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PurposeCustomer centricity has evolved into a success factor for many companies, requiring all corporate activities – including business processes – to be aligned with customer needs. With most existing approaches to business process (re-)design focusing on process efficiency, customers are often treated as second-class citizens. Despite emergent research on customer process management, there is a lack of guidance on how to design customer-centric business processes.Design/methodology/approachThe authors conducted a structured literature review and analyzed companies awarded for outstanding customer centricity to compile design heuristics for customer-centric business processes. The authors iteratively validated and refined these heuristics with experts from academia and industry. Finally, the heuristics was grouped according to their expected impact on interaction capabilities to enable their prioritization in specific settings.FindingsThe authors proposed 15 expert-approved and literature-backed design heuristics for customer-centric business processes together with real-world examples. The heuristics aim at increasing customer satisfaction with interaction-intensive core processes, which is an important driver of corporate success.Originality/valueThe design heuristics complement existing efficiency-centered (re-)design heuristics. They reflect cognitive shortcuts that support process analysts in the generation of innovative ideas during process (re-)design. The heuristics also add to customer process management and help put customer centricity into practice.

Process Management techniques are useful in domains where the availability of a (formal) process model can be leveraged to monitor, supervise, and control a production process. While their classical application is in the business and industrial fields, other domains may profitably exploit Process Management techniques. Some of these domains (e.g., people’s behavior, General Game Playing) are much more flexible and variable than classical ones, and, thus, raise the problem of predicting which activities will be carried out next, a problem that is not so compelling in classical fields. When the process model is learned automatically from examples of process executions, which is the task of Process Mining, the prediction performance may also provide indirect indications on the correctness and reliability of the learned model. This paper proposes and compares two strategies for activity prediction using the WoMan framework for workflow management. The former proved to be able to handle complex processes, the latter is based on the classic and consolidated Naive Bayes approach. An experimental validation allows us to draw considerations on the pros and cons of each, used both in isolation and in combination.

Business process management (BPM) is a holistic discipline which is focused on improving organizational performance by managing the business processes of the organization. In recent decades, it has been widely accepted and implemented across many different organizations with some success. However, there were some issues regarding the traditional approach to BPM, like the reality-model divide, etc. As a response, a new discipline, called social BPM has emerged which is based on the principles of social software. For the purpose of this article, a Delphi study has been conducted with the aim of defining the social BPM as well as to identify its main characteristics. The results are presented in this article, proposing a single definition of social BPM and the list of its characteristics.

Experimental teaching is an important part of college education, especially in engineering colleges. The process management of experimental teaching and the statistic analysis of the laboratory data are complicated and time-consuming. To solve the above problem, this paper develops an interactive laboratory information management system by utilizing emerging technologies such as big data, cloud computing and the Internet of Things. Based on the rich experience of experimental teaching and management of our National Experimental Teaching Demonstration Center, the system is designed to incorporate four functional modules. They are experimental teaching management, experimental equipment management, laboratory open management, communication and interactive management, respectively. Two years of practical operation shows that the system broadens the time and space of the experiment, improves the resource utilization and openness of the laboratory, standardizes the laboratory daily management, provides a good interactive platform for teachers and students, and most important of all, facilitates the statistic analysis of kinds of data. The interactive laboratory information management system adequately explores the functions of the laboratory and significantly improves laboratory operation efficiency.

Internet of things is a technological development which is playing salient role in making the world as “SMART”. Due to increasing use of gadgets and various technical advancements, it needs to upgrade the platform required for it. The heart of any IoT device is its operating system. IoT devices mainly exist in small size, use less memory and run with less energy having high throughput. These scant resources are run by specially designed dedicated small size operating system to reinforce IoT devices with varied applications and diversely useful specifications. In IoT devices operating system plays an important role to manage the needful components of operating system in a proper manner. This paper discusses IoT devices with its operating systems in concern with resource management techniques. Its major focus is power management, memory management, process management and scheduling algorithms.

Basing on traceability and tamper-resistant of blockchain, as well as unique identification of Internet of things, construct new pattern of power material information blockchain uploading scheme, building a waste material processing system based on blockchain technology can effectively manage the whole process of power materials, and carry out reliable traceability for material related information and the whole process, so as to achieve transparent and intelligent material procurement and whole process management.

