Template-Type: ReDIF-Article 1.0 Author-Name: Orlando Belo Author-X-Name-First: Orlando Author-X-Name-Last: Belo Author-Name: Vasco Santos Author-X-Name-First: Vasco Author-X-Name-Last: Santos Author-Name: Bruno Oliveira Author-X-Name-First: Bruno Author-X-Name-Last: Oliveira Author-Name: Cláudia Gomes Author-X-Name-First: Cláudia Author-X-Name-Last: Gomes Author-Name: Ricardo Marques Author-X-Name-First: Ricardo Author-X-Name-Last: Marques Title: RAID-B2K, transforming BPMN conceptual schemas into Kettle execution primitives Abstract: There are many tools for designing and modelling <i>extract-transform-load</i> (ETL) systems, covering its entire development life cycle. However, the vast majority of them use proprietary methodologies, notations and tasks, which undermine their understanding and application. In this paper, we present a translation tool for conceptual models, with the ability to reduce the 'gap' that usually exists when we need to translate a conceptual model for an equivalent physical one. We will demonstrate that it is possible to automatically translate ETL conceptual models developed in <i>business process model and notation</i> (BPMN) into the environment of a specific ETL implementation tool (Kettle-Pentaho data integration). The BPMN models were built to produce schemes for a specific execution environment (RAID) allowing us to demonstrate the utility of the tool in the translation, validation and generation of the physical schemas which we designated as ETL skeletons - a set of execution primitives properly orchestrated. Journal: Int. J. of Information and Decision Sciences Pages: 3-18 Issue: 1 Volume: 10 Year: 2018 Keywords: decision support systems; ETL systems modelling and implementation; ETL conceptual models; business process model and notation; BPMN; ETL physical models; Pentaho Data Integration; Kettle. File-URL: http://www.inderscience.com/link.php?id=90666 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:1:p:3-18 Template-Type: ReDIF-Article 1.0 Author-Name: Nazanin Vafaei Author-X-Name-First: Nazanin Author-X-Name-Last: Vafaei Author-Name: Rita A. Ribeiro Author-X-Name-First: Rita A. Author-X-Name-Last: Ribeiro Author-Name: Luis M. Camarinha-Matos Author-X-Name-First: Luis M. Author-X-Name-Last: Camarinha-Matos Title: Data normalisation techniques in decision making: case study with TOPSIS method Abstract: Data normalisation is essential for decision-making methods because data has to be numerical and comparable to be aggregated into a single score per alternative. In multi-criteria decision-making (MCDM), normalisation must convert criteria values into a common scale, thus, enabling rating and ranking of alternatives. Therefore, it is a challenge to select a suitable normalisation technique to represent an appropriate mapping from source data to a common scale. There are some attempts in the literature to address the subject of normalisation, but it is still an open question which technique is more appropriate for any MCDM method. Our research contribution is an assessment approach for evaluating normalisation techniques. Here, we focus on six well-known normalisation techniques and on TOPSIS method. The proposed assessment process provides a more robust evaluation and selection of the best normalisation technique for usage in TOPSIS. Journal: Int. J. of Information and Decision Sciences Pages: 19-38 Issue: 1 Volume: 10 Year: 2018 Keywords: normalisation; TOPSIS; decision making; correlation; consistency; multi-criteria decision-making; MCDM; data fusion. File-URL: http://www.inderscience.com/link.php?id=90667 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:1:p:19-38 Template-Type: ReDIF-Article 1.0 Author-Name: Rami A. Kinsara Author-X-Name-First: Rami A. Author-X-Name-Last: Kinsara Author-Name: D. Marc Kilgour Author-X-Name-First: D. Marc Author-X-Name-Last: Kilgour Author-Name: Keith W. Hipel Author-X-Name-First: Keith W. Author-X-Name-Last: Hipel Title: Communication features in a DSS for conflict resolution based on the graph model Abstract: The novel decision support system GMCR+ is designed for encapsulating advanced communication features including the capabilities to define, analyse, and communicate models and analyses of a given conflict, thereby enabling it to support negotiation and the management of strategic conflict. A major feature of GMCR+ is its ability to visualise conflicts explicitly using enriched graph models. Other tools that facilitate communication are the automatic calculation of conflict parameters and the ability to export them to Excel. Moreover, a novel status quo analysis procedure enables an