This concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments.
This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors’ previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource that adds value to this book.
The blockchain technology is currently penetrating different areas of the modern Information and Communications Technology community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect, i.e., solving the computational puzzle task. At the same time, the use of wearable and mobile devices may also become a part of eCommerce blockchain operation, especially during the on-charge time. The paper considers the possibility of using a large number of constrained devices to support the operation of the blockchain with a low impact on battery consumption. The utilization of such devices is expected to improve the system efficiency as well as to attract a more substantial number of users. This paper contributes to the body of knowledge with a survey of the main applications of blockchain for smartphones along with existing mobile blockchain projects. It also proposes a novel consensus protocol based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS) algorithms for efficient and on-the-fly utilization on resource-constrained devices. The system was deployed in a worldwide testnet with more than two thousand smartphones and compared with other projects from the user-experienced metrics perspective. The results prove that the utilization of PoA systems on a smartphone does not significantly affect the lifetime of the smartphone battery while existing methods based on PoW have a tremendous negative impact. Finally, the main open challenges and future investigation directions are outlined.
This article describes the issues of analysis and assessment of the human factor for predicting the violation committed by the locomotive driver when driving the electric rolling stock. An intelligent system overview for assessing the likelihood of a violation by a locomotive driver is given. Such a system can generate recommendations depending on previously committed violations. One of the tasks is to reduce the risk of locomotive safety devices malfunctions, which are part of the locomotive electrical equipment. The solution to the problem of predicting the occurrence of possible violations is solved using tools and machine learning algorithms. A model has been built that generates recommendations for the driver based on information about previously committed violations and several static characteristics of the locomotive driver.
We considered basic mechanisms of atmospheric particle acceleration and estimated the escape rates of ionospheric ions (H+ and O+) during the geomagnetic field reversal. It is assumed that during the reversal the Earth's magnetic field deviates from the current dipole configuration, and the quadrupole component dominates. The standoff distance of the quadrupole magnetosphere is about of 3 Earth's radii and therefore a magnetic shielding protects the atmosphere from sputtering and ion pickup but not from the polar and auroral winds.
Currently, there is an extensive set of bankruptcy prediction models, but almost all of them are classification based, i.e., they allow to estimate the posterior probability that a particular firm will fail, given its financial characteristics. The expected time to failure is not considered explicitly. On the other hand, there is a survival analysis that deals with the time of the occurrence of the event of interest (while this event may not occur during observation). However, despite its popularity in the medical and technical sciences, survival analysis is relatively rarely used in predicting financial failure. Even when it is applied, most authors use the simplest form of a model. The goal of our work is to evaluate the applicability of survival analysis to bankruptcy prediction. We compare a few state-of-art statistical and machine learning models using a real dataset. Our findings confirm that survival analysis allows (1) to extract from given data valuable information regarding the dynamics of risks and (2) to estimate the impact of features.
Auditors use behavioral red flags (BRFs) to examine which individuals are more prone to unwarranted behavior like corruption and asset misappropriation. Using a rich data set from the Association of Certified Fraud Examiners (ACFE), we analyze the impact of BRFs on loss sizes from asset misappropriation. We control for anti-fraud mechanisms established at the company level and other factors both at the individual and the firm level. Performing an exploratory factor analysis yields six factors for BRFs which capture the principal perpetrator’s situation at the private level and the workplace. A general wheeler-dealer attitude and financial distress significantly increase loss sizes. By contrast, we find no evidence that non-monetary private problems lead to higher losses.
The growing interest and expectations from the blockchain applica-tions attract many analysts to this issue. In what spheres of logistics and supply chain management blockchain is appropriate? What blockchain software solutions are available to companies now? This paper investigates the basic function-ality of the existing software solutions on the market, the comparative analysis of blockchain platforms used for developing the solutions for logistics is also carried out. The main trends of blockchain applications are identified, based on the analysis of the project experience on the use of blockchain, in logistics and supply chain management, in different countries. The problems, limitations and conditions of blockchain implementation are also determined.
