International Journal of Information Technology and Management (66 papers in press)
Top Management Support and Information Systems Implementation Success: A Meta-Analytical Replication
by Mark Hwang
Abstract: Factors that contribute to the success or failure of Information systems implementation have received extensive attention in the literature. Top management support is considered one of the most, if not the most, critical factors for implementation success. However, the positive effect of top management support is not always borne out in empirical data, prompting a quest for moderator variables. A classic meta-analysis shows that the effect of top management support on implementation success is moderated by task interdependence, a claim refuted in several more recent meta-analyses. Drawing from the lessons learned from these meta-analyses, the current research reanalyzed the top management support literature with a larger sample while controlling for the effect of common method variance and systems success measurement issues. The results reaffirm the significant and substantial effect of top management support on systems success. At the same time, evidence also supports the moderating role of task interdependence, common method variance, and how systems success is measured. Implications for systems implementation are discussed.
Keywords: top management support; task interdependence; IS implementation; IT project; meta-analysis; common method variance.
User Engagement in Social Media- Empirical Results from Facebook
by Rupak Rauniar, Greg , Ronald Salazar, Donald Hudson
Abstract: Theory building and better understanding of user engagement behaviour is fundamental to developing future approaches and effective organisational deployment of social media technologies. Based on the theory of reasoned action (TRA), predictors of intention to engage on social media sites were empirically examined with 389 users of Facebook - the most popular online social media site. Our results suggest that perceived value, social presence, interactivity, and trustworthiness are positively related to the users attitude towards social media. The research model shows promise for use by managers and organisations to predict and understand the usage of social media in a target population.
Keywords: theory of reasoned action; social media; Facebook.
Evaluation of mean and variance approximations in three point estimation of task completion times using the beta and the Kumaraswamy distribution
by Pablo Ortiz Bochard, Thomas Schwarz
Abstract: Estimation of task and project completion times within IT projects remains one of the most error-prone, but also most critical duties of an IT project manager. Various three-point methods of PERT have been evaluated by assuming that the true distribution is a beta-distribution. We evaluate PERT methods by comparing additionally with the Kumaraswamy distribution, which has an equal claim to be the true a-priori distribution for project completion times. We use skew and kurtosis in order to define test sets instead of simply choosing a range of shape parameters. We validate various approximations proposed in the literature and show that valid approximations are possible.
Keywords: project management; expert judgement; mean and variance approximations; PERT; three-point estimations; task completion times; beta distribution; Kumaraswamy distribution.
CPPM: a lightweight performance prediction middleware for cloud platforms
by Xiao Peng
Abstract: As more and more commercial clouds have been applied in various areas, how to evaluate the performance of a cloud platform has become an important issue that needs to be addressed. Furthermore, an effective performance prediction mechanism is of significant value for improving the current cloud services, such as resource allocation and task scheduling. In the paper, we present the design and prototype implementation of performance prediction system, namely Cloud Performance Prediction Middleware (CPPM), which is aiming at providing a set of lightweight and flexible services on existing cloud infrastructure so as to allow cloud providers monitoring, estimating and predicting the runtime performance from various aspects. The CPPM enables cloud providers making more efficient and fine-grained resource management and scheduling policies based on its short-term workload prediction mechanism; also it provides an application-level performance prediction service which uses skeleton approach to capture execution characteristics of the running applications so as to predict their actual runtime performance and efficiency. Extensive experiments are conducted to examine the effectiveness and efficiency of the CPPM.
Keywords: cloud computing; performance evaluation; workload; quality of service.
Impact of communication on the collaboration between 3PL service providers and their clients. Case of Lithuania
by Aidas Vasilis Vasiliauskas, Virgilija Vasiliene-Vasiliauskiene, Ieva Meidute-Kavaliauskiene
Abstract: Production enterprises constantly are trying to add more value to their products and secure their competitive advantage. One of the possible ways in achieving this goal is to get rid off of the uncommon activities to intermediaries, which take control over the process of distribution of goods. However due to intense change on the market, intermediaries responsible for provision of 3PL services sometimes falling short to adapt to shifted manufacturers needs. Because of the insufficient exchange of information 3PL service providers start to render inadequate services and are not able to assure efficiency of manufacturers logistics system. This article discusses importance of exchange of information between production enterprises and their logistics intermediaries based on the results of study on the situation of communication problems between 3PL service providers and their clients in Lithuania.
Keywords: Keywords: communication; collaboration; exchange of information; 3PL services; logistics intermediaries.
Investigating the Role of Information Technology in Developing the Learning Organization: Empirical Evidence from Egypt
by Hadia Hamdy, Karim Hodaib
Abstract: Developing a learning organization has been associated with organizational growth and innovation as it enables companies to maintain a memory for their created knowledge and experiences that can be shared among all organizational members. The most important tool identified to build such a memory is Information Technology knowledge management systems. However, despite their importance, their actual contribution to the development of the different dimensions of the learning organization has rarely been studied, especially in developing countries like Egypt.
This research investigates the effect of using IT knowledge management tools on the development of the learning organization using the dimensions of the learning organization questionnaire developed by Watkins and Marsick. A pre-post quasi experiment was conducted in an Egyptian organization to measure the dimensions of the learning organization before and after the application of a new IT system designed to enhance employees learning and knowledge sharing. Results indicate that the implementation of the new IT system significantly improved all the dimensions of organizational learning except for degree of teamwork that is essential for the process of knowledge integration. It concludes that IT systems facilitate the creation of the learning organization, and convey a positive message regarding organizational commitment to developing and empowering employees towards a shared organizational vision. However, they have to be implemented within an integrated strategy, structure, culture, and policy change management system to be able to achieve the desired outcome of transferring a rigid organization into a learning one.
Keywords: Learning organization; Organizational learning; Information Technology; Knowledge management systems; Egypt.
Enhancing Excel Business Tools with Additional Relational and Recursive Capabilities
by Pedro Ramos, Luís Botelho, Antonio Martins
Abstract: This paper presents a new plug in that enriches spreadsheet capabilities mainly in what concerns its potential regarding relational queries and recursive computational processes. Currently some apparently trivial and useful queries can only be handled with the support of programming skills. Spreadsheet users with low computer science skills should have a natural and easy way to handle those queries within the spreadsheet, without relying on external programming (e.g., VBA). The tool we have developed can be used with Prolog technology, and provides those features to the most used professional spreadsheet: Microsoft Excel. Throughout the paper we explore the plug-in features with several business examples.
Keywords: Dedutive Spreadsheet; Business Tools; Excel; Prolog; Recursive Processing; Relational Data.
Validation of Cognitive Inhibitors to Technology Adoption using Content Analysis
by Easwar Krishna Iyer, Sreehari Unnikrishnan, Paul Philip, Mallika Sundarrajan, Bharadhwaj Sivakumaran
Abstract: Inhibitors to technology play a crucial role in predicting eventual technology adoption. In the inhibitor space, studies have so far been limited to understanding affective inhibitors only. The central proposition of this paper is to identify two new cognitive inhibitor constructs. This study augments the existing body of knowledge in the area of technology acceptance behavior by positing Dissatisfiers and Risk as two cognitive inhibitor constructs to technology adoption. The methodology used for validating the proposed model is Content Analysis. The target technology chosen for validation is Internet of Things (IoT). The results obtained provide strong support for the proposed hypotheses thereby establishing Dissatisfiers and Risk as two new cognitive inhibitor constructs.
