Springer Science and Business Media LLC

Frequent Itemset mining is time consuming for large datasets. Many parallel frequent itemset mining algorithms have been proposed to speed up the mining process. This paper presents a parallel frequent itemset mining algorithm on a cluster of personal computers. To facilitate parallel frequent itemset mining, we use prefix path based method to decompose a transactional dataset into its frequent 1-itemset sub-datasets. We called the parallel frequent itemset mining algorithm based on the frequent 1-itemset sub-dataset decomposition the single-level parallel frequent itemset mining algorithm (SLPFIM) in our PC cluster platform. To mitigate the bottleneck caused by time-consuming 1-itemset sub-datasets, we propose a multi-level parallel frequent itemset mining (MLPFIM) algorithm to further decompose the time-consuming 1-itemset sub-datasets into their corresponding sub-sub-datasets. The fine granule of the sub-sub-datasets enhances the load balancing in parallel frequent itemset mining. The experimental results showed that the SLPFIM offered a maximum of 11.9x speedup over the non-parallel execution of the FP-Growth algorithm while the MLPFIM achieved a maximum of 23.1x speedup over the non-parallel execution of the FP-Growth algorithm. The experimental results also showed that the MLPFIM offered a maximum of 2.14x speedup over the SLPFIM.

Cloud computing can leverage over-provisioned resources that are wasted in traditional data centers hosting production applications by consolidating tasks with lower QoS and SLA requirements. However, the dramatic fluctuation of workloads with lower QoS and SLA requirements may impact the performance of production applications. Frequent task eviction, killing and rescheduling operations also waste CPU cycles and create overhead. This paper aims to schedule hybrid workloads in the cloud data center to reduce task failures and increase resource utilization. The multi-prediction model, including the ARMA model and the feedback based online AR model, is used to predict the current and the future resource availability. Decision to accept or reject a new task is based on the available resources and task properties. Evaluations show that the scheduler can reduce the host overload and failed tasks by nearly 70%, and increase effective resource utilization by more than 65%. The task delay performance degradation is also acceptable.

Multimedia applications are being developed and used for many aspects of our lives today. New high-speed, broadband networks have emerged and made the operation of these high-bandwidth requiring applications readily feasible. However, the development of distributed multimedia applications and efficient and reliable operation of these applications are still very difficult. This paper presents a flexible and reliable distributed multimedia collaborative environment called MAESTRO which provides a rich multimedia collaborative service API and which provides distributed multimedia services that can be used to develop a variety of multimedia applications easily. MAESTRO has been designed using CORBA. The system as well as applications running on it are managed and controlled in order to provide a reliable and efficient multimedia operations environment. We validate our claim by developing a number of multimedia applications using our distributed multimedia system and by using them for supporting distributed collaborative scientific and engineering research experiments.

The irreplaceable key to the triumph of Question & Answer (Q & A) platforms is their users providing high-quality answers to the challenging questions posted across various topics of interest. From more than a decade, the expert finding problem attracted much attention in information retrieval research. Based on the encountered gaps in the expert identification across several Q & A portals, we inspect the feasibility of identifying data science experts in Reddit. Our method is based on the manual coding results where two data science experts labelled not only expert and non-expert comments, but also out-of-scope comments, which is a novel contribution to the literature, enabling the identification of more groups of comments across web portals. We present a semi-supervised approach which combines 1113 labelled comments with 100,226 unlabelled comments during training. We proved that it is possible to develop models that can identify expert, non-expert and out-of-scope comments peaking the AUC score at 0.93, accuracy at 0.83, MAE at 0.15 degrees and R2 score at 0.69. The proposed model uses the activity behaviour of every user, including Natural Language Processing (NLP), crowdsourced and user feature sets. We conclude that the NLP and user feature sets contribute the most to the better identification of these three classes. It means that this method can generalise well within the domain. Finally, we make a novel contribution by presenting different types of users in Reddit, which opens many future research directions.

The explosive growth of wireless data services demands higher capacity of future wireless communication systems to meet this trend. One efficient solution to this problem is to look for more spectral resource. To fully exploit the high potential rates of high frequency in mobile networks, in this paper, we propose a novel beamforming method based on iterative adaptive method, pre-defined selection and compressed sensing, effectively reducing the pilot and feedback overhead of mobile broadband communication with high-frequency band, and suppressing inter-cell interference and extending system coverage.

A good estimation with high reliability of substitution parameter is the premise of all kinds of economical analyses with CES function. With great limitation of data availability, more or less compromises were adopted in most domestic literatures for substitution parameter estimation. But no one knows if those compromises are acceptable or not? In this paper, three popular methods to estimate parameters of CES function were compared in theoretical and empirical way, with a special angle of parameter unit. It was found that the unit of parameter directly and remarkably affects the results of estimation, and only the method of estimating with the first order condition (FOC for short) of CES production could produce reasonable and stable estimation of substitution parameter, while the estimation results of the other two methods were deeply involved in the influence of variable unit. Therefore, the FOC model was strongly recommended in most case of substitution parameter evaluation, unless full evidence of its value, zero near enough.

Bluetooth polling, also referred to as Bluetooth MAC scheduling or intra-piconet scheduling, is the mechanism that schedules the traffic between the participants in a Bluetooth network. Hence, this mechanism is highly determining with respect to the delay packets experience in a Bluetooth network. In this paper, we present a polling mechanism that provides delay guarantees in an efficient manner, and we evaluate this polling mechanism by means of simulation. It is shown that this polling mechanism is able to provide delay guarantees while saving as much as possible resources, which can be used for transmission of best effort traffic or for retransmissions.

An integrated scheduling problem under a make-to-order supply chain network is addressed. This problem considers integrated production and transportation scheduling with realistic supply chain features such as unrelated parallel shop and product batch-based transportation. The mathematical model for this problem is presented, which is formulated as a bilevel mixed-integer nonlinear program. A novel bilevel evolutionary optimization model based on memetic algorithm is proposed to resolve this problem because the problem is hard-to-tackle for mathematical programming techniques and traditional intelligent techniques. The effectiveness of the proposed optimization model is validated through a series of numerical experiments. The experimental results also confirmed that the proposed optimization model is superior to other three intelligent optimization models.

To span administrative boundaries, metacomputing systems require the integration of strong authentication and authorization methods. The problem is complicated because different components of the system may have different security policies. This paper presents a distributed model for authorization that we have integrated with the Prospero Resource Manager, a metacomputing resource allocation system developed at USC. The integration of authorization with PRM was accomplished through the specification of a policy language and the use of a Generic Authorization and Access-control API (GAA API). The language supports the specification of diverse authorization policies including ACLs, capabilities and lattice-based access controls. The GAA API provides a uniform authorization service interface for facilitating access control decisions and requesting authorization information about a particular resource. We describe a prototype of our system.