International Journal of Information Technology and Management

Includes bibliographical references.

Parallel naïve Bayes regression model-based collaborative filtering recommendation algorithm and its realisation on Hadoop for big data -- A fuzzy inference-based trust model estimation system for service selection in cloud computing -- Identifying inter-organisational resource-service sequences based on similarity for collaborative tasks -- An EMD-SVM model with error compensation for short-term wind speed forecasting -- Facilitating social recommendation with collaborative topic regression and social trust -- A collusion-resistant public auditing scheme for shared cloud data -- Cost and green aware workload migration on geo-distributed datacentres -- SGP: a social network sampling method based on graph partition -- A semi-supervised approach of graph-based with local and global consistency -- Diabetes index evaluation framework based on data mining technology -- Voice transmission through WiFi -- Study on image feature recognition algorithm and its application in public security management -- Needle in a haystack: an empirical study on mining tags from Flickr user comments -- Threefold similarity analysis: a case study on crowdsourcing feeds.

[Article Title: Parallel naïve Bayes regression model-based collaborative filtering recommendation algorithm and its realisation on Hadoop for big data/ Shiqi Wen,Cheng Wang,Haibo Li and Guoqi Zheng, p. 129-142] Abstract: Collaborative filtering (CF) algorithms are widely used in a lot of recommender systems. However, space-time overhead and high computational complexity hinder their use in large-scale systems. This paper implements the parallel naïve Bayes regression model based collaborative filtering recommendation algorithm on Hadoop computing platform to scalability problem of CF. Firstly, this paper analysis the inherent parallelism of the naive Bayesian regression model and constructs the theoretical model of naive Bayesian parallelisation. Secondly, the parallel naïve Bayes regression model-based collaborative filtering recommendation algorithm is realised on Hadoop platform with distributed Hadoop distributed file system (HDFS) and MapReduce as the transparent distributed infrastructure. And its temporal-spatial overhead, speedup is discussed. Finally, applying parallel the naïve Bayes regression model-based collaborative filtering recommendation algorithm to a large dataset. The experiment results on Netflix dataset show that this method has high scalability and less space-time overhead, which is suitable for real-time recommendation on large dataset.;[Article Title: A fuzzy inference-based trust model estimation system for service selection in cloud computing/ Roney Thomas,Priya Govindaraj and Jaisankar Natarajan, p. 143-155] Abstract: Cloud computing assures to be the fundamental changeover in the evolution of the computing world. The cloud computing helps the users to have no Capex, which is making a lot of businesses and individuals to it. Many services are provided by the cloud, for users to meet their application's functional as well as non-functional. Due to the vast number of available services, ambiguous requirements, security and trust measures and efficiency provided by different cloud providers, it is often difficult for the users to select the cloud services. This paper proposes a system that assesses trust of cloud services by providers using a fuzzy-based inference system for selecting the services dynamically. ;[Article Title: Identifying inter-organisational resource-service sequences based on similarity for collaborative tasks/ Haibo Li and Mengxia Liang, p. 156-170] Abstract: To improve the efficiency of a collaborative task, collaboration of resource services in a business process is important. From the business process viewpoint, the resource services should be provided as service flows to business processes. Resource services are selected and used by different organisations. This reduces the efficiency of the collaboration of resource services among different organisations. To solve this problem, a similarity based approach is proposed to identify the resource service sequences in an inter-organisational business process. Manufacturing is used as an example to discuss the problem. First, a modelling method, resource service temporal relationship modelling (RSTM), is presented. In RSTM, the temporal relationship of resource services is described, which is resolved according to the big data of business. Then, based on the RSTM, all resource service sequences are obtained directly. Next, an algorithm of similarity is presented to calculate the isomorphic resource service sequences with inter-organisation consideration. Finally, the proposed approach is tested with a simulation experiment, and the results show that it is very promising. ;[Article Title: An EMD-SVM model with error compensation for short-term wind speed forecasting/ Yuanyuan Xu,Tianhe Yao and Genke Yang, p. 171-181] Abstract: In this paper, we propose an empirical mode decomposition-support vector machine (EMD-SVM) model with error compensation in order to reduce the cumulative error and improve the prediction accuracy of short-term wind speed forecasting. The essential idea behind the proposed approach is that the error of the current prediction is highly correlated with the previous prediction errors, and the forecasted speed should be compensated in terms of the errors incurred from previous predictions. Specifically, we first predict the historical data by the EMD-SVM model so as to obtain the corresponding prediction errors. Then, we establish the error compensation mechanism. Finally, we combine the EMD-SVM model with error compensation