Keywords: Cognitive Inhibitors; Dissatisfiers; Risk; Content Analysis; Technology Adoption Model; Internet of Things.
Illusion of SNS Influence: Are Popular SNS Influential?
by DongBack Seo, Martin Bügel
Abstract: People influence each other in the environment of social networking sites (SNS). Some SNS have gained popularity. Practitioners and researchers assume that these popular SNS are influential. However, influence and popularity are two different concepts. This research aims to provide two clear concepts of influence and popularity as well as their relationship. Popularity is hypothesized that it is affected by elements in the surroundings (e.g. societal position of a SNS owner) of SNS based on a peripheral information processing mechanism, whereas influence is hypothesized that it is affected by core elements (e.g. quality of content on a SNS message) of SNS based on a central information processing mechanism. The results indicate that influence is solely affected by the central information processing mechanism, but popularity is affected by both central and peripheral information processing mechanisms. There is a relationship between influence and popularity but it is marginal. Therefore, it should not be assumed that popular SNS are influential and vice versa.
Keywords: Popularity; Influence; SNS; Micro-blog; Twitter; Central information processing; Peripheral information processing.
An EMD-SVM model with error compensation for short-term wind speed forecasting
by Yuanyuan Xu, Genke Yang, Tianhe Yao
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 robustnesscompared with the method without error compensation.
Keywords: Wind speed forecasting;EMD-SVM model; Error Compensation.
Information Systems Continuance:
The Role of Incentives and Goal Harmony
by Tove Boee, Boge Gulbrandsen, Tom Roar Eikebrokk
Abstract: This paper extends information systems continuance theory (ISCT) and integrates it with a managerial perspective. Our study integrates principalagent theory (PAT) to capture the managerial influence on users continued use of information technology. We argue that the two theories offer complementary explanations, ameliorate each others shortcomings, and enhance our ability to explain and predict IS continuance. As predicted by PAT, the study finds support for the effects of goal harmony and incentives on users continuance decisions. Additionally, goal harmony reduces the positive relationship between incentives and continuance. While the supported ISCT model shows an explained variance of 37%, the full model explains 51% of the variance of the dependent variable, indicating that the integrative model is more powerful in explaining user continuance than is ISCT in isolation. The study advances the theoretical understanding of continued use of IS from both a personal and a managerial perspective and offers proposals for organizational actions.
Keywords: Information Systems Continuance Theory; Agency Theory; Goal Harmony; Incentives.
A flexible and extensible project planning and management tool tailored for European projects
by Ruben Alonso, Daniele Bortoluzzi, Andrea Costa, Thomas Messervey, Massimiliano Raciti, Diego Reforgiato Recupero
Abstract: In this paper, we describe a software tool developed for the comprehensive management of any kind of project (research, industrial, etc.), from planning to execution and cost reporting within an organization (governmental, public, private). Our project management tool is particularly tailored for, although not limited to, European funded projects (such as FP7 and H2020). It is also capable of handling other common projects and it can easily be extended in cost reporting functionalities to cover any kind of project with other rules, funding mechanisms or other constraints and information required for specific purposes. At the current state, with the presented tool, it is possible to; 1) create new European, national level or company internal projects along with their information; 2) create and manage project activities, work packages and related tasks; 3) manage users and their seniority levels, (e.g., employees of the organizations vs. in-house consultants or owners to include contractual information); 4) assign users to specific work packages of existing projects where they are involved along with their yearly planned involvement in term of target working hours; 5) track worked hours during the projects execution; 6) create new funding types of projects (e.g., upcoming EU FP9) assigning new cost claiming schemes such as funding percentage per activity type, funding overhead etc.; 7) create reporting periods for each project and get costs automatically computed; 8) manage reimbursement associated to users and related projects for travels and purchases expenses. The tool is based on the open-source Frappe framework, has been further developed using web-based, open-source technologies, is available online, can be extended to cover other features and it exposes its data through the easy development of ad-hoc REST services that the tool supports.
Keywords: project planning and management tool; European projects; financial management; time tracking; reporting; reimbursement; calendar; time tracking feedback; full-stack web framework; FP7; H2020.
Impact of E-commerce on supply chain management
by Saleeshya P.G, Rahul R
Abstract: E-commerce is web-enabled technology that brought significant changes in the supply chain activities of anindustry. Supply chain management has become a major strategy in manufacturing and service industry. An attempt has made in this paper on identifying the significant factors that affect the e-commerce adoption in the supply chain of a company, identifying the effects of e-commerce on different aspects of supply chain. The study also identifies the major performance parameters of a supply chain and effect of e-commerce adoption on these supply chain variables. An attempt has been made to propose a conceptual model for the study based on the existing literature and field study; the model is validated by academic experts and also experts in industry. Correlation analysis is being carried out to find gap between the ideal and actual situation.rnrn
Keywords: supply chain; E-commerce; correlation; conceptual model.
Determinants of Success and Failure of Knowledge Transfer in Information Systems Offshoring: A Ranking-Type Delphi Study
by Artur Strasser, Susanne Strahringer, Markus Westner
Abstract: The transfer of knowledge from client to service provider poses major challenges in information systems (IS) offshoring projects. Knowledge transfer directly affects IS offshoring success. Therefore, associated challenges must be overcome. Our study examines the determinants of success and failure of knowledge transfer in IS offshoring projects based on a ranking-type Delphi study. We questioned 32 experts from Germany, each with more than ten years of experience in near- or offshore initiatives to seek a consensus among them. We identified 19 success and 20 failure determinants. These determinants are ranked in order of importance using best-worst scaling. Aspects of closer cooperation are critical for effective knowledge transfer. This includes regular collaboration, willingness to help and support, and mutual trust. In contrast, critical determinants of failure are concerned with fears and fluctuation of human resources. Hidden ambiguities or knowledge gaps, an unwillingness and disability to share knowledge, and high fluctuation of human resources negatively impact knowledge transfer.
Keywords: best-worst scaling; BWS; delphi; determinants of success; determinants of failure; information systems; IS; information systems offshoring; knowledge transfer; ranking-type delphi.
Special Issue on: ICSS 2015 Services Computing Technologies and Applications
Threefold similarity analysis
A case study on crowdsourcing feeds
by Kaixu Liu, Gianmario Motta
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 analyze similarity of image color 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.
Keywords: Crowdsourcing; Smart city; Image similarity analysis; Text similarity analysis.
Needle in a Haystack: An Empirical Study on Mining Tags from Flickr User Comments
by Haijun Zhang, Jingxuan Li, Bin Luo, Yan Li
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, namelyword-based and phrase-based. In ranking and recommending tags, we proposed an algorithm by jointly modeling 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.
Keywords: tag recommendation; user comment; Flickr; image annotation.