analyst to examine the possible evolution of a conflict from an initial (status quo) state to a specified outcome. Even if a win/win outcome exists, it cannot be a resolution unless it is reachable. Journal: Int. J. of Information and Decision Sciences Pages: 39-56 Issue: 1 Volume: 10 Year: 2018 Keywords: negotiation; communication; decision support system; DSS; graph model; graph model for conflict resolution; GMCR; conflict resolution. File-URL: http://www.inderscience.com/link.php?id=90668 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:1:p:39-56 Template-Type: ReDIF-Article 1.0 Author-Name: Francesca Di Donato Author-X-Name-First: Francesca Di Author-X-Name-Last: Donato Author-Name: Luciano Nieddu Author-X-Name-First: Luciano Author-X-Name-Last: Nieddu Title: A new non-parametric classifier to predict small-business failures in Italy via performance ratios Abstract: We considered the case of small-medium enterprises (SMEs) in Italy <i>introducing a new classifier to predict bankruptcy up to eight years prior to failure</i>. We considered a stratified random sample of 100 non-listed Italian SMEs, 50 of which filed for bankruptcy during the years 2000 to 2011. Results suggest that the proposed method more than holds its own when compared with standard non-parametric classification techniques. The performance of the proposed method based on recognition rate, sensitivity and specificity shows that the proposed technique is effective in predicting the failure of a firm up to eight years prior to the event. The high specificity makes the proposed technique very effective as a warning signal to determine if a firm is in distress with a sufficient enough time to take proper actions. The performance assessment has been achieved via cross-validation to get unbiased estimates of the performances. Journal: Int. J. of Information and Decision Sciences Pages: 57-76 Issue: 1 Volume: 10 Year: 2018 Keywords: constrained k-means; bankruptcy prediction; discriminant analysis; performance ratios; small-medium enterprises; SMEs; Italy. File-URL: http://www.inderscience.com/link.php?id=90669 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:1:p:57-76 Template-Type: ReDIF-Article 1.0 Author-Name: Partha Priya Datta Author-X-Name-First: Partha Priya Author-X-Name-Last: Datta Title: Decision framework for selecting last mile delivery performance in Indian e-commerce companies Abstract: Last leg delivery plays a crucial role in improving logistics efficiency and customer acquisition through improved service quality and time. When implemented successfully, it becomes competitive advantage and ensures long-term success of the business. Last leg or last mile delivery is gaining importance in recent times in India, due to growing prominence of e-commerce. Major challenges in last mile delivery in India are: identification of best possible route to the destination, identification of best possible time window to reach and increasing efficiency of logistics through adoption of best possible delivery method to reduce delivery period. This paper first carries out a comprehensive literature review of last mile delivery practices adopted by e-commerce companies worldwide and the different factors affecting last mile delivery performance. The paper then describes a framework identifying necessary and sufficient conditions for selection of effective last mile delivery practices in India for specific product types. Journal: Int. J. of Information and Decision Sciences Pages: 77-93 Issue: 1 Volume: 10 Year: 2018 Keywords: last mile delivery; decision parameters; electronic retail; India. File-URL: http://www.inderscience.com/link.php?id=90670 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:1:p:77-93 Template-Type: ReDIF-Article 1.0 Author-Name: Kaveh Khalili-Damghani Author-X-Name-First: Kaveh Author-X-Name-Last: Khalili-Damghani Author-Name: Elham Haji-Sami Author-X-Name-First: Elham Author-X-Name-Last: Haji-Sami Title: Productivity of steam power-plants using uncertain DEA-based Malmquist index in the presence of undesirable outputs Abstract: Energy generation is mixed with production of emissions, called undesirable outputs. Moreover, the values of inputs and outputs of criteria are not deterministic in real productions and usually mixed with a great amount of uncertainties during planning horizon. So, measuring the productivity in the presence of uncertainty and undesirable outputs is not a trivial task. In this paper, an uncertain data envelopment analysis (DEA)-based Malmquist productivity index (MPI) is developed in presence of undesirable outputs to assess the productivity of