Can anybody help? : mitigating IS development project risk with user

In this paper we aim to gain insight into the relationship between user participation modes and project risk factors, and then we construct a model that can be used to determine how user participation can be successfully applied in ISD projects with a given set of risk factors. We perform an in-depth literature review, which aims to clarify the concept of user participation as part of risk management. We then report on the results of a case study in Cap Gemini where we conduct an exploratory research of the application of user participation in practice. For this exploratory research, a quantitative and qualitative research method was designed in the form of a survey and interviews. Though the results from our case study we gain insight into the relationship between user participation and IS project risk and also determine how user participation can be used to mitigate such risk

Exploring the Impact of Socio-Technical Core-Periphery Structures in Open Source Software Development

In this paper we apply the social network concept of core-periphery structure to the sociotechnical structure of a software development team. We propose a socio-technical pattern that can be used to locate emerging coordination problems in Open Source projects. With the help of our tool and method called TESNA, we demonstrate a method to monitor the socio-technical core-periphery movement in Open Source projects. We then study the impact of different core-periphery movements on Open Source projects. We conclude that a steady core-periphery shift towards the core is beneficial to the project, whereas shifts away from the core are clearly not good. Furthermore, oscillatory shifts towards and away from the core can be considered as an indication of the instability of the project. Such an analysis can provide developers with a good insight into the health of an Open Source project. Researchers can gain from the pattern theory, and from the method we use to study the core-periphery movements

A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions

Enhancing vessel arrival time prediction: A fusion-based deep learning approach

The logistic community of shippers has struggled to predict the precise arrival time of the seagoing vessels with reliable certainty. While deep-learning approaches are promising, the existing methods fail to provide desirable results due to a shallow prediction architecture. This research work proposes a method to predict vessel arrival time that could eventually be incorporated into an intelligent decision support system that we call Vessel Arrival Time Prediction (VATP). VATP presents a hybrid architecture of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Attention mechanism, Dropout, and Dense layers to take advantage of the coarse-grained local features produced by CNN, the longitudinal nature of AIS (long-time dependencies) via LSTM and paying attention to the influence feature on the arrival time. While many prior approaches have relied solely on AIS data, and some incorporated a combination of AIS and vessel information, our method also integrates diverse data sources, including Automatic Identification System (AIS), Augmented information (Ain), and Maritime Weather Data (MWD). Furthermore, only a few methods have considered weather information, often using minimal weather-related features. In contrast, our approach involves a comprehensive range of weather-related features, and besides these data sources, we extracted specially crafted features. To verify the effectiveness of VATP, we conducted our experiment on large-scale datasets. The VATP model obtained a Root Mean Square Error (RMSE) of 10.63 and a Mean Absolute Percentage Error (MAPE) of 35.11 %. Our results demonstrate that VATP achieves significant performance. Furthermore, these positive results demonstrate that i) the accuracy of the VATP approach can be improved using the AIS, MWD, and Ain information; ii) learning from a unified feature set can result in a significant performance improvement compared to learning from a subset of the features; iii) we also obtained a superior performance in comparison with other well-known methods in the literature and various state-of-the-art baseline methods. Finally, our results illustrate the consistent performance of the VATP across different datasets

Methods and Applications of Data Mining in Business Domains

This Special Issue invited researchers to contribute original research in the field of data mining, particularly in its application to diverse domains, like healthcare, software development, logistics, and human resources. We were especially interested in how the data mining method was modified to cater to the specific domain in question. The challenge is that the more complex a domain is the harder it is to make good predictions, as more implicit domain knowledge is required that is not always available [1]. This is especially true in the case of complex domains where there are soft factors, like the interaction of the conflicting and cooperating objectives of the stakeholders [2,3], and system dynamics play a significant role [4]. In a business context, the challenge is that one would like to see (i) how the algorithms can be repeatable in the real world, (ii) how the patterns mined can be utilized by the business, and (iii) how the resulting model can be understood and utilized in the business environment [1]. Furthermore, the idea is to identify the variables that impact the goal variable but to do so with the data, interestingness, deployment, and general domain (business) constraints of the domain [1,5]. One of the methods to analyze a complex domain is using a method called intelligence meta-synthesis [6,7]. Intelligence synthesis is the collection and creation of perceived or understood (i.e., not necessarily objective) information. Meta-synthesis is the collection and creation of knowledge and information from collected intelligences [1]. The goal of this approach is to design and develop predictive models that could eventually be incorporated into a business intelligence dashboard. As a result, one would (i) understand the nature and origin of data that allows the system user to determine the quality of the data to perform the data cleaning; (ii) understand the factors in the domain that influence the predicted variable, leading the developer to determine which variables need to be included in the predictive model; (iii) develop predictive models that are usable and interesting within the domain in terms of predictive power, integrating with existing infrastructure, and integrating with business rules and processes; and finally (iv) use the predicted data to find the optimal business processes in the particular domain. There are also research works that have built on top of intelligence meta-synthesis, such as the study published by the authors of [1]

Analysing the trend of Islamophobia in Blog Communities using Machine Learning and Trend Analysis

Blogs have been instrumental in shaping public opinion, and constitute an important component of the burgeoning Social Media space. However, researchers have not considered the impact of blog posts and the comments on blog posts to understand public opinion on different topics. This article analyses the trend of Islamophobia in certain blog communities in UK, using public opinion from blog comments taken from a range of political blogs. A proportion of the blog comments were labelled manually, before being used to train an algorithm to label the remaining comments. The algorithms gave varying results, the best being a Bagging algorithm – which is an ensemble algorithm that combines multiple algorithms. After labelling these comments, we answered our research question: Can one identify the trend in Islamophobia by analysing blog comments and if it is related to terror attacks in a particular country? We concluded that there has not been a rise in Islamophobia, but that terror attacks in the UK and abroad caused spikes in anti-Islam comments on the blogs. The main contribution of our research is in demonstrating a method for analysing blog comments to identify the trend in Islamophobia in the blog communities of a country