The use of filesystems has become a standard, with a purpose well beyond just storing and accessing application data. For instance, common system security and compliance operations, sush as software or package installation, system and application configurations or process management also leverage a filesystem. Over decades, various system management and security tools have been designed to access system state and to implement their respective functions through a file interface. However, we observe that these tools do not require access to all the files in the filesystem and in some cases they can even work with incomplete file contents. Motivated by these observations, we propose filenail (or Filesystem Thumbnail) a system that exercises an incomplete filesystem state marshalling and un-marshalling protocol. We discuss the use of filenail to implement an effective and optimal disaggregated solution to perform common system security tasks for container clouds. In general, depending on the use-case not all the files in the filesystem are equal and that incomplete filesystem state can be often enough. The results of this paper show filenail is very efficient in capturing and transferring filesystem state of systems and enables implementing disaggregated security solutions in the cloud.

In this paper the Fuzzy Miner process modeling algorithm, which is a popular Process Mining technique supported by Disco Fluxicon was applied on real-life road traffic data collected from an Information System (IS) of police in a European country [1], [13]. The original data contained details of 150,370 Cases (or guilty drivers) and 561,470 Events (or actions) being executed, stored and captured (by the police IS) during the years 1999 to 2013. In addition, the data provided details about how the fine tickets (as a penalty) are created (by police), sent, notified, re-evaluated, justified and finally are paid by the guilty drivers. Accordingly, the main objectives of the study are as the following: (1) To evaluate the fine process models of the real-time road traffic data regarding both frequency-based and time-performance-based metrics through Disco Fluxicon as a Process Mining tool/platform. (2) To discover the existing flaws, drawbacks and problems of the current traffic fine (penalty) ticketing system with the intention of improving or benchmarking the system.

By investigating the status of data commodity delivery after the completion of payment transactions by mainstream data trading platforms at home and abroad, this paper proposes an end-to-end data commodity delivery solution based on metadata. Designed and developed a secure, automated and standardized data service API interface specification from the two aspects of data commodity content description and acquisition method, and recorded data products, transactions and delivery process information on the blockchain. Effectively support the registration and authentication of data products, transaction process management, data demanders' understanding and use of purchased data, and market supervision.

the implementation of Secure FI process by digitally by adapting the IoT based connecting many wireless smart devices may expose security vulnerabilities. The DF related to IoT based can be characterized as a field of study however ought to be utilized in defensive condition which is absence of the present world and the use of innovative and constant techniques for collect, preserve, validate, analyze, interpret and present the evidences extracted using the forensic tools and carefully separated from various sources and presenting official courtroom for justice and fair judicial processor legal cycle. The IoT smart devices will require appropriate procedural and ensured remote condition with safer and secure information reinforcement in place from secure home control to cutting edge city the executives connected networks. The smart gadgets will detect their condition with interconnected one another to shape canny brilliant spaces and any suspected or unapproved occasion will be put away and set off to the requirement office to heighten the issue or Forensic examination [1], [2]. These smart devices all things considered produce and store enormous sum delicate criminological information. The paper clarifies the current Process by zeroing in and improving on pre and post examination measure with more solid and secure cycle with back up Data stockpiling with proof. The information stream from Pre-examination to post examination and capacity or cloud-based focusing on key zones of character on irregular action registering impressions to the worker (pre-examination) when recognized and continue to post examination with assessment and sparing the information in to make sure storage.

