International Journal of Information Technology and Management (64 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.
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.
How to evaluate brand extension in the mobile Internet environment
by Wenlong Zhu, Shiye Wang
Abstract: How to successfully implement a brand extension has been a common topic among global enterprises. Mobile Internet (M-Internet), a new information and communication technology, creates suitable conditions for enterprise brand extension. However, most researches related to brand extension evaluation focus on traditional information technologies presently. Little research has addressed M-Internet. From the perspective of consumer, this study constructs brand extension evaluation model based on the Task-Technology Fit (TTF) and Aaker and Keller Model (A & K Model), and analyzes the influencing mechanism of brand extension evaluation by using the Structural Equation Modeling (SEM). The final results show that technical characteristics of M-Internet produce a positive effect on attitude of parent brand. Furthermore, attitude of parent brand influences the brand extension evaluation, brand trust and perceived fit positively. Lastly, brand extension evaluation is subject to positive impact of brand trust and perceived fit besides attitude of parent brand. The theoretical and practical implications of this study are discussed lastly.
Keywords: Mobile Internet; Brand extension evaluation; Structural Equation Modeling; Mediation effect.
QoE-based service differentiation: An analysis of the business implications for the mobile services market
by Luis Guillermo Martinez Ballesteros, Per Jonny Nesse, Jan Markendahl
Abstract: Mobile network operators (MNOs) face a future characterized with new challenges, such as growing data consumption, a slowdown in subscriber growth and reduced revenues due to the success of over-the-top providers. To remain competitive, MNOs must offer affordable services and provide innovative strategies to retain current customers. Quality of Experience (QoE) is a well-established methodology for measuring and understanding the overall level of customer satisfaction and has also been presented as a way to improve telecommunication services. Even though QoE can be used to solve problems, such as customer loyalty and optimization of network resources in mobile networks, there is still a lack of knowledge on how the MNOs can take advantage of QoE and its potential benefits. In this paper, we explored the implications of the incorporation of QoE feedback in mobile networks at the business level. The analysis, which is based on a combination of value network configuration and business model analysis of scenarios, shows that value-added offers of differentiated and personalized services can be seen as alternatives to generate new revenue streams in the mobile service market. An important finding from our study is that, due to the nature of the challenges facing the mobile services industry, a QoE analysis cannot be limited to only a technical discussion but needs to be combined with an informed analysis of the business implications of the proposed solution.
Keywords: Quality of Experience; Business Models; Mobile Networks; Service Differentiation.
Scrutinizing medical practitioners twitter feeds analysis
by Arushi Jain, Vishal Bhatnagar, Nilanjan Dey, Amira Ashour, Fuqian Shi
Abstract: Mining of social media data has found widespread applications in recent times. Twitter feeds and facebook posts are being used to device product marketing strategies, sentiment analysis, financial predictions and forebode alarming situations. Twitter feeds analysis can be applied for analyzing the behavior and experiences of medical practitioners. Doctors informal conversations on Twitter can provide deep insights about their work experiences, their concerns about the profession, their feelings- pathos or excitement they feel and the affecting conditions. In the present work, twitter feed of Doctors along with the Twitter hashtags are used to collect data from tweets with hashtags such as #DoctorProblems. Afterward, data analysis was performed to determine the major problems faced by the members of the medical fraternity. These problems were categorized into five main categories. Further, based on these categories, the multi-label Na
Keywords: Big data; Hadoop distributed file system; MapReduce; Naïve Bayes multilabel classifier; Tweets; Evaluation based measure; Label based measure.
Big Data Block Impact within Big Data Environment
by Ron Ziv, Oded Koren, Nir Perel
Abstract: Handling data is becoming more and more complex. A higher velocity of data is created as more people have access to data generating devices such as computers, mobile phones, medical devices, home appliances, etc. Data files, such as user activity logs, system logs and so on, are stored in HDFS big data platform in various sizes, considering the business requirements, infrastructure parameters, administration decisions, etc. Dividing the data files (in various volumes), without taking into consideration the HDFS predefined block size may create performance issues which can affect the systems activity. This paper presents how HDFS block design affects the performance of Apache Hadoop
Keywords: architecture; HDFS; performance; big data.