SGP: A Social Network Sampling Method Based on Graph Partition
by Xiaolin Du, Yunming Ye, Yan Li, Yueping Li
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, SGP (Sampling based on Graph Partition), is proposed to sample social networks. SGP firstly partitions the original network into several subnetworks, and then samples in each subnetwork 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 data sets. The experimental results show that SGP method outperforms seven state-of-the-art methods.
Keywords: sampling algorithms; social networks; graph partition; community structure; topology structure.
Special Issue on: LISS16-ITM Information and Technology Management in Big Data Environment
Analysis of the Multi-Agents Relationship in Collaborative Innovation Network for Science and Technology SEMs Based on Evolutionary Game Theory
by Guangyou Nan, Jinyu Wei, Haiju Hu
Abstract: The construction of collaborative innovation network for the science and technology small and middle enterprises (SMEs) is an attempt to effectively combine the advantages of science and technology SMEs. In this paper, the evolutionary game theory is used to establish the three partys game model including the government, science and technology SMEs, the college and research institution. Then, the interaction mechanism among them is analyzed. On the basis of analyzing each partys cost and benefit under different strategic portfolios of three parties, the stable strategy of the evolutionary game is derived. The research results show that the government can effectively mobilize the enthusiasm of enterprises and the college and research institution by setting up reasonable subsidies or fines for them, and could also influence and promote the cooperation relations between enterprises and the college and research institution.
Keywords: Collaborative Innovation Network; Government; Science and Technology SMEs; College and Research institution; Innovation Management; Evolutionary Game Theory.
Research on the Measuring Method and Structure Mining of Organization Conflict
by Lei Liu, Xinan Zhao
Abstract: Jehn divides organization conflict into task conflict and relationship conflict. To grasping the degree and structure of organization conflict, which can provide effective decision-making basis for dealing with conflict. Establishing measurement model of organization conflict , giving the weight with the methods of distinguish individual advantage characteristic and using Intergroup Conflict Scale to measure conflict situation .According to the measurement results, (1) analyzing the intensity of the conflict of the organization, (2) analyzing the types of organization conflict, (3)fingding the common features of high conflict members. Taking a company in Shenyang as an example, selecting market services and property management departments. Applying the research method to analyze the organization conflict, determining the causes for conflict of high conflict group members, and verifing the feasibility of this study.
Keywords: organization conflict;structure mining;individual advantage characteristic;clustering analysis.
Electric vehicle range estimation based on the road congestion level classification
by Hong Liang, Wenjiao Wang, Yunlei Sun, Min Zhong, Jianhang Liu
Abstract: Electric vehicles have been an emerging industry in recent years even though the remaining driving range bothering the drivers. To strengthen the user acceptance and relieve the range anxiety, an efficient and accurate estimation approach of remaining driving range would be a solution. In this paper, a road congestion level classification based on support vector machines is proposed and an electric vehicle power model is implemented based on the real-world dataset collected from LF620 battery vehicles. The experiment includes data pre-processing, best parameters searching, support vector machine model training and remaining driving range calculation. The results show the significant influence of considering the big data analysis results on range estimation.
Keywords: electric vehicle; driving range estimation; support vector machine; python.
A Decision Support System for Identification of Technology Innovation Risk Based on Sequential CBR
by Quan Xiao
Abstract: To identify risks in the increasingly complex market is an important issue for the survival and development of technology innovation enterprises. But it is contended that there still lack effective methods to support the dynamic characteristic and knowledge reuse of the problem. In front of a variety of risk sources, the utilization of IT is necessary, and we introduce Case-based Reasoning (CBR) technique to identify new risks from cases in the past. However, extant CBR method has limitations on problems with dynamic characteristics. In this paper we provide insights into the dynamic nature of technology innovation risk identification, and contribute a novel extension of CBR to sequential CBR and design a decision support system for identification of technology innovation risk. In our framework, cases are represented as sequences of risk events, and similarity between cases is measured based on weighted event sequence pattern mining method. The effectiveness of this work is illustrated with a case of technology innovation risk identification.
Keywords: risk identification; case-based reasoning; technology innovation; sequential data.
The research on the selection of the rice transfer machine
by Xin Yang, Zhenxiang Zeng, Xinjiang Cai
Abstract: This article sets the fuel consumption index and the working efficiency index as the main basis for the rice transfer machine. Through the simulation experiment of different load transfer machine models, it reaches their fuel law in the rice field. Finally it determines the best model by the optimal ratio and the working efficiency, which extremely satisfies the requirement of the short payback period and the long service life. The transfer machine reduces the labor intensity of the future agriculture field, improves the production efficiency, and reduces the labor cost. And it also has an important role on improving business efficiency.
Keywords: Rice mechanization; Transfer machine; BOM; Fuel consumption efficiency ratio.
Data set Replica Placement Strategy under a Response Time Constraint in the Cloud
by Wu Xiuguo, Su Wei
Abstract: In cloud computing environment, especially data-intensive systems, large amounts of data sets are stored in distributed data centres, and often be retrieved by users in different regions. To reduce the users' response time, replicating the popular data sets to multiple suitable data centers is an advisable choice, as tasks can access the data sets from a nearby site. Nevertheless, the data set replicas' suitable storage placement selection is still an important issue that should be solved urgently from the response time constraint view, for the reason that too much more replicas are infeasible in practice. In this paper, we first propose a comprehensive data set response time estimation model, then present a replica placement model based on Steiner tree. After that, an approximate replica placement algorithm under a response time constraint in the Cloud is given using Kruskal minimum spanning tree. At last, a practical and reasonable performance evaluation is designed and implemented. Both the theoretical analysis and simulations conducted on general (random) data sets show the efficiency and effectiveness of the proposed strategy in the cloud.
Keywords: cloud computing; replicas placements; response time constraint; Steiner tree; minimum spanning tree algorithm.
Sampling Method Based on Improved C4.5 Decision Tree and Its Application in Prediction of Telecom Customer Churn
by Weibin Deng, Linsen Deng, Jie Qi
Abstract: Nowadays, customer churn prediction is quite important for telecom operators to reduce churn rate and remain competitive. However, the imbalance between the retained customers and the churners affects the prediction accuracy. For solving this problem, a new sampling method based on improved C4.5 decision tree is proposed. Firstly, an initial weight is set for each sample according to the data scale of each class. Then, the samples weight is adjusted through several rounds of alternative training by the improved C4.5 decision tree algorithm. Both the gain ratio and the misclassification cost are considered for splitting criterion. Besides, the boundary minority examples and the centre majority examples are found according to their weights. Furthermore, over-sampling is conducted for the boundary minority examples by SMOTE and under sampling is executed for the majority examples. Experiments on UCI public data and telecom operator data show the efficiency of the new method.
Keywords: Telecom customer churn; Imbalanced data; Under-sampling; Over-sampling; Decision tree; Data mining.
An improved reversible data hiding scheme based on pixel permutation
by Jilin Wang, Xiao Sun, Xiaoqing Feng
Abstract: Low payload capacity is a flaw of pixel exchange based reversible data hiding schemes. In order to improve it, this paper proposes a triangular pixel pair structure, which requires fewer pixels than a traditional 2
Keywords: data hiding; reversibility; pixel pair; Huffman coding; pixel permutation.