production. The theoretical properties of the proposed models are discussed. The proposed method is applied on real case study in ten steam electricity power-plants. Moreover, the changes in technical efficiencies and changes in technology during multiple periods which influence productivity are sensed using proposed approach. The regress and progress of a power plant is demonstrated during planning horizons and the cause of these are also illustrated. Journal: Int. J. of Information and Decision Sciences Pages: 162-180 Issue: 2 Volume: 10 Year: 2018 Keywords: uncertain data envelopment analysis; Malmquist productivity index; MPI; energy planning; energy productivity; steam power-plant. File-URL: http://www.inderscience.com/link.php?id=92422 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:2:p:162-180 Template-Type: ReDIF-Article 1.0 Author-Name: Mustafa Aljumaili Author-X-Name-First: Mustafa Author-X-Name-Last: Aljumaili Author-Name: Ramin Karim Author-X-Name-First: Ramin Author-X-Name-Last: Karim Author-Name: Phillip Tretten Author-X-Name-First: Phillip Author-X-Name-Last: Tretten Title: Data quality assessment using multi-attribute maintenance perspective Abstract: The paper proposes a model for data quality (DQ) assessment in maintenance. Data has become an increasingly important since most of the maintenance planning and implementations are based on data analysis. Poor DQ reduces customer satisfaction, leading to poor decision making, and has negative impacts on strategy execution. To improve DQ as well as to evaluate the current status, DQ needs to be measured. A measure for DQ could be an important support for decision makers. Multi-criteria decision-making (MCDM) methods can provide a framework for DQ assessment, however, they are not used in literature for DQ assessment. In order to assess DQ, the attributes or KPIs need to be defined, their hierarchy should be designed and the assessment model is proposed to evaluate these attributes. A case study is also presented in this paper. The study shows that MCDM methods could provide qualitative estimation for the quality of DQ attributes. Journal: Int. J. of Information and Decision Sciences Pages: 147-161 Issue: 2 Volume: 10 Year: 2018 Keywords: data quality; information; maintenance; eMaintenance; attributes; assessment. File-URL: http://www.inderscience.com/link.php?id=92423 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:2:p:147-161 Template-Type: ReDIF-Article 1.0 Author-Name: Mengni Zhang Author-X-Name-First: Mengni Author-X-Name-Last: Zhang Author-Name: Mengying Zhang Author-X-Name-First: Mengying Author-X-Name-Last: Zhang Title: The factors with interval target levels in data envelopment analysis Abstract: In reality, there widely exist kinds of factors that have interval target levels. Differing from traditional DEA, the factors are demanded to achieve the interval target levels for better performance. The paper gives a supplement to the situation that all target levels are able to be reached by DMUs. Furthermore, we measure the DMUs with interval-targeted factors though formulating new production possibility sets and models based on previous papers. This paper has great advantages in decision-making and applications. Firstly, the paper offers an approach to solve the problems that DMUs with interval-targeted factors. Secondly, interval target levels offer decision makers much more flexibility and inclusiveness than fixed target levels. Thirdly, they are applicable to many areas, such as education and resource allocation. Decision makers can use our studies on interval target levels to develop reasonable goals in practice. Graphs and examples are given to illustrate our thought. Journal: Int. J. of Information and Decision Sciences Pages: 95-115 Issue: 2 Volume: 10 Year: 2018 Keywords: data envelopment analysis; DEA; interval target levels; production possibility set; efficiency. File-URL: http://www.inderscience.com/link.php?id=92424 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:2:p:95-115 Template-Type: ReDIF-Article 1.0 Author-Name: Ehsan Noorollahi Author-X-Name-First: Ehsan Author-X-Name-Last: Noorollahi Author-Name: Dawud Fadai Author-X-Name-First: Dawud Author-X-Name-Last: Fadai Author-Name: Seyed Hassan Ghodsipour Author-X-Name-First: Seyed Hassan Author-X-Name-Last: Ghodsipour Title: A hybrid multi-criteria assessment framework to prioritise power generation technologies in Iran Abstract: This paper develops a hybrid multi-criteria assessment framework to determine Iran's energy status and prioritise different alternatives of power generation technologies based on both renewable and