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: 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.
Research on Evaluation of Network Payment Security
by Xiang Xie, Ying Cui
Abstract: The paper collects the data about the network payment fraud through the questionnaire and then analyzes the data. We propose a prediction model based on the hybrid support degree Apriori algorithm, and find out the factors that are closely related to the success of cheating. Then we find out the nodes of the decision and their importance tree use the ID3 algorithm to construct the decision tree, construct and verify the evaluation index system of the network payment from the user's point of view, considering the results of association rules and decision tree, combined with principal component analysis and text mining results, and then determine the weight using entropy method. So we can determine the probability of being deceived according to the situation of different people, not only remind people to be vigilant, but also provide reference for the community.
Keywords: Apriori algorithm; ID3 algorithm; Internet payment security; styling; Evaluation System; Principal component analysis; entropy method.
Special Issue on: ICSS 2018 Advances of Service Science in Information Technology
A Construction and Self-Learning Method for Intelligent Domain Sentiment Lexicon
by Shaochun Wu, Qifeng Xiao, Ming Gao, Guobing Zou
Abstract: A new method of building intelligent sentiment lexicon based on LDA and word clustering is put forward in this paper. In order to make seed words more representative and universal, this method uses LDA topic model to build the term vectors and select seed words. The improved SO-PMI algorithm has been used to calculate the emotional tendency of each sentiment word. In addition, the domain sentiment lexicons automatic extension and update method is designed to deal with dynamic corpus data. Experiments show that the proposed method can build the sentiment lexicon with higher accuracy, and can reflect the change of words emotional tendency in real time. It is proved in this paper that this method is more suitable for processing a large number of dynamic Chinese texts.
Keywords: Sentiment Lexicon;SO-PMI algorithm;seed words;LDA Topic Model;word clustering; incremental text processing.
Comprehensive Evaluation of Cloud Services based on Fuzzy Grey Method
by Wenjuan Li, Jian Cao, Shiyou Qian
Abstract: Cloud-based applications have become more and more popular. However, it remains a big challenge to comprehensive evaluate the reliability and performance of cloud providers and services due to the reason that Cloud is extremely dynamic and uncertain, resources distributed, virtualized, and freedom of entry and exit. The grey theory is adapted to handle the problems of blurring and uncertainty. Therefore, based on the grey relational analysis and grey comprehensive evaluation method, this paper proposes a novel trust comprehensive evaluation method for cloud services, also a comprehensive user satisfaction evaluation method for the better selection of suitable providers. In addition, it discuss in detail the construction and calculation of the evaluation model by case study.
Keywords: Cloud computing; Grey comprehensive evaluation; trust; user satisfaction.
Predicting Service Collaboration for Users based on Data Variation Patterns
by Jiaqiu Wang, Zhongjie Wang
Abstract: Service collaboration allows the realization of more complicated business logic by using existing services. Nowadays, users use lots of services in their daily work. For example, developers use a large number of services (e.g., Stack Overflow, Github, Blogger, etc.) to develop programs. Services are used continuously. Since most of the users\' data is distributed in these different service providers, these data are separated from each other although they are correlated. If we coordinate different services based on these correlation data, we can provide users with seamless and effective support. This is very significant because it greatly increases users\' productivity. However, due to the segregation of data, it is difficult to coordinate different services based on data correlation. To deal with this challenge, we propose a novel deep recurrent neural network (runs in a centralized service) to predict future services collaboration and their generated data. The network captures the correlation between different data and discovers patterns of data variation by using multiple hidden layers, which are beneficial to services collaboration prediction. Extensive experiments are conducted on the real world data set. Experimental results show that our model significantly outperforms a few competitive baseline methods.
Keywords: Service Collaboration Prediction; Correlation Data; Data Variation; Deep Recurrent Neural Network.