Special Issue on: Intelligent Technologies in Modern Industries Challenges Facing Globalisation and Informatisation
Automatic recognition and defect compensation for calf leather
by Yu-Tang Lee, Chung Yeh
Abstract: Various defects existed on the surface of calf leather could affect its usable area and the salable price. No international criterion specifies the compensatory credits for calf leather surface defects which cause additional cost between supplier and purchaser in complicated negotiation process. This paper is to develop an artificial intellectual technique to implement the automatic recognition for types of leather defect and to compensate for leather defective unusable area in order to bridge trading gap between leather provider (supplier) and manufacturer (purchaser). An approach starts with scanning the images of collected samples of leather and classifies surface defects into seven categories by using digital image processing technique and blobs analysis which attributes hole, area, perimeter, ratio of perimeter, length and width of defects etc. Data of calf defects from sample is extracted to develop an automatic recognition system via artificial intellectual techniques ANN learning process is introduced to make a sustainable automatic recognition system used to identify types of categories for upcoming leathers under inspection, then incorporated with compensation criterion of specific category for leather defect which is accepted by both parties to conclude compensation credit. Study results the validity and accuracy of automatic defect recognition can meet requirement of practical implementation on leather business transaction; the mean error rate of recognizing leather defect is less than 2.16% and the mean deviation rate for compensation area is 0.03% under this simulated transaction.
Keywords: Leather surface defects; artificial neural network; digit image processing; mean error rate of recognizing leather defect; mean deviation rate for the leather area.
Fitting the power spectrum of stationary random sequence
by Chang-qing ZHANG, Zhan-wen LIU, Zhi-gang XU
Abstract: Based on the spectral representation of zero-mean stationary process, another method of fitting the target power spectrum is obtained. The fitted random sequence is uniformly convergent with respect to the target sequence. Compared to the commonly used wave superposition method in simulating wind velocity fluctuations, its merits and deficiencies are shown.
Keywords: Turbulent wind velocity sequence; stationary process; fitted power spectrum; wave superposition method.
Factors Influencing Cloud Computing Adoption for Higher Educational Institutes in India: A Fuzzy AHP Approach
by Mahak Sharma, Ruchita Gupta, Padmanav Acharya
Abstract: Information and communication technologies (ICT) are changing the ways of imparting education, providing learning opportunities and enhancing institutional performance. Cloud Computing (CC) helps in lightening the burden of massive technology investments required for improving performance. However, CC adoption in higher educational institutes in India is still in nascent phase. Therefore, the objective is to identify and rank the factors enabling CC adoption in the context. Fuzzy Analytic Hierarchy Process (FAHP) has been applied. Time to cater IT demand, security and relative advantage are found as the most critical factors. The findings will aid other institutes in adoption decision and gain competitive advantage.
Keywords: Adoption; Cloud Computing; Higher Educational Institutes; India; Fuzzy Analytic Hierarchy Processing; Critical Factors.
A Blind Demodulation Algorithm of DSSS by Continuous Wavelet Transform
by Wei Huang, Xiangmo Zhao, Chao Wang
Abstract: This paper proposes a novel blind demodulation algorithm of the Direct Sequence Spread Spectrum Signal (DSSS) modulation with noise by using continuous wavelet transform. We chose Morlet wavelet with similarity and symmetry. The proposed algorithm can demodulate DSSS with a negative signal to noise ratio(SNR) without any prior knowledge. The simulated the bit error rate(BER) curves of the existing blind demodulation algorithms of DSSS are above the theoretical BER curve of the coherent demodulation or coincidence. Simulation result of new algorithm shows that BER curve of the blind demodulation of the wavelet transform is below the theoretical BER curve of coherent demodulation. The novel blind demodulation algorithm allows to obtain an 8dB gain in SNR terms compared with that obtained by the theoretical coherent modulation when SNR is 10^-4 .
Keywords: DSSS; SNR; BER; continuous wavelet transform; blind demodulation.
E-HRM implementation,adoption and its predictors;A Case of Small and Medium Enterprises of Pakistan'
by Abdul Waheed, Miao Xiaoming
Abstract: The purpose of this study is to investigate the predictors of E-HRM implementation and adoption in the Small medium enterprise (SMEs) in the manufacturing sector. Three main predictors including conventional HRM practices, availability of resources and employees attitude towards E-HRM were analyzed. Data was collected through a questionnaire survey, and 500 employees participated in this survey. Results reveal that E-HRM heavily depends on employee attitude towards E-HRM and availability of resources. Conventional HRM practices including training and development, compensation and benefits, and performance appraisal also have an essential role. The readiness of implementing and adopting E-HRM practices in small and medium enterprises heavily dependent on expertise, financial and technical resources. This study will help managers to develop a strategy for effective implementation of E-HRM in Small, medium enterprises (SMEs) in the manufacturing sector.
Keywords: E-HRM;Conventional HRM; Small Medium Enterprises; Pakistan SME’s; Implementation; Adoption.
Moderating Effect of Information Technology Ambidexterity linking New Human Resource Management practices and Innovation Performance
by Abdul Waheed, Miao Xiaoming
Abstract: This study aims to investigate the relationship between new human resource management (NHRM) practices and innovation performance. Further, the moderating role of IT ambidexterity was examined between NHRM and innovation performance. This study selected Pakistans largest IT-based semi-government organization National Database & Registration Authority (NADRA) as a case study. Data were collected from three major cities (Lahore, Gujranwala, and Jhelum) of Pakistan. 500 employees of NADRA participated in the survey-based research. The empirical results found the positive relationship between NHRM practices and innovation performance. The moderating influence of IT ambidexterity was also found in this study. Employees with high IT ambidexterity are more involved in innovation performance. Continuous adaption of technology enhances long-term competitive advantage. Therefore, utilizing new technologies and knowledge consistently is significant for the enhancement of innovation performance.
Keywords: New HRM practices; Innovation performance; IT ambidexterity;IT flexibility; IT Standardization.
Canonical correlation analysis of the impact of ICT on the diversification performance
by Omar Alexánder León García, Juan Ignacio Igartua, Jaione Ganzarain
Abstract: The main objective of this study was to establish the relationship between the use of information and communication technologies (ICT) and the performance of business diversification within a sample of companies belonging to the Autonomous Community of the Basque Country. For this, a Canonical Correlation Analysis (CCA) was performed, taking the coefficients to explain the variance of the two sets of variables. . It can be affirmed that the ICT of general use, of electronic commerce and of relationship impact in the elements that compose the performance of the diversified company.
Keywords: ICT; diversification; performance; canonical correlation.
Simulation Verification for Automobile Anti-lock Braking System Bench Test Principle
by Ruru Hao, Xiangmo Zhao, Lan Yang
Abstract: Anti-lock braking system (ABS) is an automobile safety system that has been designed to achieve maximum deceleration by preventing the wheels from locking up and avoiding uncontrolled skidding. In this paper, a bench-based ABS test principle was proposed, which can provide different surface adhesion coefficient dynamically for four wheels. And the simulation model, consists of the vehicle model, wheel model, braking force model, and tire-road model, was established to verify the principle. The simulation experiments on a variety of simulated road conditions are carried out. And the comparative analysis between the ABS bench detection model and ABS road test model is accomplished. The comparative analysis indicated that the simulation curves of the bench detection method and those from road test model are highly consistent in the changing trend under different braking conditions, which verified the correctness and feasibility of the proposed bench detection scheme.