to obtain the final prediction results. The error compensation strategy is validated by a series of actual 10 min wind speed data collected from New Zealand. Experimental results demonstrate that the proposed EMD-SVM model with error compensation can be successfully applied to short-term wind speed forecasting, and it has higher accuracy and stronger robustness compared with the method without error compensation.;[Article Title: Facilitating social recommendation with collaborative topic regression and social trust/ Xiaoyi Deng and Feifei Huangfu, p. 182-194] Abstract: Social networks make users more dependent on online information regarding purchasing decision making. Networks which make users more dependent on online information regarding purchasing decision making. Therefore, social network information can be utilised to improve the performance of recommender systems that aim to mitigate information overload and provide users with the most attractive and relevant items. To improve recommender systems by incorporating social network information, this paper exploits multi-sourced information to predict ratings and make recommendations. An improved collaborative topic regression model that incorporates social trust, in which users' decisions regarding ratings are affected by their preferences and the favours of trusted friends, is proposed. In addition, an approach to calculating the maximum a posteriori estimates is proposed to learn model parameters. Empirical experiments using two real-world datasets are conducted to evaluate the performance of our model. The results indicate that the proposed model has better accuracy and robustness than other methods for making recommendations.;[Article Title: A collusion-resistant public auditing scheme for shared cloud data/ Fulin Nan,Hui Tian,Tian Wang,Yiqiao Cai and Yonghong Chen, p. 195-212] Abstract: With the increasing popularity of collaboration in the cloud, shared data have become a new branch of cloud data, which also brings new challenges for remote integrity auditing. To address the concerns, this paper presents a novel public auditing scheme for shared data. Differing from the existing works, we introduce a new entity called local authentication server to finalise the block tags of shared data, which can thereby prevent the collusion attack effectively. Moreover, thanks to the new mechanism of tag generation, our scheme relieves the user manager of the burden of management and largely reduces the computation and communication overheads. In addition, we extend the scheme to support batch auditing by employing the aggregate BLS signature technique. The theoretical proof and experimental evaluation demonstrate that the proposed scheme can provide excellent security and outperform the previous ones in computational costs in the user revocation phase.;[Article Title: Cost and green aware workload migration on geo-distributed datacentres/ Jiacheng Jiang,Yingbo Wu,De Xiang,Keqin Yu and Tianhui Wang, p. 213-226] Abstract: With the development of the inter-datacentre (inter-DC) virtual machine migration technology, it is possible to reduce the cost of electricity and the environment by using the workload migration across the datacentre. This paper presents a solution - cost and green aware workload migration algorithm (CGWM) that utilising the difference of electricity prices, CO2 emissions and water consumption between different geographical locations to manage the workload. CGWM attempts to reduce electricity costs, carbon emissions and water consumption. When the three optimisation goals conflict, CGWM first to ensure the reduction of electricity cost, and then by adjusting the weight factor to make CGWM more biased to optimise the carbon dioxide or water consumption.

Simulation results show CGWM can reduce electricity costs while controlling carbon dioxide emissions and water consumption.;[Article Title: SGP: a social network sampling method based on graph partition/ Xiaolin Du,Dan Wang,Yunming Ye,Yan Li and Yueping Li, p. 227-242] Abstract: A representative sample of a social network is essential for many internet services that rely on accurate analysis. A good sampling method for social network should be able to generate small sample network with similar structures and distributions as its original network. In this paper, a sampling algorithm based on graph partition, sampling based on graph partition (SGP), is proposed to sample social networks. SGP firstly partitions the original network into several sub-networks, and then samples in each sub-network evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. Finally, we evaluate SGP on several well-known datasets. The experimental results show that SGP method outperforms seven state-of-the-art methods. ;[Article Title: A semi-supervised approach of graph-based with local and global consistency/ Yihao Zhang,Junhao Wen,Zhi Liu and Changpeng Zhu, p. 243-255] Abstract: An approach of graph-based semi-supervised learning is proposed that consider the local and global consistency of data. Like most graph-based semi-supervised learning, the algorithm mainly focused on two key issues: the graph construction and the manifold regularisation framework. In the graph construction, these labelled and unlabelled data are represented as vertices encoding edges weights with the similarity of instances, which means that not only the local geometry information but also the class information are utilised. In manifold regularisation framework, the cost function contains two terms of smoothness constraint and fitting constraint, it is sufficiently smooth with respect to the intrinsic structure revealed by known labelled and unlabelled instances. Specifically, we design the algorithm that uses