non-renewable resources with emphasis on sustainable development criteria. This issue is investigated by a hybrid model named as FANP-BOCR that integrates various concepts including analytic network process (ANP), benefits, opportunities, costs and risks analysis (BOCR) and fuzzy sets theory. Comprehensive analysis of strategic criteria and various sub-criteria under benefits, opportunities, costs and risks sub-network is done to evaluate the most suitable power generation technologies in Iran. The calculation has been done by Super decision software. The results show that, in the benefits, opportunities and costs networks, the most preferred three alternatives are hydroelectric, wind and natural gas combined cycle (NGCC). But in the risk network; NGCC, natural gas combustion turbine (NGCT) and natural gas steam turbine (NGST) have the highest priority. Journal: Int. J. of Information and Decision Sciences Pages: 116-146 Issue: 2 Volume: 10 Year: 2018 Keywords: analytic network process; ANP; BOCR; fuzzy; renewable energy; strategic planning; sustainable development; power generation technology. File-URL: http://www.inderscience.com/link.php?id=92425 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:2:p:116-146 Template-Type: ReDIF-Article 1.0 Author-Name: Nurul Aswa Omar Author-X-Name-First: Nurul Aswa Author-X-Name-Last: Omar Author-Name: Shahreen Kasim Author-X-Name-First: Shahreen Author-X-Name-Last: Kasim Author-Name: Mohd Farhan Md Fudzee Author-X-Name-First: Mohd Farhan Md Author-X-Name-Last: Fudzee Title: A review of semantic similarity approach for multiple ontologies Abstract: Measuring semantic similarity between concepts is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. Many methods have been proposed. This paper contains a review on the state of art approaches including structure-based approach, information content-based approach, feature-based approach and hybrid-based approach. We also discussed the similarity according to their advantages, disadvantages and issues related to multiple ontologies. Besides that, we also concentrated on methods in feature-based approach which we will be using as a mechanism to measure the similarity for multiple ontologies. Journal: Int. J. of Information and Decision Sciences Pages: 212-221 Issue: 3 Volume: 10 Year: 2018 Keywords: semantic similarity; feature-based; ontology; multiple ontology; cross ontology; heterogeneous sources. File-URL: http://www.inderscience.com/link.php?id=93921 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:3:p:212-221 Template-Type: ReDIF-Article 1.0 Author-Name: Ibrahim Zeghaiton Chaloob Author-X-Name-First: Ibrahim Zeghaiton Author-X-Name-Last: Chaloob Author-Name: Razamin Ramli Author-X-Name-First: Razamin Author-X-Name-Last: Ramli Author-Name: Mohd Kamal Mohd Nawawi Author-X-Name-First: Mohd Kamal Mohd Author-X-Name-Last: Nawawi Title: Measuring economic and environmental efficiency for agricultural zones in Iraq using data envelopment analysis Abstract: Data envelopment analysis (DEA) is a non-parametric linear programming based on method for evaluating performance of similar production units such as agricultural firms. Although the method is already extensively applied in many areas of economics, its use in environmental economics and related fields is still limited. The productivity of the agriculture sector in Iraq has yet to reach an acceptable level to control resources and increase production to meet the modern century requirements. The concept recognises the need to simultaneously raise yields, increase input use efficiency and reduce the negative environmental impacts of farming systems to secure future food production and to sustainably use the limited resources for agriculture. Accordingly, this paper proposes a novel approach to measure using DEA to evaluate five zones in production strategic crops. The significance of this objective lies in the fact that some of the zones have limitations while others adversely impact their environment. This paper also employs a model to determine the efficiency of one over zone over the others and to improve optimal mix of the resources and paths to improve the index of technical efficiency and eco efficiency. Incorporating provision of environmental goods as one of the outputs of the farm and reducing environmental pressures are also outlined. Journal: Int. J. of Information and Decision Sciences Pages: 235-248 Issue: 3 Volume: 10 Year: 2018 Keywords: strategic crops; data envelopment analysis; DEA; technical efficiency; economic efficiency; environmental efficiency; eco-efficiency; undesirable output; Iraq. File-URL: http://www.inderscience.com/link.php?id=93922 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:3:p:235-248 Template-Type: ReDIF-Article 1.0 Author-Name: Sandhya Author-X-Name-First: Author-X-Name-Last: Sandhya Author-Name: Rakesh Garg Author-X-Name-First: Rakesh Author-X-Name-Last: Garg Author-Name: Ramesh Kumar Author-X-Name-First: Ramesh Author-X-Name-Last: Kumar Title: Computational MADM evaluation and ranking of cloud service providers using distance-based approach Abstract: The advancements in the information technology have emerged a new approach in the field of distributed computing referred as cloud computing that has garnered tremendous popularity in the short time span. Cloud computing is one type of internet-based computing in which the service providers provide the various services and resources in the sharable mode to the cloud customers on demand. As a result of the rapid growth of this approach, most of the IT companies such as IBM, HP, Microsoft, Amazon, Google have started to offer various cloud services to the users. Due to the huge availability of the cloud service providers, it becomes extremely challenging from the customer's point of view to select the most suited cloud service provider (CSP). In this research, the problem of the CSP selection is formulated as multi-attribute decision making problem and distance-based approximation (DBA) method is anticipated to solve the problem. In order to validate the applicability of the proposed method, the results are compared with the well known methodology, namely analytical hierarchy process (AHP) and fuzzy-AHP. Journal: Int. J. of Information and Decision Sciences Pages: 222-234 Issue: 3 Volume: 10 Year: 2018 Keywords: multi-attribute decision-making; MADM; DBA; selection indexes; cloud service provider; CSP. File-URL: http://www.inderscience.com/link.php?id=93930 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:3:p:222-234 Template-Type: ReDIF-Article 1.0 Author-Name: G.P.T.S. Hemakumara Author-X-Name-First: G.P.T.S. Author-X-Name-Last: Hemakumara Author-Name: Ruslan Rainis Author-X-Name-First: Ruslan Author-X-Name-Last: Rainis Title: Geo-statistical index for reshaping the pattern of households emerging over the low lying areas of the Colombo Metropolitan Region Abstract: This study examines how individual households have been emerging across the low lying areas during the period 2005-2012 in Colombo Metropolitan Region and the process through which they have gradually established themselves as either stable or unstable households. Mass manipulation of geo-spatial factors in innumerable land plots has led to an increase in harmful environmental effects because the collective strength of human interference is very high. Hence, it is attempted to build a geo-spatial model that can be used as an index to understand how low lying area conversion caused by process of individual households. The individual household plot has been chosen as unit of analysis. Information of 294 households has been collected and data tested with a logistic regression. The model indicates accuracy about 92.2% together with high significance levels for eight variables. Predicted probability value of each household indicates the conversion ratio correctly together with its distribution map. Journal: Int. J. of Information and Decision Sciences Pages: 263-278 Issue: 3 Volume: 10 Year: 2018 Keywords: geo-statistics; low lying areas; GIS; spatial logistic model; spatial index; urban planning; urban housing. File-URL: http://www.inderscience.com/link.php?id=93931 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:3:p:263-278 Template-Type: ReDIF-Article 1.0 Author-Name: Gökhan Akyüz Author-X-Name-First: Gökhan Author-X-Name-Last: Akyüz Author-Name: Ömür Tosun Author-X-Name-First: Ömür Author-X-Name-Last: Tosun Author-Name: Salih Aka Author-X-Name-First: Salih Author-X-Name-Last: Aka Title: Multi criteria decision-making approach for evaluation of supplier performance with MACBETH method Abstract: Supply chain consists of all the processes from obtaining the inputs to shipping the final goods to customers. To survive in a competitive environment firms must efficiently manage this chain, minimise the supply risk, decrease their costs, and optimise their inventory level and response customer demands quickly. Measuring and evaluation of supplier performance is also as important as selecting the right supplier. In this study, a multi criteria decision-making method is proposed ranking the suppliers of an international company in Turkey. In the proposed model quantitative criteria (acceptable product rate, major fault, return rate and delivery