İnternet kullanımının yaygınlaşması hem bireysel anlamda hem de organizasyonlar açısından web uygulamalarının kullanımını arttırmıştır. Buna bağlı olarak gönderilen, alınan, saklanan ve analiz edilen veri hacminde önemli bir artış olmuştur. Web uygulamalarındaki bu bilgiye sahip olmak için web uygulamalarına yapılan saldırılar ise her geçen gün artmaktadır. Kişiler veya kurumlar web uygulamalarına yapılan bu saldırıları önlemek amacıyla güvenlik önlemlerine gereksinim duymaktadırlar. Web uygulamalarının geliştirilmesi aşamasında ne kadar önlem alınırsa alınsın bazı güvenlik açıkları kaçınılmazdır. Bu nedenle geliştirme aşamasında alınacak önlemlerin yanında, web uygulamalarının sürekli kontrol ve denetim altında tutulması gerekmektedir. Web uygulamalarını test etmek için birçok yazılım geliştirilmiştir. Ancak bu testlerin gerçekleşmesi kadar web uygulamalarının güvenliğinin sürekliliğinin de sağlanması gerekmektedir. Web uygulamalarının sürekli olarak kontrolünün bireysel olarak yapılması neredeyse imkânsızdır. Bu kontrollerin testi gerçekleştiren yazılım tarafından planlanması sürekliliği sağlayacaktır.Bu çalışmanın genel amacı web uygulamalarının güvenliğini denetim altına alacak bir süreç modeli geliştirmektir. Bu anlamda web uygulamalarını test eden ve bunu süreçler haline getirebilen bir yazılım geliştirilmiştir. Geliştirilen yazılım, web uygulamaları üzerindeki güvenlik açıklarını bulan testleri gerçekleştirmektedir. Ayrıca bu yazılım, üzerinde bulunan süreç yönetimi bölümü ile bu testlerin planlanmasını ve kontrolden sorumlu kişilerin otomatik olarak bilgilendirilmesini sağlamaktadır., The spread of internet usage has been increased the usage of web applications with regards to both individuals and organizations. Attacks on web applications are increasing day by day in order to have this knowledge in web applications. Some security measures are needed to prevent these attacks on web applications by people or organizations. During development of web applications, some security holes are inevitable although how measures are taken. Therefore, besides these measures, web applications should keep control and audit continuously. Many software has been developed to test web application. However, besides these tests, it is necessary to provide the continuity of security of web applications. Continuity of control of web applications is almost impossible on an individual basis. Planning these controls by software that performs tests, provides continuity.The general purpose of this study is to develop a process model that will control the security of web applications. In this sense, a software has been developed that tests web applications and can turn it into processes. The developed software carries out tests that find vulnerabilities on web applications..Also, this software provides planning tests by means of process management part on it and informing people who are responsible for control automatically.

The physics/information fusion has become an important foundation for operation control of AC/DC power distribution system. In this paper, the operation control architecture of multi-terminal DC power distribution system based on IEC 61850 is proposed, including system management level, process management level, and process equipment level. The composition modules and operation control functions of the system management level are designed. The design method of software/hardware for intelligent electronic devices (IED) in the process management level is presented. Given the typical operation control strategies of the multi-terminal DC power distribution system, the IEC 61850 physics/information fusion model is extended. On this basis, the implementation method of system operation and control based on IEC 61850 is also elaborated. Further, the typical operation scenarios are verified by using the actual test platform. The research results show that the proposed operation control technology based on physics/information fusion is feasible.

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

The variable reconstruction method to establish the parameterized model of a certain kind of bearing-rotor system is proposed by integrating the multisource programs, parameterized modeling and collaborative process management, a co-simulation design platform is established of the rotor system. It can inherit, fuse and reuse all kinds of design knowledge, and integrate design and simulation software, and even manage the data of design, simulation and experiment. This platform can improve the design and research efficiency of the bearingrotor system.

Successful modernization of the Russian Federations healthcare system depends on the efficiency of organization processes transformation in healthcare and medicine on the basis of digital platforms. It will allow to provide constant control over expenses for achievement of careful rational expenditure of means, and also the new medical, organizational technologies promoting formation of economy (management of patients streams, telemedicine, providing information support of medical diagnostic processes and medical decisions, geoinformation systems application, systems of processing of medical images). The creation and active use of digital platforms will contribute to the introduction of a model of process management in the healthcare sector, which focuses on the performance of functions by departments and, as a consequence, meeting the needs of citizens. Digital healthcare platforms combine such information resources as laboratory results, electronic patient histories, standards of diagnosis and treatment of patients financial and economic information, databases of medicines, material and labor resources, expert systems, etc. Electronic services allow to use all modern possibilities of collection, storage and processing of information that promotes leveling of the errors connected with time delays of paper document circulation, increase of efficiency of use of working time of doctors, creation of comfortable conditions for interaction with patients.

Operating systems is the last required course in our undergraduate Computer Science major. Although our students have had two semesters of programming in C++, one in assembler, and a semester of data structures using the C++ Standard Template Library, many of them have weak mastery of some important programming topics. In addition, they have had minimal exposure to using these constructs with real world data structures. In this paper we describe how we have used the assignments in a Linux-based Operating Systems course to remedy these deficiencies while teaching a full semester of OS content. Assignments include retrieving data via standard API calls and from /proc; process management, including fork, wait and system; building a microshell with fork/exec and pipe/dup; semaphore handling; and building an OS simulator. We have rewritten these assignments to provide additional practice with data type conversion, pointers and memory management, file handling, classes and subclasses, and the use of vectors, maps and other data structures in the STL.