Keywords: Anti-lock Braking System; Bench detection; Modeling and simulation; Comparative analysis.
OBWD: An ontology and Bayesian network-based workflow design platform
by Chao Dong, Chongchong Zhao
Abstract: Workflow management provides a great convenience for the cooperation between different roles in modern industry and business. The task reuse and design automation are challenges of workflow management currently. In this paper, an ontology SDWMO is constructed for workflow resources integration and task request release. An algorithm DOMDM is proposed to achieve the conversion of the data from traditional workflow database to SDWMO ontology. In order to provide workflow templates for designers, we extract statistic-oriented cases from the workflow database. Based on these cases a Bayesian network is established for workflow template recommendation. We have designed OBWD platform to implement the above methods. The experimental data indicates that OBWD is statistically effective and saves a lot of time for workflow designers. Currently, OBWD has been used in space debris mitigation domain for workflow management. Moreover, our methodology can also be applied in many other domains in the future.
Keywords: workflow; ontology; Bayesian network; JBPM.
Special Issue on: Information and Communication Technologies for Business Process Efficiency and Effectiveness
Impact of information and communication technologies on productivity growth
by Alma Maciulyte-Sniukiene, Mindaugas Butkus
Abstract: In modern economy information and communication technologies (ICT) play the essential role. The improvement of ICT infrastructure, developing ICT knowledge and usage can lead to economic growth due to higher productivity. However, this impact can depend on countries ICT investment or expenditure amount, reached ICT development and productivity levels. Moreover, ICT impact on productivity growth can occur after a certain period. Consequently, forming the ICT development strategies it is important to identify ICT impact period and impact differences between relatively high and low productivity countries. Authors examined the impact of ICT investments on productivity in EU countries covering the period of 1995-2015. Research results have revealed that ICT development positively and directly influences productivity, but this effect manifests with a lag in time. Moreover, it was found that the impact of ICT development on productivity is about twice bigger in countries with relatively high productivity level compared with countries that have relatively low productivity level.
Keywords: ICT; ICT investment; labour productivity; productivity growth.
A Delphi-based study on the innovation practices in the Albanian financial sector
by Amali Cipi, Enida Pulaj( Brakaj)
Abstract: Based on an application of the Delphi technique, this paper aims to analyse the current situation of innovation practices in Albania, based on an application of the Delphi technique to a panel of Albanian financial institutions executives. Despite the short life of most of the financial companies in the Albanian trade market, results show that executives have a relatively good level of knowledge regarding the benefits of the innovation approach to the firm, but there is a lack of some innovation types used by their companies. Furthermore, there is a low level of investment amount to be spent in innovation, indicating a non-possibility to create new products or processes and thus not having the possibility to be a leader and create better promise for the company growth. Government is advised to consider innovation as a mediating factor to achieve better business performance and thus helping and promoting sustainable macroeconomic growth.
Keywords: innovation; technology innovation; product innovation; Albania; Delphi technique; financial companies.
Does Social Media Use at Work Lower Productivity?
by Joseph Vithayathil, Majid Dadgar, John Kalu Osiri
Abstract: We conducted an empirical study that analyzed the relationship between the use of social media at work and project success at work. This study adds to the emerging literature on the impact of social media use on organizational outcomes. We found that only one of the four popular social media platforms studied adds value to the workplace. Specifically, we found the use of Facebook at work, whether controlling for age, gender and education or not, to be negatively associated with project success. The use of LinkedIn (with the controls) and the use of Other Social Media (without the controls) were both found to be positively associated with work project success. Other Social Media was used to capture all other social media platforms, including firm-specific or work-specific social media platforms. We explained our results using social network analysis (SNA), the strength of weak ties, and information diffusion theories. Our findings have implications for practice, policy and future research.
Keywords: social media usage; project success; social network analysis; strength of weak ties.
Assessing the Business Dimensions of Green IT Transformation: A Case of an Indian IT Organisation
by Parvathi Jayaprakash, R. Radhakrishna Pillai
Abstract: Green Information Technology (green IT) is the application of ecological consciousness to Information Technologies (IT). A Green IT strategy is an IT strategy that attempts to reduce the environmental impact of IT such as reduction in CO2 emissions. With a Green IT strategy in place, it is also necessary to assess the business dimensions of the transformation using appropriate key performance indicators. This would help the IT managers or the top management team to understand the refinements that are needed into the strategy. This paper attempts to address the gap in the literature on how to assess the business dimensions of green IT transformation in an organization. The four business dimensions of green IT transformation, namely, economy, technology, people, and process are used in this study in the context of an Indian IT organization. Though green IT strategy is unique for every organisation, a case of a particular organisation can provide a roadmap for other companies to develop such strategies for their own organization. Such findings could be used to evaluate the green IT transformation across several organisations. Our analysis brings out the status of transformation in all four dimensions and recommendations are drawn to fine-tune the green IT strategy based on the findings. The lessons learned can guide others in developing Green IT strategies of their own.
Keywords: green IT; green IT Strategy; green IT Transformation; Business Dimensions; Indian IT organisation.
Implementing ERP evaluation through a fuzzy analysis: an empirical investigation
by Maria Rosaria Marcone
Abstract: The purpose of this paper is to present an integrated manufacturing performance measurement through an (implemented) integrated information technology system to focus the organization on value streams and, by doing so, to improve performance, and, secondly, to analyze the results obtained. This study extends the literature on information systems (IS) integration by providing insights into a set of interrelationships that are relevant for the attainment of firm profitability from IS integration.rn The conceptual model for fitness evaluation was developed by literature review. The purpose of this paper is to develop a comprehensive model for fitness evaluation and to determine a fitness index using fuzzy methods. To test the hypotheses, we employed a multi-case study methodology. An evaluation of the improvement in efficiency of the processes was conducted on 21 medium sized enterprises (SMEs) which produce on the basis of orders, and have implemented a system of enterprise resource planning (ERP), in order to check the progress of each order and to increase the efficiency of the processes they are connected to. rn
Keywords: information system; ERP; production system.
Special Issue on: Information Technology/Information Systems Applications in Enterprise Systems
Investigation and Analysis on Crowdsourcing for Improving Enterprise QoS
by Remya S, Sasikala R
Abstract: Crowd sourcing is treated as an open contest for a crowd of people known as workers. All workers can contribute their suggestions and solutions to the platform. Hence crowd sourcing can connect a large number of people and they can share their knowledge. The amount of unstructured data is increasing now. This is where crowd sourcing can help big data by breaking down data into mini chunks and have the power of crowd to do the organizing task. This helps big analytics companies focus on the core aspect of infrastructure and security. It also makes sense of the data and not invests resources in organizing data and this distributed environment can be solved intelligently. Here various crowd sourcing techniques in different aspects related to data pre-processing, performance approaches, security issues and applications are analysed. Out of these approaches the most efficient one in each are characterized. This survey helps to analyse the various issues in crowd sourcing and proposed some solutions for improving the quality and security of workers in crowd sourcing based on the literature survey
Keywords: Enterprise; Crowd sourcing; Bigdata; K-means; QoS.