performance) and qualitative criteria (production flexibility, capacity management, reliability, communication and ecological awareness) are used and it is solved with Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) method. Performances of the suppliers are ranked with M-MACHBET software and sensitivity analysis are also given to discuss further the solution. Journal: Int. J. of Information and Decision Sciences Pages: 249-262 Issue: 3 Volume: 10 Year: 2018 Keywords: MACBETH; supplier performance; multi-criteria decision-making; MCDM. File-URL: http://www.inderscience.com/link.php?id=93932 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:3:p:249-262 Template-Type: ReDIF-Article 1.0 Author-Name: Issam Hamdi Author-X-Name-First: Issam Author-X-Name-Last: Hamdi Author-Name: Emna Bouazizi Author-X-Name-First: Emna Author-X-Name-Last: Bouazizi Author-Name: Saleh Alshomrani Author-X-Name-First: Saleh Author-X-Name-Last: Alshomrani Author-Name: Jamel Feki Author-X-Name-First: Jamel Author-X-Name-Last: Feki Title: Improving QoS in real-time data warehouses by using feedback control scheduling Abstract: Nowadays the update frequency for traditional data warehouses cannot meet the objectives of real-time data analysis relying on data freshness. To alleviate this problem, the real-time data warehouse (RTDW) technology has emerged. A RTDW allows decision makers to access and analyse fresh data as fast as possible in order to support real-time decision processes. The RTDW must often deal with transient usage charges, due to the unpredictability of access to data. The purpose of this paper is two-fold: to maintain the behaviour of the RTDW at a stable state, as well as the reduction of the number of transactions responsible for not meeting their deadline. Moreover, we focus on optimisation techniques to speed up query processing; in particular, a query response time optimisation and storage space optimisation. This paper proposes our FCSA-RTDW architecture (feedback control scheduling architecture for RTDW) which deals with quality of service management by optimising the resources used and reducing significantly the RTDW overloads. Journal: Int. J. of Information and Decision Sciences Pages: 181-211 Issue: 3 Volume: 10 Year: 2018 Keywords: real-time data warehouse; RTDW; real-time transactions; quality of service; QoS; materialised views; data partitioning. File-URL: http://www.inderscience.com/link.php?id=93933 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:3:p:181-211 Template-Type: ReDIF-Article 1.0 Author-Name: Arkadiy I. Urintsov Author-X-Name-First: Arkadiy I. Author-X-Name-Last: Urintsov Author-Name: Vladimir V. Dik Author-X-Name-First: Vladimir V. Author-X-Name-Last: Dik Author-Name: Andrey S. Larionov Author-X-Name-First: Andrey S. Author-X-Name-Last: Larionov Title: Development of decision support systems through the contradictions of informational society Abstract: In this article, the authors have investigated the development of existing methods and computer applications used to support decision-making at different levels of a corporate management to setup controlling systems. An increased number of challenges when doing business and inadequate management reaction lead to accumulation of contradictions and issues which could be solved by using decision support systems (DSS) - the set of tools that support the process of decision-making. Development of the informational systems and applications, globalisation of economy and organisational maturity create background to new tools and methods used by DSS. Informational society sets up pre-requisites to the learning module incorporated into DSS structure to help to identify competency of the user, give recommendation to the more advanced use of it and allow to share experience collected by other users. The authors have presented original classification of the DSS types and stated paradigm of DSS development in informational society in the future. Journal: Int. J. of Information and Decision Sciences Pages: 279-296 Issue: 4 Volume: 10 Year: 2018 Keywords: decision support systems; DSS; diagnostic DSS; approximate reasoning DSS. File-URL: http://www.inderscience.com/link.php?id=95489 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:4:p:279-296 Template-Type: ReDIF-Article 1.0 Author-Name: Maria Leonilde R. Varela Author-X-Name-First: Maria Leonilde R. Author-X-Name-Last: Varela Author-Name: António Arrais-Castro Author-X-Name-First: António Author-X-Name-Last: Arrais-Castro Author-Name: Rita A. Ribeiro Author-X-Name-First: Rita A. Author-X-Name-Last: Ribeiro Title: A data fusion approach for business