Our objective is to develop an approach based on case-based reasoning that detects and handles unforeseen deviations that occur in flexible workflow execution. With a case-based approach we aim at supporting the continuation of a deviant workflow execution by utilizing successfully completed processes, where similar deviations emerged. As a first step, this work introduces a novel similarity measure based on time sequence similarity that is able to compare running and completed workflow instances. We implemented and evaluated our approach in the ProCAKE framework. The proposed similarity measure achieves promising results considering runtime and similarity assessment.

The problem of constructing an attribute description of a business process with the automated formation of process models “as is” using logs of information systems in which the tracks of individual processes are not identified is considered. It is shown that to solve this problem, it is advisable to distinguish the distinctive properties of individual business processes represented by the attributes of log events. A method for constructing an attribute description of a business process is proposed. The method is based on the comparison of combinations of attributes for intervals of events of a fixed length and the subsequent selection of subsets of attributes with the same values. The method includes the steps of forming the intervals of events, constructing combinations of attributes for specified intervals, as well as calculating and subsequently averaging the weights of combinations of attributes on these intervals. The result of the method is a weight-ordered set of event attributes and their values, which takes into account the attribute and temporal aspects of the business process. The method creates conditions for a more efficient transition from functional to process management based on splitting the log into processes using the resulting attribute description and subsequent prototyping of business process models “as is” by means of process mining.

High-Performance Computing (HPC) applications have historically executed in static resource allocations, using programming models that ran independently from the resident system management stack (SMS). Achieving exascale performance that is both cost-effective and fits within site-level environmental constraints will, however, require that the application and SMS collaboratively orchestrate the flow of work to optimize resource utilization and compensate for on-the-fly faults. The Process Management Interface - Exascale (PMIx) community is committed to establishing scalable workflow orchestration by defining an abstract set of interfaces by which not only applications and tools can interact with the resident SMS, but also the various SMS components can interact with each other. This paper presents a high-level overview of the goals and current state of the PMIx standard, and lays out a roadmap for future directions.

Our research objective is to lower intralogistics costs by minimizing the number of shuffling operations in a steel plant company commercial warehouse. The process of dispatching products consists of retrieving set of steel bar (SSB) from a floor stored stack or a special stacking frame by an overhead crane. To retrieve a targeted merchandise all SSB above targeted must be reshuffled. Proper assignment of storage locations is a key logistics problem for efficient order picking. We are comparing two heuristics, that do not require information of dispatching sequence of any stored products. We simulated the problem at hand with both methods. Our objective is to count the number of reshuffles using each heuristic on randomly generated examples and decide which is better in the long run. Our problem has similarities with storage assignment of steel plates or steel coils for minimization of reshuffling operations. The problem is also comparable to storage assignment of containers in a container yard. In our case we are dealing with a special stacking configuration of products, that demands different approach. We want to demonstrate which heuristic should be used in companies that lack necessary storage information infrastructure.

Software defect prediction is very important in software engineering. It uses the defects found in historical software modules to predict defects in new software modules, and provides decision support for planning and process management in software project. Machine learning is one of the core research directions in the field of artificial intelligence and covers many disciplines. In the research of software defect prediction, various machine learning methods have been widely studied and applied, and have been verified to obtain good performance. In this paper, the basic framework of software defect prediction model based on machine learning is described, several important prediction models are studied and discussed. Finally, the main problems of software defect prediction are summarized, and corresponding solutions are proposed.

The management of the Reverse Engineering (RE) process for polymeric parts is a relatively complicated one. Composition on this market segment is tough due to the fact that approximately 85% of the machinery and equipment industry has migrated from the steel parts industry to the plastics industry over the last 30 years. This is why not only old mechanical parts and assemblies are subject to the RE process, but also new parts / products that competition is trying to reproduce. Therefore, the goal is to achieve the RE process of superior quality at the lowest cost and in the shortest possible time (if possible in a fully automated system). The RE process management involves selecting the hardware, selecting the RE software, and adopting the proper technique for obtaining surfaces (or solids, as the case may be). All these steps depend on the quality of the product to be achieved and the speed at which the results are obtained. The paper presents a concrete case of RE, solved by a certain process, but also other possible variants, as well as their impact on the quality of the final product (precision / price / effort).

