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The purpose of this paper is to propose an objective method for technology forecasting (TF). For the construction of the proposed model, the paper aims to consider new approaches to patent mapping and clustering. In addition, the paper aims to introduce a matrix map and K‐medoids clustering based on support vector clustering (KM‐SVC) for vacant TF.

TF is an important research and development (R&D) policy issue for both companies and government. Vacant TF is one of the key technological planning methods for improving the competitive power of firms and governments. In general, a forecasting process is facilitated subjectively based on the researcher's knowledge, resulting in unstable TF performance. In this paper, the authors forecast the vacant technology areas in a given technology field by analyzing patent documents and employing the proposed matrix map and KM‐SVC to forecast vacant technology areas in the management of technology (MOT).

The paper examines the vacant technology areas for MOT patent documents from the USA, Europe, and China by comparing these countries in terms of technology trends in MOT and identifying the vacant technology areas by country. The matrix map provides broad vacant technology areas, whereas KM‐SVC provides more specific vacant technology areas. Thus, the paper identifies the vacant technology areas of a given technology field by using the results for both the matrix map and KM‐SVC.

The authors use patent documents as objective data to develop a model for vacant TF. The paper attempts to objectively forecast the vacant technology areas in a given technology field. To verify the performance of the matrix map and KM‐SVC, the authors conduct an experiment using patent documents related to MOT (the given technology field in this paper). The results suggest that the proposed forecasting model can be applied to diverse technology fields, including R&D management, technology marketing, and intellectual property management.

Most TF models are based on qualitative and subjective methods such as Delphi. That is, there are few objective models. In this regard, this paper proposes a quantitative and objective TF model that employs patent documents as objective data and a matrix map and KM‐SVC as quantitative methods.

The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.

A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.

To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.

This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.

The purpose of this paper is to improve decision quality, and therefore project and portfolio success, by testing the influence of different visual representations of interdependency data in a simulated decision experiment. A network mapping approach to visualize project interdependencies is introduced and compared with matrix and tabular displays.

A simulated decision task in a controlled classroom setting tested five hypotheses though a sample of 480 experiments.

The type of data representation used is associated with differing levels of decision quality, and the use of network mapping displays is aligned with the best results.

The findings are limited as this experiment-based study presented a simplified decision scenario and involved students rather than practicing managers. The findings are best interpreted in combination with organization-based research.

The findings of this study suggest that visual data displays, particularly network mapping displays, can provide benefits and improve project portfolio decision quality. Managers may draw upon this study to design ways to include visual data representations in their project portfolio management decision processes.

This study uses experimentation to complement organization-based studies to better understand the influence of different methods of visualizing data and managing interdependencies between projects. This research provides an important contribution to meet the acknowledged need for better tools to understand and manage project interdependencies.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

The purpose of this study was to develop a new folding method for modeling complicated folded fabric with surfaces of revolution.

Irregular wrinkles and mesh distortions easily appear in the fold modeling of a complex curved surface. Aimed at this key technical problem, the segmentation mapping folding method (SMFM) is proposed in this paper. First, high-precision flattened planes were obtained by using segmentation mapping techniques. Second, the segmented planes were transformed into a folded and continuous geometric model by using matrix transformations. Finally, initial stress was used to modify the geometric folding errors, which ensured agreement with the inflated flexible fabric’s geometry and the original design.

Compared with the traditional folding method, SMFM has the advantages of good finite-element mesh quality, large radial compression rate, regular folds, etc. The surface area error and the volume error of the inflated single torus established by SMFM were only 1.2 per cent, showing that SMFM has high modeling accuracy. The numerical results of an inflatable re-entry vehicle are presented to demonstrate the reliability, feasibility and applicability of SMFM. Moreover, the stress modification reduced the problems of stress concentration and mesh distortions, improving the accuracy and stability of the numerical calculations.

In this paper, for the first time, a folding method for modeling complicated folded fabric is proposed. This methodology can be used to model the multidimensional compression and regular folds of complex surfaces of revolution that cannot be flattened and to improve the accuracy and stability of the numerical calculations.

Properly allocating an organization's activities within a building is vital to reducing the relational complexity arising from process–environment interactions. Multiple relationships are mapped, and certain interferences are only identified after these have been processed. The method/software employed for this task is Mapping Activity Environment Allocation (MAEA). However, data input and interpretation of results depend on the usability conditions of the organization's agents. This paper presents MAEA's usability test results.

Test sessions and interviews were carried out with seven agents registered at a University Hospital. Participants were instructed to think aloud during its use, and immediately afterward, responded to semi-structured interviews. Test sessions were audio recorded and screen captured.

Participants found the software easy to use and pointed out valuable implications for professional and academic use. In addition to relationship, priority and parallelism data, customized visualizations were created, including organizational charts, flowcharts and activity flow routes on the floor plan.

MAEA's simplicity allows non-designers to conduct evidence-based assessments and decisions. It allows designers to test their proposals during the programming and outline proposal stages.

A more detailed definition of design requirements from the beginning increases the conditions to successfully achieve project goals.

The ability to map the allocation of activity-spaces in the pre-design phase of building architecture allows for early identification of interactions, aiding in the development of more robust project requirements during programming.