partners selection Abstract: Strategies of market diversification push companies to provide novel products and services to customers, belonging to new geographic and demographic segments. Additionally, market development strategies targeting non-buying customers in selected segments or new buyers in new segments may be paired with increased product diversification and improved business agility. To fulfil the requirements associated with manufacturing, a wider range of products and increased customised demands imply having a wider set of competences available. Most companies find it increasingly difficult to have all required competences in their internal structures; therefore, they need to rely on strategic business partnerships and suppliers to be successful. In this paper, we discuss a data fusion decision approach for supplier and business partner evaluation, which includes past, current and forecast information about business partners. This approach may prove vital for companies to establish strong collaborative business networks. Journal: Int. J. of Information and Decision Sciences Pages: 311-344 Issue: 4 Volume: 10 Year: 2018 Keywords: collaborative networks; supplier evaluation and selection; business strategies; dynamic multi-criteria model; data fusion; decision support methods and tools; fuzzy decision-making; uncertainty treatment. File-URL: http://www.inderscience.com/link.php?id=95494 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:4:p:311-344 Template-Type: ReDIF-Article 1.0 Author-Name: Manije Sanei Tabass Author-X-Name-First: Manije Sanei Author-X-Name-Last: Tabass Author-Name: G.R. Mohtashami Borzadaran Author-X-Name-First: G.R. Mohtashami Author-X-Name-Last: Borzadaran Title: A comparison of generalised maximum entropy and ordinary least square Abstract: The generalised maximum entropy (GME) estimation method is based on the classic maximum entropy approach of Jaynes (1957). It has the ability to estimate the parameters of a regression model without imposing any constraints on the probability distribution of errors and it is robust even when we have ill-posed problems. In this paper, we simulate two sets of data from regression model with different distribution for disturbance, standard normal and Cauchy distributions respectively. For this dataset, regression coefficients are obtained by GME and OLS methods and these techniques are compared with each other for some sample sizes. Moreover, we have used some prior information on parameters to obtain GME estimators. The estimation results of GME in the case of non-normal distributed are discussed here. Journal: Int. J. of Information and Decision Sciences Pages: 297-310 Issue: 4 Volume: 10 Year: 2018 Keywords: regression model; generalised maximum entropy; GME; Monte Carlo experiment; ordinary least square; OLS. File-URL: http://www.inderscience.com/link.php?id=95495 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:4:p:297-310 Template-Type: ReDIF-Article 1.0 Author-Name: Neetu Narwal Author-X-Name-First: Neetu Author-X-Name-Last: Narwal Author-Name: Sanjay Kumar Sharma Author-X-Name-First: Sanjay Kumar Author-X-Name-Last: Sharma Author-Name: Amit Prakash Singh Author-X-Name-First: Amit Prakash Author-X-Name-Last: Singh Title: Fuzzy rule-base optimisation using genetic algorithm for mobile web page adaptation Abstract: There is a global rise in use of mobile devices like mobile phones, PDA, palmtop, etc. for web browsing. Web page usually includes the scrolling that makes web browsing time-consuming. In this work, we used genetic algorithm based fuzzy inference system and utilised the power of genetic algorithm to optimise the fuzzy rules base web content classification. The content of the web page is partitioned into blocks and applies the genetic-based fuzzy inference system to discriminate the main block. The filtered main blocks are then reorganised on the device. As a result of our approach, the mobile web user is presented with the filtered web page content without noise which results in persistent content, fast accessing, and better utilisation of limited space. We implemented the system and result shows that the hybrid genetic-based fuzzy inference system provides better classification accuracy (93.57%) as compared with fuzzy inference system (78.55%) accuracy of classification. Journal: Int. J. of Information and Decision Sciences Pages: 345-364 Issue: 4 Volume: 10 Year: 2018 Keywords: genetic algorithm; fuzzy inference system; FIS; web page visual blocks. File-URL: http://www.inderscience.com/link.php?id=95496 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:10:y:2018:i:4:p:345-364