Recently, the Internet of Things (IoT) concept has attracted a lot of attention due to its capability to translate our physical world into a digital cyber world with meaningful information. The IoT devices are smaller in size, sheer in number, contain less memory, use less energy, and have more computational capabilities. These scarce resources for IoT devices are powered by small operating systems (OSs) that are specially designed to support the IoT devices' diverse applications and operational requirements. These IoT OSs are responsible for managing the constrained resources of IoT devices efficiently and in a timely manner. In this paper, discussions on IoT devices and OS resource management are provided. In detail, the resource management mechanisms of the state-of-the-art IoT OSs, such as Contiki, TinyOS, and FreeRTOS, are investigated. The different dimensions of their resource management approaches (including process management, memory management, energy management, communication management, and file management) are studied, and their advantages and limitations are highlighted.

In this project we propose, describe, implement and test the Renewable Energy Monitoring System (REMS), a new concept on data acquisition and transmission systems (DATS) applied to real-time cloud monitoring of a decentralized photovoltaic (PV) plant. To achieve this latest design, we went through various systems projects alongside the evolution of technology. From this practical experience and in agreement with Brazil’s policy of diversifying the electricity generation matrix, our proposal focuses on a multi-user remote system using Raspberry Pi and Internet of Things (IoT) concept. REMS is capable of sensing and modifying monitoring process management via remote firmware update through the developed Analog/Digital Converter Embedded System (ADCES) as well as communicating with a personal developed cloud server profile via RPi Embedded Linux System (ELS), thus not requiring a dedicated PC. The measured variables are PV voltage and current, ambient and PV module temperature, solar irradiance, and relative humidity.

The Linux kernel provides its services to the application layer using so-called system calls. All system calls combined form the Application Programming Interface (API) of the kernel. Hence, system calls provide us with a window into the development process and design decisions that are made for the Linux kernel. Our paper [1] presents the result of an empirical study of the changes (8,770) that were made to the system calls during the last decade (i.e., from April 2005 to December 2014). The main contributions and most important findings of our study are: (1) An overview of the Linux system calls. As of December 2014, 396 system calls existed in the Linux kernel. They can be categorized into 10 groups (process management, signal processing, and so on). 76 of the system calls were added over the last decade (new system calls). A new system call is usually not activated for all architectures at the same time. 40 out of 76 (53%) new system calls and 102 of the 393 (26%) existing system calls were sibling calls. A sibling call is a system call that is similar in functionality, and often in name, to another system call. (2) A study of the evolution of the Linux system calls over the last decade in terms of the size and type of changes that were made to the system calls. With an average growth of 25 LOC per day, the Linux system calls are relatively stable. The commits that are made to system calls are slightly more scattered than kernel commits. There exists a small group of very active system call developers. 8,288 of the 8,770 studied commits (95%) were made to maintain, improve and fix bugs in system calls. 36% of the system call-related commits were bug fixes. 4,498 (50%) of the commits were made to only 25 (6%) of the 393 system calls. 35% of the system call-related commits were made to conduct code restructuring, and 36% of the system call-related commits were made to fix bugs. (3) A study of the type of bug fixes that were made to the system calls over the last decade. Developers make mistakes in the seemingly trivial activation process of a system call. The steps that are required to activate a system call, such as assigning the unique number and updating the system call table, are performed manually. 58% of the bug fix commits were made to fix semantic bugs. The proportion of bug fixes that fixed memory-related bugs remained constant throughout the last decade. (4) An analysis of the results and a discussion of their implications. Generalizability of results. We compared Linux system calls with FreeBSD system calls, to validate that our results are generalizable to other UNIX-based operating systems. Our findings for the FreeBSD operating system confirm that other UNIX-based operating systems use a system call mechanism that is similar to that of Linux. Therefore, we can safely assume that our findings are of value to other UNIX-based operating systems. Suggestion for automation. First, we suggest the automation of simple, reoccurring tasks in Linux, such as adding and removing system calls. Our study on FreeBSD shows that such tasks can successfully be automated. Second, we suggest that historical information about the evolution of a kernel API should be used to guide the testing process. Finally, we suggest that existing automated testing tools are extended to support testing system calls. Maintenance Effort. Compared to regular software systems, kernel APIs require an additional type of maintenance that involves adding and removing system calls. Also, approximately 11% of the maintenance effort of a kernel API is assigned to the infrastructure for providing the API. Overall, the results of our study can be beneficial to practitioners, researchers, and more specifically kernel developers, by providing insights related to the challenges and problems that come with long term maintenance of a kernel API, such as the long-lived Linux kernel API. We have published our classification of 8,870 system call-related changes [1] so that it can be used to conduct further studies. The full paper is accepted for publication in the Empirical Software Engineering journal, and can be found at: https://link.springer.com/article/10.1007/s10664-017-9551-z.