Assessing the Impact of Information Technology on Human Resource Practices: Evidence from Organisations in Ghana.
by Mayqueen Attatsitsey, Noble Osei-Bonsu
Abstract: Information Technology (IT) is universally regarded as an essential tool in enhancing the competitiveness of the economy of a country. There is consensus that IT has significant effects on the productivity of firms. This study focused on how modern Information Technology impacts on effectiveness and efficiency of HR practices. Human Resource Management practitioners generally use IT in the form of Human Resource Information System (HRIS) for the purposes of decision making in the field of HR. The main objective of this study was to examine the impact of IT on HR practices in organisations in Ghana. One hundred organizations were purposively sampled for the study. The descriptive survey methodology was used. Data was collected using a self-designed questionnaire made up of closed and open-ended questions and statements. Results revealed organizations awareness and use of the various HR-related software, and despite the fact that these software are costly, they, at the same time, generates terrific benefits.
Keywords: Information Technology; Human Resource; Human Resource Information Systems; Ghana.
Congestion Management with Improved Real Power Transfer Using TCSC in Thirty Bus System
by Mohana Sundaram Kuppusamy, Kalaimani P
Abstract: Secure operation and reliable utilization of transmission lines is a challenging issue in deregulated power system. The scheduled power transactions are difficult due to the overloading of transmission lines in restructured power system as the electricity market has become more competitive. Due to congestion of transmission lines, the transfer of real power and the power system voltage profile are greatly affected in the power system. The aim of this research work is to increase the real power and the reactive power flowing in the lines of multibus system using thyristor controlled series compensator (TCSC). Real power transfer with reduced losses and improved voltage stability is an important factor in the present global scenario. This paper deals with the improvement of power flow in power transmission lines by series compensation device in thirty bus system with reduced congestion. The thirty bus system without and with thyristor controlled series compensation device (TCSC) is modeled and simulated and the results are presented. The simulation studies indicate a significant improvement in the real and the reactive power flow with the introduction of TCSC .The advantages of the proposed system include the smooth variations of the real and the reactive powers.
Keywords: TCSC; Congestion Management; Real power flow; Voltage Stability; Available Transfer Capability.
CLASSIFICATION OF CRICKET VIDEOS USING FINITE STATE MACHINES
by VIJAYAN ELLAPPAN
Abstract: The problem of classifying scenes from cricket video is addressed and a robust framework for this problem is proposed. It is proposed that the finite state machines (FSM) are suitable for detecting and classifying scenes and their usage is demonstrated for three types of events: wicket, six, four. This framework utilises the structural information of the scenes together with the low-level and mid-level features. Low level features of the video including motion and audio energy and a mid-level feature, body, are used in this approach. The transitions of the FSMs are determined by the features from each shot in the scene. The FSMs have been experimented on over 80 clips and convincing results have been achieved.
Keywords: Fine State Machine.
Automatic Brain Tumor Detection using Image Processing and Data Mining Techniques
by Geetha Ramani R, Febronica Faustina, Shalika Siddique, Sivaselvi Krishnamoorthy
Abstract: In recent days, analysis on Magnetic Resonance Imaging (MRI) has extensively performed to understand the complex information in the human brain. Mostly, the pathological regions in the brain are detected using various MRI techniques. Depending upon the MRI technique specific regions may be exhibited better than other regions. These images are computationally analysed to identify the abnormal regions. In this work, glioma images are involved to detect the tumor regions in the brain using image processing and data mining techniques. Broadly, the pixels are grouped into tumor and tumor pixels using unsupervised as well as supervised data mining methods. Further, the tumor pixels are classified into four classes namely, edema, necrosis, enhancing tumor and non-enhancing tumor using supervised classification methods. K-means clustering could detect the tumor pixels with the accuracy of 94.64% whereas Random Forest classifier with 99.5% could identify the pixel classes correctly.
Keywords: Image Processing; Data Mining; Clustering; Classification; Random Forest; Brain Tumor Detection;.
An Improved Mean Curvature Based Bending Model for Cloth Simulation
by Xiaohui Tan
Abstract: In cloth animation, the bending behavior of cloth is important for cloth simulation effects. The presentation of cloth bending properties plays a key role in cloth animation research because cloth is characterized by strong resistance to stretch while weak resistance to bending. This paper proposed an improved approximate nonlinear bending model based on local geometric information. In the dynamic simulation, cloth was divided into several regions according to mean curvature of surface. The bending force was updated according to the changes of the mean curvature in each region. The calculation of bending force was simple and accurate with the proposed model. Experimental results show that wrinkles and folds generated in a natural way with the improved model and the efficiency of simulation is improved compared with the original algorithm.
Keywords: bending model; cloth simulation; mean curvature; surface segmentation.
Frequency Variations Management in Deregulated Environment using Intelligent Controller
by Chockalingam Aravind Vaithilingam, Yi Heng Ser, Ramani Kannan, Charles R Sarimuthu
Abstract: Information of the frequency variations is critical to restore the dynamics of power system network. In this paper a modified load frequency control method in the deregulated power system to restore back the frequency is proposed and analysed. The research is done using industry standard modelling tool and the frequency variations are investigated through the construction of the power system network. The distribution company participation matrix is used in the deregulated environment with number of Generation Company (GENCO) and Distribution Company (DISCO). Three type of controllers are applied to the proposed two-area system through load frequency control including the conventional and intelligent controllers. The deviation of the output frequency in each area and tie line exchange are studied. It is concluded that the modified load frequency control method using intelligent controller shows 10% improvement on the settling time and about 20% improvement on undershooting.
Keywords: Load Frequency Control; Multi-Area Power System; Reliable Grid; Energy storage systems.
Automation of Smart Monitoring for Person Localization & Alerting Network
by Deepika Kripanithi
Abstract: my Person Localization and Alerting Network (myPLAN) is an application for cautioning connections in deadlock situations. myPLAN is a mobile Application (App) developed for Android enabled smartphone. The App invokes Global Positioning System (GPS) Application Programming Interface (API) from the smartphone. Android Operating System (OS) enables the GPS API to retrieve the geographical location data. The whereabouts of an individual are incorporated using GPS module and Wi-Fi networks that are embedded in the device. GPS and Wi-Fi sensors utilize triangulation techniques to pinpoint the exact location on the global map. The application involves in linking the entity with the blood relations. myPLAN App opens by passing an unique code in the phone. The entity can send help request to family associations and emergency vehicle equipments through the App. An individual can inform the associated people about the whereabouts in case of trouble. The person can alert the connections about the location and time via Short Message Service (SMS) and Electronic Mailing System (E-MAIL). Emergency dispatchers can be chosen based on the necessity. The GPS API in the mobile phone locates the relevant assistance in the specified radius from the place where the individual positioned. The information is sent to the emergency carriers through SMS with the entity details along with the location co-ordinates.
Keywords: Application; Emergency; Global Positioning Systems; Location;rnLocalization; Positioning; Security; Sensors.
