government agencies use information technology extensively to collect business data for regulatory purposes. Data communication standards form part of the infrastructure with which businesses must conform to survive. We examine the development of, and emerging competition between, two open business reporting data standards adopted by government bodies in France; Electronic Data Interchange for Administration, Commerce and Transport (EDIFACT) (incumbent) and eXtensible Business Reporting Language (XBRL) (challenger). The research explores whether an incumbent may be displaced in a setting in which the contest is unresolved. Latour's translation map is applied to trace the enrollments and detours in the battle. We find that regulators play an important role as allies in the development of the standards. The antecedent networks in which the standards are located embed strong beliefs that become barriers to collaboration and fuel the battle. One of the key differentiating attitudes is whether speed is more important than legitimacy. The failure of collaboration encourages competition. The newness of XBRL's technology just as regulators need to respond to an economic crisis and its adoption by French regulators not using EDIFACT create an opportunity for the challenger to make significant network gains over the longer term. ANT also highlights the importance of the preservation of key components of EDIFACT in ebXML.

A general communication cost optimization framework for big data stream processing in geo-distributed data centers

With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.

Space-crossing : community-based data forwarding in mobile social networks under the hybrid communication architecture

In this paper, we study two tightly coupled issues, space-crossing community detection and its influence on data forwarding in mobile social networks (MSNs). We propose a communication framework containing the hybrid underlying network with access point (AP) support for data forwarding and the base stations for managing most of control traffic. The concept of physical proximity community can be extended to be one across the geographical space, because APs can facilitate the communication among long-distance nodes. Space-crossing communities are obtained by merging some pairs of physical proximity communities. Based on the space-crossing community, we define two cases of node local activity and use them as the input of inner product similarity measurement. We design a novel data forwarding algorithm Social Attraction and Infrastructure Support (SAIS), which applies similarity attraction to route to neighbor more similar to destination, and infrastructure support phase to route the message to other APs within common connected components. We evaluate our SAIS algorithm on real-life datasets from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs. Based on this new type of community, SAIS achieves a better performance than existing popular social community-based data forwarding algorithms in practice, including Simbet, Bubble Rap and Nguyen's Routing algorithms.

An efficient replicated data management approach for peer-to-peer systems

The availability of critical services and their data can be significantly increased by replicating them on multiple systems connected with each other, even in the face of system and network failures. In some platforms such as peer-to-peer (P2P) systems, their inherent characteristic mandates the employment of some form of replication to provide acceptable service to their users. However, the problem of how best to replicate data to build highly available peer-to-peer systems is still an open problem. In this paper, we propose an approach to address the data replication problem on P2P systems. The proposed scheme is compared with other techniques and is shown to require less communication cost for an operation as well as provide higher degree of data availability.

Development of a condition-specific measure of quality of life for children with cerebral palsy: empirical thematic data reported by parents and children

Background: Although there is increasing recognition that quality of life (QOL) and health-related quality of life (HRQOL) are important outcome variables in clinical trials for children with cerebral palsy, there are substantial limitations in existing measures of QOL. This study identify themes of QOL for children with cerebral palsy and their parents to guide the development of a new condition-specific QOL scale. Methods: A qualitative study of parent and child views on QOL composition was conducted, using a grounded theory framework. Families participated in semistructured interviews on QOL until thematic saturation was reached (n = 28 families). Results: Overall, 13 themes emerged from the interviews: physical health, body pain and discomfort, daily living tasks, participation in regular physical and social activities, emotional well-being and self-esteem, interaction with the community, communication, family health, supportive physical environment, future QOL, provision of, and access to services, financial stability, and social well-being. Conclusions: Research with parents and children with cerebral palsy, representative of severity across the disease spectrum and socio-economic status, reinforced and expanded on the traditional themes that have underpinned QOL measurement development. This has implications not only for the development of a new QOL scale for children with cerebral palsy, but also for clinical interventions and community care management.

A novel data replication and management protocol for mobile computing systems

Mobile computing has enabled users to seamlessly access databases even when they are on the move. Mobile computing environments require data management approaches that are able to provide complete and highly available access to shared data at any time from any where. In this paper, we propose a novel replicated data protocol for achieving such goal. The proposed scheme replicates data synchronously over stationary sites based on three dimensional grid structure while objects in mobile sites are asynchronously replicated based on commonly visited sites for each user. This combination allows the proposed protocol to operate with less than full connectivity, to easily adapt to changes in group membership and not require all sites to agree to update data objects at any given time, thus giving the technique flexibility in mobile environments. The proposed replication technique is compared with a baseline replication technique and shown to exhibit high availability, fault tolerance and minimal access times of the data and services, which are very important in an environment with low-quality communication links.

Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.