As organizações cada vez mais se interessam em conhecer o negócio que executam, com a finalidade de alcançar reduções de custo e tempo (MAGDALENO et al, 2007). Gonçalves (2000) salienta que a gestão de processos é capaz de promover maior flexibilidade na gestão das organizações possibilitando melhores resultados. A empresa objeto de pesquisa deste trabalho é uma distribuidora de alimentos localizada no Estado de Santa Catarina. A empresa sofre com um grande número de devoluções das vendas que são faturadas para pagamento à vista (quando da entrega do produto no ponto de venda). Um levantamento apontou uma média para três meses de R$ 6.436,00 de perdas. Pode-se definir que o problema da pesquisa é o alto custo das devoluções de mercadorias por falta de pagamento no ato da entrega para pedidos à vista pelo cliente no PDV (Ponto de Venda). Assim esse artigo teve por objetivo sugerir, através da análise de BPM, um novo processo de faturamento que reduzisse o nível de devoluções das vendas à vista para a distribuidora Beta. Realizado o estudo de caso através de pesquisa de campo, tabulação, modelagem e análise dos fluxos de processo, a pesquisa concluiu que há possibilidades de alteração dos fluxos de processo que garantam a entrega dos pedidos à vista sem risco de devoluções por falta de pagamento. O estudo também atesta a eficácia das ferramentas de mapeamento e modelagem (5W2H e BPMN) utilizadas para a resolução do problema de pesquisa.

Process mining is a new emerging discipline related to process management, formal process modelling, and data mining. One of the main tasks of process mining is model synthesis (discovery) based on event logs. A wide range of algorithms for process model discovery, analysis, and enhancement is developed. The real-life event logs often contain noise of different types. In this paper, we describe the main causes of noise in the event logs and study the effect of noise on the performance of process discovery algorithms. The experimental results of application of the main process discovery algorithms to artificial event logs with noise are provided. Specially generated event logs with noise of different types were processed using the four basic discovery techniques. Although modern algorithms can cope with some types of noise, in most cases, their use does not lead to obtaining a satisfactory result. Thus, there is a need for more sophisticated algorithms to deal with noise of different types.

In order to improve aerospace quality problem closeloop work level and standardize the quality problem closeloop management, the quality problem close-loop process control method is studied based on close-loop document. By analyzing the key objects involved in the process of quality problem close-loop, characteristics of close-loop process document, close-loop role, and close-loop activity are defined. Combined with the quality problem close-loop guideline standard, quality problem close-loop process control mechanism is proposed. Quality problem closeloop stage is divided into six parts, and the operation contents of different close-loop stages are analyzed. Based on the process management requirements, a three-layer control structure model of quality problems close-loop is studied and constructed. The functional requirements of online management about quality problems close-loop are analyzed, and the system architecture is designed. A prototype system of quality problem close-loop management is developed. System test results show that the process control and integrated management of quality problem close-loop is realized.

The Internet of Things (IoT) is a new technology that offers many applications to connect things to things and people to things through the Internet. Every object in the world, connected via the Internet, making decisions independently, can be identified. All communication networks and technologies are used to build the IoT concept such as mobile technologies, RFID, wireless sensor networks and integrated systems, alongside diverse algorithms, methodologies to achieve process management, data storage, and security issues. The technology underlying the Internet of Things is rapidly developing and transforms the way companies, inclusive medical professionals and consumers (patients) carry out their daily lives. In this paper are presented a series of experiments: ECG EEG monitor, heart rate, blood oxygenation, and blood pressure during the daytime, during sleep and effort, fever warning, fallout or warning for medication administration. This system is an example of a medical monitoring system for patients with chronic diseases and is based on the use of intelligent sensors and IoT.

In this study, the decision-making process management of forest tending in the forestry business is decentralized, and forest tending decision-making activities at different points in time are integrated by decision makers at different geographical locations. The decision-making process was analyzed and optimized from a system perspective. Based on the optimized decision-making process, a forest tending business group decision support system (FTGDSS) was established. We first reviewed and discussed the characteristics and development of the forest tending business and forestry decision support system. Business Process Modeling Notation was used to draw a current state flow chart of the forest tending business, to identify and discover important decision points in the process of tending decision-making. We also analyzed the content and attributes of each decision point, and described the system structure, functional framework, knowledge base structure, and reasoning algorithm of FTGDSS in detail. Finally, FTGDSS was evaluated from the two dimensions of the technology adoption model. FTGDSS integrates different levels of time-space decision-making activities, historical tending data, business plans, decision-makers' management tendencies into the decision-making process and automatically extracts decision-making data from the forest business process management enterprise resource planning system (Smartforest) that improves the ease of use of the decision support system (DSS). It also improves the quality of forest tending decisions, and enables the DSS to better support multi-target management strategies.