A Review on Feature Selection Methods for Improving the Performance of Classification in Educational Data Mining
by Maryam Zaffar, Manzoor Hashmani, Sameer Khan
Abstract: Educational Data Mining (EDM) evaluates and predicts students performance that assists to discover important factors affecting students academic performance and also guides educational managers to make appropriate decisions accordingly. The most common technique for discovering meaningful information from the educational database is classification. The accuracy of classification algorithms on educational data can be increased by applying Feature Selection Algorithms. Feature Selection Algorithms help in selecting robots and meaningful features for predicting students performance with high accuracy. This paper presents different EDM approaches for forecasting students performance using different data mining techniques. In addition, this paper also presents an evaluation of recent classification algorithms and feature selection algorithms used in Educational Data Mining. Furthermore, the paper will guide the researchers on new and possible dimensions in building a prediction model in EDM.
Keywords: Educational Data Mining (EDM); Feature Selection in Educational Data Mining; Filter Feature Selection; Wrapper Feature Selection.
PERFORMANCE ANALYSIS OF IRIS BIOMETRIC SYSTEM USING GKPCA AND SVM
by Suganthy M, Manjula S
Abstract: Among all biometric technologies, iris recognition is most accurate and high confidence authentication system. Due to the limitations in PCA based system, modified Principal Component Analysis (PCA) based feature extraction is proposed in iris recognition system. In the proposed system, features are extracted using Gaussian Kernal PCA (GKPCA) and classified using Support Vector Machine (SVM). GKPCA and SVM algorithms are evaluated using CASIA V3 Iris database. The performances are compared with the existing PCA based system. The proposed system achieves 96.67% of accuracy for 256 features using GKPCA linear SVM. False Acceptance Rate (FAR) and False Rejection Rate (FRR) are 0 and 3 respectively, for linear SVM. The results show that the proposed system performs accurate localization of patterns even in non-ideal conditions.
Keywords: Gaussian Kernal Principal Component Analysis; Support Vector Machine; Iris recognition; False Acceptance Rate.
Mobile Application for Children to Learn Hadith: Hidup Cara Rasullullah
by Aliza Sarlan
Abstract: Informal learning for many people starts at home from the moment they were born until they die. Starting from as simple as learning Arabic alphabet, informal learning of Islamic context for children is a vital part in any Muslim child development. However, the platform for children to obtain informal learning within Islamic context is limited. The rapid development of emerging technologies for mobile devices has increase the possibility to exploit them for creation of Islamic contexts apps necessary for children informal learning phase. As such, this project aims at developing a mobile application for android that enables children and parents to learn Islamic Hadiths in an interactive and engaging manner called Hidup Cara Rasullullah. User acceptance and usability testing results demonstrate an acceptable level of user acceptance and usability level. The mobile apps able to assist parents and teachers in promoting hadith learning among children in an informal learning environment.
Keywords: Mobile learning; children education; informal learning;rnIslamic education; hadith;.
Promoting Business -IT Alignment through Agent Metaphor Based Software Technology
by Venkatesan Devanathan
Abstract: Business-IT Alignment (BITA) not only enables enterprises to synchronise their effort among stakeholders but also facilitates efficient achievement of organizational goals. A strategy to facilitate BITA in enterprises is to adopt suitable Enterprise Architecture (EA). Popular software modeling approaches like unified modeling language (UML), Business Process Modeling Language (BPML) are used in EA to depict its IT models. So there is a direct interdependency or connection among BITA, EA, and software technology. Aligning the modeling terminologies of BITA, EA and software technology, facilitate CASE automation. It also improves traceability among different kinds of enterprise models, and facilitates change propagation. By this enterprises can avoid unnecessary effort to synchronise different models and terminologies using error prone and hard to comprehend model connectors. This result in enhanced stakeholder performance by reducing their effort needed to comprehend and perform BITA, EA and software modeling tasks. The present study evaluates the suitability of the object-oriented and agent-oriented modelling for the development of business model aligned software. EA alignment capabilities of these technologies are compared using Zachman Framework (ZF). The comparison is carried out by evaluating the property of the models of these technologies in filling up the grid cell units of ZF, in a manner preserving the syntactic and sematic relationship between grid cell units. This study presents the fact that agent models provide better business process software model alignment between gird cells of ZF due to the syntactic continuity of model abstractions in grid cell diagrams. It improves stakeholder communication, reduces the possibility of misunderstanding of business flow and augments savings due to fewer errors. This study also demonstrates that models created using agent abstraction can satisfy the information requirements as needed in ISO 19440/19439 and other enterprise modelling standards.
Keywords: Keywords: Enterprise Architecture - Enterprise Architecture Framework- Business Model Alignment- BITA- Model Driven Development – Zach man Framework- Enterprise Integration – Information System Design.
Special Issue on: ICSS 2017 Service Computing Technologies and Applications
Study on Image Feature Recognition Algorithm and Its Application in Public Security Management
by Xiaoyi Yang, Qian Wu, Xinmei Deng
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.
Keywords: Public security; security management system; public security management; government concern; image distortion; overall structure; details information; orthogonal wavelet transform&; wavelet coefficient matrix; texture measure&; false edges; enrich the details.
Identifying Inter-Organizational Resource-Service Sequences Based on Similarity for Collaborative Tasks
by Haibo Li, Mengxia Liang
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 organizations. This reduces the efficiency of the collaboration of resource services among different organizations. To solve this problem, a similarity based approach is proposed to identify the resource service sequences in an inter-organizational business process. Manufacturing is used as an example to discuss the problem. First, a modeling method, RSTM (resource service temporal relationship modeling), 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-organization consideration. Finally, the proposed approach is tested with a simulation experiment, and the results show that it is very promising.
Keywords: collaborative task; inter-organization; resource service sequence; big data.
A Collusion-Resistant Public Auditing Scheme for Shared Cloud Data
by Fulin Nan, Hui Tian, Tian Wang, Yiqiao Cai, Yonghong Chen
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. One of the most serious concerns is the potential collusion attack when some users leave the shared group. To address the concern, 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 finalize the block tags of shared data, namely re-signing the data block once again for security, 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 the user revocation scenario. In addition, we extend the scheme to support batch auditing by employing the aggregate BLS signature technique. We formally prove the security of the proposed scheme and evaluate its performance by comprehensive experiments and comparisons with the state-of-art schemes. The results demonstrate that the proposed scheme can effectively achieve the public auditing for shared data while providing excellent security, and outperforms the previous ones in the computation and communication overheads in the user revocation phases.
Keywords: cloud storage; shared data; public auditing; collusion attack; user revocation; local authentication server.
Cost and Green Aware Workload Migration on Geo-Distributed Data Centers
by Jiacheng Jiang, Yingbo Wu, De Xiang, Keqin Yu, Tianhui Wang
Abstract: With the development of the inter-datacenter (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 data center. This paper presents a solution - cost and green aware workload migration algorithm (CGWM) that utilizing 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 optimization goals conflict, CGWM first to ensure the reduction of electricity cost, and then by adjusting the weight factor to make CGWM more biased to optimize the carbon dioxide or water consumption. Simulation results show CGWM can reduce electricity costs while controlling carbon dioxide emissions and water consumption.