The present increase of attention toward blockchain-based systems is currently reaching a tipping point with the corporate focus shifting from exploring the technology potential to creating Distributed Ledger Technology (DLT)-based systems. In light of a significant number of already existing blockchain applications driven by the Internet of Things (IoT) evolution, the developers are still facing a lack of tools and instruments for appropriate and efficient performance evaluation and behavior observation of different blockchain architectures. This paper aims at providing a systematic review of current blockchain evaluation approaches and at identifying the corresponding utilization challenges and limitations. First, we outline the main metrics related to the blockchain evaluation. Second, we propose the blockchain modeling and analysis classification based on the critical literature review. Third, we extend the review with publicly accessible industrial tools. Next, we analyze the selected results for each of the proposed classes and outline the corresponding limitations. Finally, we identify current challenges of the blockchain analysis from the system evaluation perspective, as well as provide future perspectives.
This paper summarizes practices of customer- driven services applied in the leading Russian bank to avoid the impact of financial sanctions (2014–2019). We show how economic sanctions and strict national policies triggered this bank to increase flexibility in customer care to attract more capital from their existing clients. The project comprised three stages: (1) to analyse requirements and to develop ‘‘as-is’’ state of processes; (2) to analyse best practices and to improve processes under the scope of flexibility and customer orientation; (3) to implement the new vision in ‘‘to-be’’ state and final verification. At the third research stage to assess the results of processes improvement in the bank within a year we have applied a set of methods based on data envelopment analysis which provides a multidimensional understanding of processes and new scopes of customer’s value profiles. We have found that process reengineering result could give the contribution already at the first month of implementation and argue the findings could be used to introduce flexible data-driven customer care and improve customer-related processes in organisations worldwide.
Highlight generation and subsequent video production processes are expensive when it is done by humans. The paper shows how the process can be automated. It defines the highlight generation problem, suggests and discusses five different approaches for solving this problem. Statistics-based approach is discussed separately in deep detail along with algorithm implementation elements.Web-based highlight generation and video production service requirements and architecture are identified and discussed, respectively.
This work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were compared with other well-known multi-objective genetic algorithms (e.g., MOEA, NSGA-II, SPEA2). Moreover, P-RCGA was aggregated with the developed simulation of the behavior of human agent-rescuers in emergency through the objective functions to optimize the main parameters of the evacuation process.
We propose an efficient algorithm based on boundary operator equations for the numerical simulation of time-dependent waves in 3D. The algorithm employs the method of difference potentials combined with the (strong) Huygens’ principle (lacunae of the solution). It can handle nonconforming boundaries on regular structured grids with no loss of accuracy and offers sublinear computational complexity.
Performance impacts of ordering and production control policies in the presence of capacity disruptions are studied on the real-life example of a retail supply chain with product perishability considerations. Constraints on product perishability typically result in reductions in safety stock and increases in transportation frequency. Consideration of the production capacity disruption risks may lead to safety stock increases. This trade-off is approached with the help of a simulation model that is used to compare supply chain performance impacts with regard to coordinated and non-coordinated ordering and production control policies. Real data of a fast moving consumer goods company is used to perform simulations and to derive novel managerial insights and practical recommendations on inventory, on-time delivery and service level control. In particular, for the first time, the effect of ‘postponed redundancy’ has been observed. Moreover, a coordinated production–ordering contingency policy in the supply chain within and after the disruption period has been developed and tested to reduce the negative impacts of the ‘postponed redundancy’. The lessons learned from experiments provide evidence that a coordinated policy is advantageous for inventory dynamics stabilization, improvement in on-time delivery, and variation reduction in customer service level.
The trend on electricity grids digitalization is gradually leading to the shift of busi-ness value towards more sustainable and efficient electricity services. Sustainability and efficiency are challenged by the increasing demand for electricity which is fol-lowed by a dramatic transformation of energy systems. While smart grids seem to be crucial in this process, there is a discrepancy in understanding the costs and benefits for the multiple actors involved. In addition, there are benefits of smart grids that cannot be measured directly in terms of money, such as higher energy system reliabil-ity or commitment to carbon reduction. Despite the rise of interest to the managerial aspects of smart grids implementation and development, many aspects remain out of the scope. This paper contributes to the research of smart grids by providing a con-ceptualized business model that would allow for value co-creation, delivery and cap-ture. A Russian energy sector perspective is primarily considered throughout the pa-per and the results are supported by evidence from interviews with of industrial ex-perts
In this paper we propose an approach for compact storage of big graphs. We propose the preprocessing algorithms of a certain type of graphs which can signi cantly increase the data density on the disc and increase performance of fundamental operations with graphs.