A Caching Strategy Based on Dynamic popularity for Named Data Networking
by Meiju Yu, Ru Li
Abstract: Named Data Networking (NDN) is a prominent architecture for the future Internet. In NDN, routers have the capacity of in-network cache, which can completely improve network performance. However, the cache capacity in routers is limited and how to utilize the cache resources effectively is still a great challenge. To solve the problem, this study presents a dynamic popularity caching strategy based on additive increase multiplicative decrease for NDN (DPCA). DPCA takes content popularity and caching capacity into account and it utilizes AIMD algorithm to adjust the popularity threshold dynamically. At the same time, it also proposes a evict algorithm which takes the historical information of content popularity, the trend of content request and the interval from the last request time into account. The simulation results show that the DPCA strategy can effectively improve cache hit ratio, decrease network throughput and reduce the average hit distance compared with other schemes.
Keywords: named data networking; caching replacement policy; dynamic content popularity; additive increase multiplicative decrease; evict algorithm.
Log Automaton under Conditions of Infrequent Behavior Mining
by Xianwen FANG, Juan LI, Lili WANG, Huan FANG
Abstract: In the existing process mining methods, infrequent behaviors are often considered as noise is ignored, but some infrequent behaviors have an important role in business process management. Firstly, the knowledge of log automaton is applied to the low-frequency log to delete infrequent behavior in the logs; secondly, the processed logs are added into attributes. Then, the condition-dependent value of the communication characteristics of different module networks is compared with the threshold, and the effective infrequent log is retained to optimize the model. Finally, a practical case is applied, which indicates the effectiveness and validation of the proposed method.
Keywords: process mining; Log automaton; infrequent behavior; Conditional dependency measure.
A Dynamic Programming-based Approach for Cloud Instance Type Selection and Optimization
by Pengwei Wang, Wanjun Zhou, Caihui Zhao, Yinghui Lei, Zhaohui Zhang
Abstract: With the advantages of cloud computing gradually highlighted, users increasingly want to deploy their applications and services on the cloud to reduce costs and obtain high computing capacity. Nowadays, cloud providers
(e.g. Amazon, Microsoft) at home and abroad provide a large amount of cloud instance types optimized to fit different use cases, such as compute optimized and memory optimized. Due to the potentially large quantity of cloud instance types in the public cloud market, it is often a challenge for users to select an optimal set of cloud instance types subject to limited resource capacity. In this paper, a dynamic programming-based approach is proposed for cloud instance type selection, which can provide optimal combination of cloud instance types
to users. Experiments are performed based on real-world cloud information to evaluate the proposed method.
Keywords: cloud computing; cloud instance type; dynamic programming; selection and optimization.
Learning context-dependent word embedding based on dependency parsing
by Ke Yan, Jie Chen, Wenhao Zhu, Xin Jin, Guannan Hu
Abstract: Word embeddings constitute the basic method of text representation. Whether they are the input to a machine learning algorithm or the features used in a natural language processing application, such embeddings have proven helpful in solving various text processing tasks. In natural language texts, contextual information exerts a crucial influence on the semantics of word representations. In current research, most training models are based on shallow textual information and do not fully exploit deep relationships in sentences. To overcome this problem, this paper proposes the dependency-based continuous bag-of-words (DCBOW) model. This model integrates the dependency relationships between words and sentences into the context in the form of weights, thereby increasing the influence of specific contextual information on the prediction of target words. This method increases the abundancy of word context information and enhances the semantics of word embeddings. The experimental results show that relative to syntactic similarity, the proposed method highlights semantic relations and improves the performance of word representations.
Keywords: word embedding; context-dependent; dependency.
A Recommendation Algorithm for Point of Interest Using Time-based Collaborative Filtering
by Jun Zeng, Yingbo Wu
Abstract: Location-based social networks (LBSNs) make it possible for people to share their visited places by uploading the check-in information. To improve the efficiency of recommendation algorithm, researchers introduce check-in data into point of interest (POI) recommendation to help users find new and interesting place. However, some researches ignore the signification of time factor for POI recommendation in LBSNs. In this paper, we propose a time-based collaborative filtering algorithm according to the similarity between users which combines the global similarity during a long period and local similarity within a short time interval. The experimental results show that the method we proposed can get more accurate recommendation.