the normalised Laplacian eigenvectors, which ensure the cost function can converge to closed form expression and then, we provide the convergence proof. Experimental results on various datasets and entity relationship classification show that the proposed algorithm mostly outperforms the popular classification algorithm.;[Article Title: Diabetes index evaluation framework based on data mining technology/ Yao Wang,Dianhui Chu and Mingqiang Song, p. 256-267] Abstract: With the development of data mining, scientists began to apply information technology to solve medical problems. In this context, the idea of auxiliary medical service emerged. The purpose of this study is to propose a new framework predicting the probability of suffering from diabetes via diabetes index (DI), which is defined as a score to assess the diabetes-related risk of the participant. DI is calculated based on a diabetic clinical dataset and the SVM model is applied as well. Particularly, genetic feature is innovatively introduced as an important factor in view of the fact that people with family history are more vulnerable to diabetes. The framework is applied to implement a diabetes auxiliary evaluation system. After a set of comprehensive experiments, the assessment result is supposed to identify risk of the disease at an early stage, which contributes to a deeper understanding of one's own health conditions.;[Article Title: Voice transmission through WiFi/ Shalini Goel,Vipul Garg,Deepak Garg and Manshiv Kathait, p. 268-283] Abstract: In an era of digital communication, one of the key requirements is of free connectivity. In addition, one of the most anticipated issues is poor connectivity in most of the areas and it is not possible to install infrastructure-based networks due to cost-effectiveness or non-vulnerability terrains (cellular blind spots like a desert, battlefields, forests, etc.). In the light of the above-mentioned discussion, an android application has been developed in the ongoing project for Android-based wireless devices named WiFi_Intercom. WiFi_Intercom uses classes which allow its user to connect with other connected users through WiFi wireless standard using point to point (P2P) or WLAN connection as a means of communication between Android-based wireless devices. The application will also allow the mobile user to search and call other connected users within the WiFi range through the application. Each mobile device connects to a WLAN router and identifies itself in the routing table. ;[Article Title: Study on image feature recognition algorithm and its application in public security management/ Xiaoyi Yang,Qian Wu and Xinmei Deng, p. 284-296] Abstract: Public security is the topic of common concern of the government and the common people. In order to solve the puzzle of image distortion, being complex in algorithm and being difficult to take into account of the overall structure and details of the image in the image recognition algorithm of public security management system, the paper presented a fusion algorithm of texture consistency measure based on bi-orthogonal wavelet transform. By means of the orthogonal wavelet transform, the wavelet transform is used to decompose the source image, and then the low frequency and high frequency wavelet coefficient matrix of the fused image is determined according to a certain proportion and texture measure, thus the fusion image is obtained. The experimental results show that the algorithm can not only distinguish the false edges of the image, but also enrich the details of the image and take into account the overall visual image, so it can better improve the recognition effect of the image in the public security management system.;[Article Title: Needle in a haystack: an empirical study on mining tags from Flickr user comments/ Haijun Zhang,Jingxuan Li,Bin Luo and Yan Li, p. 293-326] Abstract: In the Web2.0 era, user generated content has become the main source of information of many popular photo-sharing websites such as Flickr. In Flickr, many photos have very few or even no tags, because only the uploader can mark tags for a photo. Meanwhile, the user can deliver his/her comment on the photo, which he/she is browsing. Therefore, it is possible to recommend new tags or enrich the existing tag set based on user comments. The work of this paper contains two phases, i.e., the tag generation, and the ranking algorithm. In the phase of candidate tags generation, two methods are introduced relying on natural language processing (NLP) techniques, namely word-based and phrase-based. In ranking and recommending tags, we proposed an algorithm by jointly modelling the location information of candidate tags, statistical information of candidate tags and semantic similarity between candidate tags. Extensive experimental results demonstrate the effectiveness of our method.;[Article Title: Threefold similarity analysis: a case study on crowdsourcing feeds/ Kaixu Liu,Gianmario Motta,Tianyi Ma and Ke Fan, p. 327-345] Abstract: Crowdsourcing is a valuable social sensing for the smarter city. We present a framework of crowdsourcing feeds similarity analysis from a threefold point of view, namely image, text, and geography, which is based on similarity analysis, founded on a sequence that goes from coarse to thinner similarity filters. The main idea is to extract feeds within a specific geographic range, and then to analyse similarity of image colour and text in clustered feed sets. The framework enables to identify feeds that report the same issue, and hence to filter redundant information. Based on proved methods and algorithms, such framework has been implemented in a software application, called CITY FEED, which is used by the Municipality of Pavia.