Social BPM allows for businesses to adapt and be flexible to ever changing demands. This flexibility is created by the participation and collaboration between users. These interactions are achieved through the successful implementation of Social BPM. This paper will propose critical success factors (CSF) which lead to a successful Social BPM implementation such that these benefits are realised. This is a progress paper which is part of a broader method which will validate against the literature, expert opinions and case studies in order to produce a definitive set of CSFs for Social BPM.

As regulatory reporting involves loosely defined processes, it is a considerable challenge for data scientists and academics to extract instances of such processes from event records and analyse their characteristics e.g. whether they satisfy certain process compliance requirements. This paper proposes a software framework based on a semantic data model that helps in deriving and analysing regulatory reporting processes from event repositories. The key idea is in using business-like templates for expressing commonly used constraints associated with the definition of regulatory reporting processes and mapping these templates with those provided by an existing process definition language. A case study investigates the efficacy of the proposed solution in the case of an “Off-market bid” regulatory process. The results demonstrate the capability of the architecture in deriving process instances from a repository of Australian Company Announcements provided by the Australian Securities Exchange.

In this paper, we examine usage of the Process Management Interface (PMI) during MPI_Init. Specifically, how PMI is used to exchange address information between peer processes in an MPI job. As node and core counts continue to increase in HPC systems, so does the amount of address data processes need to exchange. We show how by applying well-established locality-awareness techniques, we can significantly reduce the time spent in MPI_Init. We first present the use of shared memory to reduce the total amount of information retrieved from PMI. Next, by combining shared memory with a minimally connected set of processes, we further reduce the dependence on PMI, and employ the HPC fabric to transfer the bulk of address data. Our approach is low impact, relying on functionality already provided by MPI libraries and process managers, instead of new APIs and capabilities.

This paper takes the cloud accounting and financial sharing center as the research object. It firstly expounds and analyzes the current status of cloud accounting and financial sharing, its operation management, and data mining intelligence, and provides necessary theoretical support for subsequent optimization. Secondly, through field research, interviews and collection of relevant materials, the current status of cloud accounting and financial sharing center operation management is described and the problems are analyzed. During the description of the current status, problems that can be optimized based on the intelligent perspective of data mining are found. Including performance management, risk management and process management modules. It enriches the method and technical system of cloud accounting and financial sharing centre’s operation management optimization, contributes to the intelligence and automation of cloud accounting and financial sharing centre’s operation management, effectively reduces the cost of operation management of cloud accounting and financial sharing center, and improves the operational management quality.

The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.

Business Process customization is an active research area in the process management field, dealing with variations/commonalities among processes of a given process family and runtime adaptations of single process instances. Many theoretical approaches have been suggested in the last years; however, practical implementations are rare and limited in their functionality. In this article, a new approach is proposed for capturing customizable process models based on well-known graph transformation techniques and with focus on practical aspects like definition of variation points, linking and propagation of changes, visual highlighting of differences in process variants, and dynamically selecting a specific variant at runtime. The suggested concepts are discussed within case studies, comprising different graph transformation systems for generating process variants supporting (a) variability by restriction, (b) variability by restriction and by extension, and (c) runtime adaptations due to the executing actor. The overall approach is being implemented in the FireStart BPM suite.

Operating system represents one of the most complex man-made systems. In this paper, we analyze Linux Operating System (LOS) as a complex network via modeling functions as nodes and function calls as edges. It is found that for the LOS network and modularized components within it, the out-degree follows an exponential distribution and the in-degree follows a power-law distribution. For better understanding the underlying design principles of LOS, we explore the coupling correlations of components in LOS from aspects of topology and function. The result shows that the component for device drivers has a strong manifestation in topology while a weak manifestation in function. However, the component for process management shows the contrary phenomenon. Moreover, in an effort to investigate the impact of system failures on networks, we make a comparison between the networks traced from normal and failure status of LOS. This leads to a conclusion that the failure will change function calls which should be executed in normal status and introduce new function calls in the meanwhile.