Keywords: Cloud Computing; VM Migration; Geographical Datacenters; Green Datacenters; Carbon Dioxide Emissions; Greedy Algorithm.
by Shiqi Wen, Cheng Wang, Haibo Li, Guoqi Zheng
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 Na
Keywords: Parallel Naïve Bayes regression model; model-based collaborative filtering; big data; Hadoop; MapReduce.
A Semi-Supervised Approach of Graph-based with Local and Global Consistency
by Yihao Zhang
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 regularization framework. In the graph construction, these labeled and unlabeled 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 utilized. In manifold regularization 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 labeled and unlabeled instances. Specifically, we design the algorithm that uses the normalized 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.
Keywords: Semi-supervised learning; graph construction; manifold regularization; data consistency.
Diabetes Index Evaluation Framework Based on Data Mining Technology: a genetic factor involved solution for predicting diabetes risk
by Yao Wang, Dianhui Chu, Mingqiang Song
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 DI (diabetes index), 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 ones own health conditions.
Keywords: data mining; diabetes evaluation framework; diabetes index; genetic feature; SVM; diabetes auxiliary evaluation system.
Voice Transmission through WiFi
by Shalini Goel, Vipul Garg, Deepak Garg, Manshiv Kathait
Abstract: In the current dynamic era of digital communication, one of the key requirements is of free connectivity. In addition to that, one of the most anticipated issues is that connectivity is not available in most of the areas. Also, at several geographical locations, it is not possible to install infrastructure based networks due to cost-effectiveness or non-vulnerability terrains (cellular blind spots like desert, battlefields, forests etc.) Therefore, application for free connectivity needs to be developed which can be applied to these infrastructure-less wireless standards (Wi-Fi, Bluetooth) to improve them. In the light of the above-mentioned discussion, an android application has been developed in the ongoing project for android based wireless devices and has been named as Wi-Fi_Intercom. Wi-Fi_Intercom uses classes which allows its user to connect with other connected users through Wi-Fi wireless standard using P2P (point to point) or WLAN connection as a means of communication between Android-based wireless devices. The application will also allow a mobile user to search and call other connected users within the Wi-Fi range through the mobile application. Each mobile device connects to a WLAN router and identifies itself in the routing table. The application was successfully developed and the issues as stated before were successfully resolved.
Keywords: WLAN; P2P; Protocols; Wireless standards; Connectivity; Wireless Infrastructure; Mobile Devices.
A Fuzzy Inference Based Trust Model Estimation System for Service Selection in Cloud computing
by Roney Thomas, Priya Govindaraj
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. Lot of services are provided by cloud, for users to meet their applications 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.
Keywords: Cloud computing; Trust; Service Selection; Fuzzy inference; FCL; jFuzzyLogic.
Facilitating social recommendation with collaborative topic regression and social trust
by Xiaoyi Deng, Feifei Huangfu
Abstract: Internet technologies foster the significant growth and development of social networks, which make users more dependent on online information regarding purchasing decision making. Therefore, social network information can be utilized 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 favors 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.
Keywords: collaborative topic regression; matrix factorization; social trust; trust propagation; recommender system.
Special Issue on: LISS 2017 Emerging Trends, Issues and Challenges in Big Data and Its Implementation
Automatic data analysis technique: Data mining tasks discovery based on the concept network
by Ai Wang, Xuedong Gao
Abstract: Rapid improvement of data processing technology gradually makes data preparation, modeling, evaluation develop into a kind of automatic computing technique, and various programmed tools can be utilized directly. However, the problem of data mining tasks determination still depends on people due to their intangible knowledge and experience. In this paper, we study this problem based on the cognitive psychology. First, we constructed a structural model, concept network, to describe knowledge and experience according to the mind map and concept map. After demonstrating the structure of mining tasks in a concept network, an algorithm for data mining tasks discovery is presented. Experiments show that the proposed method is able to discover all potential interesting tasks after constructing the concept network. Such results are consistent with fifty published papers.
Keywords: Task determination; automatic data analysis; clustering judgment theorem; classification judgment theorem; concept network.
Evolution of intellectual structure of data mining research based on keywords
by Yue Huang
Abstract: Data mining has made rapid progress in the past decade and detecting intellectual structure of data mining research is of great help to researchers. We retrieved 5380 papers, published in 11 leading journals of data mining defined by Google Scholar, from SCIE under Web of Science and Scopus databases between 2007 and 2016 to carry out bibliometric analysis. As indicated by the analysis on the evolution of keyword frequency, the research focus of data mining has shifted from such topics as association rule mining to large-scale complex networks. Matrices of high-frequency keywords were also built for different time periods, namely 2007 to 2016 for the whole picture during these years, 2007 to 2011 and 2012 to 2016 for two periods. Clustering results show there are four main data mining topics and the attention has been paid more to graph data mining and complex network analysis in the past 5 years.
Keywords: data mining; intellectual structure; co-word analysis; clustering; evolution analysis.
The Effects of Relationship Quality and Knowledge Sharing on Service Innovation Performance: Organizational learning as a mediator
by Zhaoquan Jian, MOHAMED A.L.I. OSMAN, L.E.I. LI
Abstract: Drawing on RBV and service dominant logic and using data collected from 243 companies, this paper aims at examining the interplays between relationship quality, knowledge sharing, organizational learning, and service innovation performance. This empirical research found that knowledge sharing and relationship quality were significantly related to organizational learning, that in turn significantly affected service innovation performance. Moreover, better relationship quality would yield improved knowledge sharing. Furthermore, we propose that organizational learning is a significant mediator through which knowledge sharing influences firm performance, and that relationship quality is also a critical factor that facilitates service innovation.
Keywords: Relationship Quality; Knowledge Sharing; Organizational Learning; Service Innovation Performance.
Time Series Similarity Measurement Based on Fluctuation Features and Application for Clustering
by Hailan Chen, Xuedong Gao
Abstract: In time series data mining the traditional time series similarity measurement does not consider the structural features of time series. In order to solve this problem, a time series similarity measure method based on fluctuation features is proposed in this paper. Firstly, wavelet analysis is used to extract the trends of time series. Then, an identifying fluctuation points method, that contains important fluctuation information of time series, is proposed. Finally, a similarity measurement of different length time series is put forward to calculate the distance between different fluctuation points sequences. The clustering experimental results show that this proposed method can reflect the trend features of time series more accurately.
Keywords: time series; similarity measurement; cluster; fluctuation features.
VC-Recom: Venture capital recommendation algorithm based on heterogeneous information network
by Sen Wu, Huifei Li, Liu Lu
Abstract: According to its characteristics, venture capital can be described as a typical heterogeneous information network, which includes multiple kinds of nodes and various relations. Getting hints from PathRank algorithm, this paper proposes VC-Recom, a recommendation algorithm based on heterogeneous information network, which helps investment companies find suitable start-up projects. Besides, the experimental results show that the proposed algorithm can produce more effective recommendation results for investment firmscompared with other methods.
Keywords: PathRank; venture capital recommendation; heterogeneous information network; meta path.