The purpose of this paper is to identify the current problems and difficulties in automotive project management and explore solutions to improve its efficiency and effectiveness. To meet the industry demands for new project management methods, a strategy‐based scorecard concept was developed to monitor and control collaborative projects, to measure their performance, and to manage risks. The method aims to improve the project management performance of cross‐company projects.

The development of collaborative strategy maps and integrated measures for project control based on key performance indicators (KPIs) is an essential step for successful project management with a collaborative project scorecard. The concept was developed and evaluated on the basis of two workshops organised by the German Project Management Association (GPM e.V.) and interviews with project managers of automotive projects (manufacturers and suppliers).

The application of a balanced scorecard to cross‐company projects in the automotive industry can be facilitated by an impact matrix to develop strategy maps. Examples of collaborative strategy maps, project objectives, and KPIs to manage automotive projects are given and advantages, disadvantages, and limitations discussed. The paper discusses the possibilities of improving project management efficiency and effectiveness, as well as increasing project transparency with a CPS.

This paper fulfils the need to align collaborative project objectives with cross‐company business strategies in the automotive industry. The application of a balanced scorecard to cross‐company projects in the automotive industry is a new approach and would be of interest to all project managers aiming to increase transparency in their projects.

The purpose of this paper is to accomplish a bibliometric analysis, investigate the underlying knowledge structure, founding and development, and evolution of the Journal of Knowledge Management (JKM) through its articles published between 1997 and 2020.

A total of 1,346 research papers from JKM were selected and VantagePoint^® software was used to generate bubble maps, auto-correlation maps, and matrix maps through techniques such as principal component analysis (PCA) and natural language processing (NLP). The analysis gives insights about the foundation of knowledge structure, its evolution and the development of JKM.

The systematic mapping of research illustrates topics emerging as new offshoots, global favourites, saturated and plateaued and reached academic maturity. The USA, the UK, Australia, Spain, Italy, China, Canada, Germany, and France have contributed the most to JKM. This paper provides a robust roadmap for future research investigation of JKM.

The authors humbly admit the possibility of overlooking some research papers while evaluating and filtering the database of JKM. The research outcome summarizes 23 years, subject to information retrieval from archival files.

This research is a detailed bibliometric analysis explaining paradigm shifts in the body of knowledge of JKM. The bibliometric outcomes can act as beacons for future researchers and academicians to revisit the current trends that shape the domain of knowledge management, particularly for the JKM audience with a focus on contemporary topics of research interest.

This is a unique endeavour to accomplish a systematic bibliometric analysis of the JKM for two decades, offering insights about its structural body of knowledge through an overview of the chronology of scholarly development in the field of knowledge management.

This paper aims to develop a novel risk analysis model that uses both participatory and computerized techniques to capture and model the dynamic of risk impact propagation and interaction pattern.

In this research, an integrated model, applying modified participatory method and novel dichotomize procedure in the perspectives of social network topological analysis, is developed.

Based on the analysis output, it is found that; (i) the risk propagation is characterized by its dynamic and non-linear impact pattern, and (ii) the risk interaction is distinguished based on the degree of connectedness between various risks.

This research assumes that the risk impact propagation and interaction pattern within the risk network are static. Further research is required to analyze the risk network in dynamic circumstances.

This research contributes in delivering practical tools that could potentially provide a further path for developing mitigation strategy and policies that seek to address the complexity of risk phenomena, and thus enhance community resilience.

This research reveals some underlying patterns of how the risk impact propagation and interaction pattern are structured. Thus, it can help decision-makers make formal arrangements of particular urban infrastructure (UI) governance visible toward building risk plan and mitigation strategies.

This research contributes to filling the risk management knowledge gap. It is suggested that analyzing risk using a network approach is suited to capture the intricate processes that shape the complexity of UI risk structural network. By validating the model, this research shows the applicability and capability of the model to improve both the RA accuracy and decision making effectiveness towards risk mitigation plan and strategy.

This research project focuses on developing techniques and technologies for automatically identifying human faces from images in the situations where face sample collections in the database as well as in the input query images are “as is”, i.e. no standard data collection environment is available. The developed method can also be used in other biometric applications.

The specific method presented in this paper is called scale independent identification (SII). SII allows direct “comparison” between two images in terms of whether the two objects (e.g. faces) in the two images are the same object (i.e. the same individual). SII is developed by extensively using the matrix computation theory and in particular, the singular value decomposition theory.

It is found that almost all the existing methods in the literature or technologies in the market require that a normalization in scale be done before any identification processing. However, it is also found that normalization in scale not only adds additional processing complexity, but also may reduce the identification accuracy. In addition, it is difficult to anticipate an “optimal” scale in advance. The developed SII complements the existing methods in all these aspects.

The only limitation which is also the limitation for many other biometric identification methods is that each object (e.g. individual in human face identification) must have a sufficient number of training samples collected before the method works well.

SII is particularly suitable in law enforcement and/or intelligence applications in which it is difficult or impossible to collect data in a standard, “clean” environment.

The SII method is new, and the paper should be interesting to researchers or engineers in this area, and should also be interesting to companies developing any biometrics‐based identification technologies as well as government agencies.