Keywords: location-based social networks; recommendation system; point of interest recommendation; time-based collaborative filtering;.
Special Issue on: Control and Management of Logistic Systems Based on Information Technologies
Big Data Prediction Method of Traffic Logistics Demands Based on Regional Differences
by Rongting Sun, Yiqun Guo
Abstract: Aiming at the optimization effect of traditional methods on logistics transportation, and the inaccurate prediction of logistics demand, this paper proposes a big data forecasting method for traffic logistics demand based on regional differences.Based on the regional differences, a linear statistical programming model for the prior data of traffic logistics demand data is established. On this basis, the association rule feature decomposition and average mutual information analysis are carried out for the traffic logistics demand big data.The BP fuzzy decision classification model is adopted for feature information clustering and information fusion processing of traffic logistics demand big data to optimize the big data prediction model. The simulation results show that the model has higher accuracy and better global convergence in the big data forecast of traffic logistics demand, which improves the overall forecasting ability, and the forecasting time is reduced by 12.8% compared with the traditional method.
Keywords: Regional difference; traffic logistics; big data prediction; fuzzy decision; linear planning model.
Research on Regional Spatial Logistics Information Integration Method Based on Big Data
by Xiangdong Chen, Gregory Kalra
Abstract: Aiming at the shortcomings of current regional spatial logistics, such as low efficiency of logistics resource utilization, high cost of logistics transportation and slow speed of goods transportation, a regional spatial logistics information integration method based on large data is proposed. Firstly, the integration principle and process of regional spatial logistics information are described. Then, the logistics transportation route optimization model is assumed. Finally, the integration of regional spatial logistics information is realized by using the logistics transportation route optimization model with time windows. The experimental results show that the proposed regional spatial logistics information integration method can improve the utilization efficiency of logistics resources, and the data consistency can reach 96.9%. When the number of goods is 10,000, the transportation cost of the proposed method is the lowest of 12,300 yuan, so the transportation time of the method is the shortest.
Keywords: Regional Spatial Logistics; Information Integration; Path Optimization.
Intelligent Classification of Logistics Multi-Distribution Resources Based on Information Fusion
by Xinxian Qiu
Abstract: Aiming at the problems of low recall rate and low precision of intelligent classification of logistics multi-distribution resources, an intelligent classification method of logistics multi-distribution resources based on information fusion is proposed. The discrete transformation of measurement equation and state equation is used to describe the state parameters of logistics distribution vehicles and roads. The state estimation component is used to estimate the state of logistics multi-distribution vehicles, and the state equation of logistics multi-distribution vehicles is obtained. The state error of multi-distribution vehicles is used to realize the intelligent classification of logistics multi-distribution resources. The experimental results show that this method has high recall rate and accuracy in intelligent classification of logistics multi-distribution resources, and can get more accurate classification results, at the same time, it takes less time to classify resources, which is conducive to promoting the development of logistics distribution technology.
Keywords: Information Fusion; Logistics Multi-Distribution; Resources; Intelligent Classification.
Research on Routing Optimization of Logistics Distribution Vehicle Based on Cloud Model
by Zhongmin Liu
Abstract: The path optimization problem is very important in logistics distribution. Under the current situation that urban traffic road congestion is serious and user service demand is gradually improved, logistics distribution path optimization is not the simple combination optimization problem. To this end, combined with the deep belief network and the cloud model, the cloud-based logistics distribution vehicle routing optimization algorithm is proposed. After pre-processing of the collected urban traffic data for repair, denoising, etc., the data is trained and learned using the Deep Belief Network Model (DBN). According to the manually set tag data and model training results, the road conditions of the logistics distribution route are predicted; then, the time-sharing weighted traffic network is established. Combined with genetic algorithm, the cloud model theory is introduced and to realize real-time solution and update of time-sharing weights of time-division weighted network paths. The global optimal solution is acquired and the optimal solution for the logistics distribution vehicle path is obtained. The experimental results show that the performance of the logistics model based on cloud model is better than the current algorithm.\r\n\r\n
Keywords: cloud model; logistics and distribution; path optimization;.