Healthcare services are tightly connected with complex data analysis techniques to enable optimal resource allocation in medical institutions. This paper proposes a detailed analysis of incoming patient flow to local polyclinic by integrating clustering techniques, process mining and a concept of self-organizing systems. The study takes into account concepts based on models of managing social networks, the participants of which today can be both people and intelligent software. How could patient flow model be developed using a clinical pathways approach that combines clinical pathways tool, social media analysis, hierarchical agglomerative clustering method and probabilistic topic modeling to investigate the optimal resource utilization of medical facility? The methodology to answer this research question was demonstrated using a time- series clustering (kmedoids, Ward's method, Latent Dirichlet Allocation, Additive Regularization of Topic Models), Naive Bayes classifier based on public real data of 64668 depersonalized patient- doctor of 32 specialties conversions. In this paper, a modeling methodology for heterogeneous patient flow segmentation is proposed. The presented approaches serve as the foundation for the further development of a queuing system model of a medical institution. In addition, the shared economy principles are applied by the development of such service that would reduce the workload of appointments to therapists by matching patients to needed doctors.
The rapid development of information technologies and their widespread use in various sectors of the economy, including transportation, have led to a massive growth in data flows. However, the paradox of our time is that this data is not used to its full potential to provide companies with powerful impetus for their development. This is especially true at the strategic level, where company executives still mostly make decisions without relying on the recommendations of business intelligence systems. In the management of road transport, this phenomenon can be attributed to the fragmentation of data sources and the vague understanding of the relationships between them. This is also partially due to the diversity in opinions in the expert community on the efficiency of the transport processes. Thus, it is obvious that, without deciding on how to measure efficiency, it is hard to say where the data for calculations should be acquired from and what should be the architecture of a decision support system. Note that the diversity of data sources in the digital age creates prerequisites for various metrics that highlight different aspects of the transportation process.
Pharmaceutical market integration is highly dependent on digital technologies in general and the Internet in particular. The work aims are to study the available software for creating Electronic common technical documents and the possibility of its application on the Eurasian Economic Unionmarket.To obtain information, the surveywas performed based on employees of pharmaceutical companies, which were interviewed to identify the software they are using or plan to use. Based on the obtained results, a list of preferred software was compiled. It contains 9 software products. As part of the second phase of the study, a survey of developers of designated software solutions was conducted. Results showed that for pharmaceutical companies operating in the Eurasian Economic Union region, the issue of software readiness for working with the requirements of domestic regulators is of particular importance. Most foreign software products can be localized only after significant modifications. Domestic software solutions are just beginning to appear and in some cases are highly specialized. For example, programs for planning and meeting regulatory deadlines in the market are represented by single products.
This book gathers the best papers presented at the first conference held by the Russian chapter of the Association for Information Systems (AIS). It shares the latest insights into various aspects of the digitalization of the economy and the consequences of transformation in public administration, business and public life. Integrating a broad range of analytical perspectives, including economic, social and, technological, this interdisciplinary book is particularly relevant for scientists, digital technology users, companies and public institutions.
The ripple effect refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain (SC) as a result of a severe disruption. The bullwhip effect refers to operational dynamics and amplifies in the upstream direction as ordering oscillations. Being interested in uncovering if the ripple effect can be a driver of the bullwhip effect, we performed a simulation-based study to investigate the interrelations of the structural and operational dynamics in the SC. The results advance our knowledge about both ripple and bullwhip effects and reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction. The findings show that the ripple effect influences the bullwhip effect through backlog accumulation over the disruption time as a consequence of non-coordinated ordering and production planning policies. To cope with this effect, a contingent production-inventory control policy is proposed that provides results in favour of information coordination in SC disruption management to mitigate both ripple and bullwhip effects. The SC managers need to take into account the risk of bullwhip effect during the capacity disruption and recovery periods.