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In the field of information systems, workflow modelling has attracted a lot of researchers. Most works in the domain do not take into consideration the concrete execution of resulting workflows. This failure puts beside the time and resource concepts. The aim of this paper is to develop a model that allows to deal with the execution of workflows based on the constraints of the resources.

Based on the domain engineering approach, one describes models of a business processes, and resources in an incremental manner. At each step of the modelling, one defines some requirements for the validation of the models.

The formalization gives the core features that are suitable to deal with the organisational aspect of business process management. These features are generic as they can be extended to capture the representation of various stages in the resource and business process management.

This paper does not deal with the concrete assignment of tasks to resources, and does not show how these features can be refined in order to model real world business processes. This work can be extended by defining some case studies, developing a supporting tool and carrying analysis.

By applying these models in daily work, enterprises will improve their productivity and deal with the competitive pressure of the network economy.

This paper defines an ontological framework for the business process, resource and correctness of a workflow within an enterprise that may be used by enterprise managers in the delivery of goods and services.

Previous studies on special dielectrics for multilayer screen printing led to defining a new concept for standard alumina substrate. The modification of the process and structure permits an increase of the thermal conductivity. The thermal conductivity of this new material is between that of alumina and BeO. It is possible to increase the thermal conductivity of 94% standard alumina (used in chip‐carriers) up to 3·6 times. It it known that the thermal conductivity of beryllia is 7 times higher than that of alumina. The thermal model gives an increase of 4 times for other configurations non‐tested in this study. With this material, the other characteristics such as thermal expansion, adhesion of the conductors, etc., scarcely change. The ‘percolation’ effect of the physical properties can usually be found with the addition of another material inside the matrix. In this particular case, the material is not submitted to the percolation law. Different configurations of metallic insert alumina with beryllia are compared by a simulation programme. The main applications are in the field of electronic packaging such as chip‐carriers with higher thermal dissipation and substrates for power devices. Since the process used to produce this new material is based on standard operations well known by alumina manufacturers, the cost is potentially much lower than for BeO.

The purpose of this paper is to explore various limitations of conventional mining systems in extracting useful buying patterns from retail transactional databases flooded with Big Data. The key objective is to assist retail business owners to better understand the purchase needs of their customers and hence to attract customers to physical retail stores away from competitor e-commerce websites.

This paper employs a systematic and category-based review of relevant literature to explore the challenges possessed by Big Data for retail industry followed by discussion and implementation of association between MapReduce based Apriori association mining and Hadoop-based intelligent cloud architecture.

The findings reveal that conventional mining algorithms have not evolved to support Big Data analysis as required by modern retail businesses. They require a lot of resources such as memory and computational engines. This study aims to develop MR-Apriori algorithm in the form of IRM tool to address all these issues in an efficient manner.

The paper suggests that a lot of research is yet to be done in market basket analysis, if full potential of cloud-based Big Data framework is required to be utilized.

This research arms the retail business owners with innovative IRM tool to easily extract comprehensive knowledge of useful buying patterns of customers to increase profits. This study experimentally verifies the effectiveness of proposed algorithm.

Since, the middle of the 1990s, UNO has been funding a large‐scale project aimed at designing a family of natural language (NL) processing systems transforming the sentences in various NLs into the expressions of a new language‐intermediary called the Universal Networking Language (UNL) and vice versa. The purpose of the paper is to propose a constructive way of developing a semantic networking language (SNL) of a new generation and, as a consequence, to bridge a gap between UNL‐based studies and Semantic Web projects.

The methodological basis of the paper is a new theory of designing semantic‐syntactic analyzers of NL texts elaborated by the author of the paper and called the theory of K‐representations (knowledge representations). One of its basic components is a mathematical model describing a system of ten partial operations on conceptual structures and determining a new class of formal languages called restricted standard knowledge languages (RSK‐languages).

It is shown that the expressive possibilities of RSK‐languages surpass the expressive possibilities of UNL from the standpoint of representing the meanings of discourses, compound goals, descriptions of sets, definitions of notions. It is proposed to use the definition of the class of RSK‐languages as a model of a SNL of a new generation in comparison with UNL.

It is also proposed to use the definition of RSK‐languages for building semantic annotations of arbitrary web‐documents and web‐services.

The paper describes an original approach to representing conceptual structure of NL texts.

Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular.

It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications.

The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works.

This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems.

DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

The purpose of this paper is to describe and analyze the first experiences (activities, attitudes and inclinations) of an undergraduate student team with eco-design activities.

Undergraduate students of an industrial design course were invited to participate in the design project. The activities of students were carried out in the class titled in Japanese “Sogo Project” (Overall Project). The project is experimental learning based on pedagogical case studies that students propose practical designs with a sustainable approach.

According to the activities and attitudes of the student team, they showed interest in focusing on sustainable consumption and consequently leant towards a socio-cultural rather than a technological eco-design approach in their works. The barriers to design education for sustainable design were found, and the student team expressed that the available support tool during their design process was complex. They also expressed that the tool was not compatible with their academic skills and background.

This paper has limited participants, resources, time and contextual scale. Few Japanese educators are skilled in eco-design, and eco-design modules are also poorly integrated into undergraduate and graduate industrial-design courses at Japanese universities.

The paper contributes to an initial discussion in the field of Japanese industrial-design education regarding the principles of and barriers to design education for sustainability.

Our paper is intended to show that System Dynamics can be a priceless tool for realizing long‐term models which will help to choose the appropriate policies in socioeconomic systems. According to this idea, and by using System Dynamics, we have built and experimented with a model of the Spanish Financial System. So far, the results have been quite satisfactory.

Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT.

A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands.

The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s.

Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic.

The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time.

A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

The Securities Exchange Commission mandated eXtensible Business Reporting Language (XBRL) filing data provide immediate availability and easy accessibility for both academics and practitioners. To be useful, this data should provide information for decisions, specifically, investment decisions. The purpose of this study is to examine whether the XBRL database can be used with models, developed in previous studies, predicting the directional movement of earnings. The study does not attempt to examine the validity of these models, but only the ability to use the data in the analysis of financial statements based on these models.

The study analyzes New York Stock Exchange companies’ XBRL data using a two-step logistic regression model. The model is then used to arrive at the directional movement of earnings between current and subsequent quarters. Additional models are created by dividing the sample into industry membership.

The results classified companies as realizing an increase or a decrease in earnings. The final model indicated a significant ability to predict earnings changes, on average about 65 per cent of the time, for the entire model, and 71 per cent, for the industry-based models (higher than those of previous studies based on COMPUSTAT). The investment strategy created average quarterly return between 2.8 and 10.7 per cent.

The originality of this study is in the way it examines the quality of XBRL data, by examining whether findings from prior research which relied on traditional databases (such as COMPUSTAT) still hold using XBRL data. The use of XBRL allows not only easier and less-costly access to the data but also the ability to adjust the models almost immediately as current information is posted, thus providing a much more relevant tool for investors, especially small investors.