Design of Logistics Transportation Monitoring System Based on GPS/DR Combined Positioning Technology
by Wenlian Deng, Aida Maki
Abstract: Through the logistics transportation monitoring system, logistics companies and customers can grasp the status of cargo transportation in real time. The current research on logistics transportation monitoring has problems such as poor positioning accuracy, low actual load rate and high cost. Therefore, the logistics transportation monitoring system based on GPS/DR combined positioning technology is proposed. According to the actual needs of logistics and transportation monitoring, the overall framework of the system is designed. The designed system hardware has functional modules such as tracking, query, scheduling, monitoring center, and alarm of the transportation tool. According to the overall structure and hardware design of the system, the main process of the system operation is as follows: After receiving the GPS/DR combined positioning data, the coordinates of the current location of the vehicle are automatically calculated by the system. Every once in a while, the communication network will restore the received coordinate information and other related data. It is then registered with the electronic map of the system\'s underlying data module. At the same time, combined with the database of other functional modules, the transportation status is presented on the electronic map interface to realize the positioning monitoring of logistics transportation. The experimental results show that compared with the current research results, the proposed system has high positioning accuracy and real load rate, low transportation cost and high reliability.
Keywords: GPS; DR; location technology; logistics and transportation; monitoring system.
Design of Logistics Operation Management Algorithm based on Information Technology on Internet
by Lili Shao, Fuxian Huang, Yuzhen Yang
Abstract: Aiming at the problems of slow convergence speed, high cost and low search efficiency when ant colony algorithm is used to solve the distribution routing optimization problem in logistics operation management, a logistics operation management algorithm based on hybrid intelligent optimization algorithm is proposed and designed. Based on the establishment of a time-limited one-way distribution routing optimization model for logistics enterprise product and its constraints, artificial fish swarm is adopted. In the early stage, the artificial fish swarm algorithm is used to obtain the initial solution of the model, select the update strategy of ant pheromone concentration and improve the transition probability of ant state. In the later stage, the basic ant colony algorithm is improved by introducing the concept of crowding degree to improve the ability of ant optimization. The parameters affecting the performance of the algorithm are analyzed and set up, and the optimal solution of the model is obtained. The results show that the design algorithm is superior to the basic ant colony algorithm in terms of optimization efficiency, convergence speed and minimum logistics distribution cost.\r\n\r\n
Keywords: Internet; Information technology; Logistics operation management; Basic ant colony algorithm; Artificial fish swarm algorithm.
Integrated Equilibrium Planning for Emergency Logistics Warehouse Allocation based on Internet Plus Mode
by Chaosheng Han, Kim R. Thorup
Abstract: The development mode of integration of emergency logistics warehouse distribution affects the security and timeliness of emergency supplies supply. When using the current method to plan the integration of emergency logistics warehouse allocation, demand points have low satisfaction with the services provided by emergency logistics centers, high cost of transportation of emergency materials, long time of transportation of emergency materials, and problems of low service level, high transportation cost and low transportation efficiency. In the Internet plus mode, we propose an integrated planning method for emergency logistics warehouse allocation. Under the premise of service capacity constraint, operation cost constraint, land resource constraint and timeliness constraint, the costs and maximum service level as the objective function of the integrated planning for emergency logistics warehouse allocation are minimized, and a multi-objective integration equilibrium programming model for emergency logistics warehouse in Internet plus mode is built. The multi-objective equilibrium planning model for enterprises emergency logistics warehouse allocation is solved by genetic algorithm, and the optimal planning strategy is obtained to realize the integration of enterprises emergency logistics warehouse allocation. The experimental results show that the proposed method has high service level, low transportation cost and high transportation efficiency.\r\n\r\n
Keywords: Internet plus mode; emergency logistics; balanced planning.