With the rapid development of computer science and technology, data mining modelling techniques have emerged and rapidly developed as an alternative powerful meta-learning tool to accurately and fast analyze the massive volume of data generated by modern applications. The combination of data analysis technique and evaluation of public servant execution is urgently needed. Improve the execution of public servants at the grass-roots level is one of the important link to strengthen the construction of authority administrative efficiency of administrative goals is very important. Enhance the execution must first cultivate advanced concept, armed with advanced execution concept to the vast number of public servants at the grass-roots level. The assessment of public execution has a lot of traditional methods and models can be used but there is limitation. The limitation could be concluded as the following. Carelessness or poor sensitivity, At the grassroots level, the implementation of the main body of the general public servants at the grass-roots level and they can perform in place, one of the important factor is whether the leader on the work division of labor, organization, management and supervision effectively. In this paper, we conduct research on evaluation of public servant execution based on data mining technique and joint modeling analysis of multiple factors under big data environment. Firstly, we introduce some state-of-the-art clustering algorithm to serve as the basis of our model. Combined with deep neural network and optimization modelling, we propose our support vector machine based data clustering algorithm through multiple factor modelling. Subsequently, we discuss the principles on evaluation of public servant execution and process management. In the experimental part, we conduct experiment on both data clustering based data pre-processing step and the evaluation of elements’ weight for process management. The result indicates the most important factor for management and the feasibility and effectiveness of our proposed clustering method. Future potential research areas are also discussed in the final Section.

The growing popular demand for improved health care by Brazilian public hospitals has been increasing the need for more efficient hospital management and stronger oversight of the use of public funds. To achieve these goals, it is necessary to improve the recording of information, not only to assure accurate patient clinical histories and to generate technical knowledge, but also to facilitate the administrative and financial management of hospitals. The objective of this study is to present the mapping of the various processes involve in billing of outpatient services at Hospital Federal dos Servidores do Estado (Federal Civil Servants’ Hospital) in Rio de Janeiro, to identify problems and propose recommendations to improve these processes. The main evidence indicates that better process management will allow the introduction of a culture of quality in the support (administrative) areas and that better use of information technology resources should be established, to reduce duplicated effort, errors and failures in executing the processes, especially regarding the billing system.

Scientific applications on the CORAL systems demanded a fault-tolerant, scalable job launch infrastructure for complex workflows with multiple job steps within an allocation. The distinct design of IBM's Job Step Manager (JSM) infrastructure, working in concert with Load Sharing Facility (LSF) and Cluster System Management (CSM), achieves these goals. JSM demonstrated launching over three-quarters of a million processes in under a minute while providing efficient process management interface for exascale-based services to communication libraries, such as parallel active messaging interface and message passing interface, and tools over the management network. JSM relies on the parallel task support library to provide a fault-tolerant, scalable communication medium between the JSM daemons. Application workflows using job steps harness the unique resource set abstraction concept in JSM to manage CPUs, GPUs, and memory between groups of processes, possibly in discrete job steps, sharing a node. The resource set concept gives JSM the opportunity to better organize process placement to optimize, for example, CPU-to-GPU communication. Applications that need complete control over the shaping of the resource sets and the placement, binding, and ordering of processes within them can leverage JSM's co-designed Explicit Resource File mechanism. This article explores the design decisions, implementation considerations, and performance optimizations of IBM's JSM infrastructure to support scientific discovery on the CORAL systems.

Process discovery, one of the key steps in process management, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log fully comply with the process execution specification. However, in real event logs, noise and irrelevant infrequent behaviour are often present. In this paper, we propose a novel filtering method that the removal of infrequent behavior from event logs. The method has been evaluated in detail and it is shown that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.

Monitoring system is a crucial part in establishing sophisticated IT infrastructure. Monitoring of IT infrastructure becomes indispensable for knowing the status and performance of technology components. Complex infrastructure, such as virtualization and cloud computing systems, boost up the number of nodes to be monitored. Notifications accumulate, which makes it more difficult to track problems. With lots of unneccessary notifications, the challenge to provide availabmty and improve service quality becomes more complex. A monitoring system that can deliver accurate monitoring information is needed. This research proposed a method to manage IT infrastructure monitoring system using Lean method. Lean method is a popular method of process management and has been successfully used in several IT process improvement projects. The results of this research show that lean methods can be applied to increase the quality of monitoring system, through the process stages that have been described in the lean implementation principle. Quantitatively it is known that after the lean method is applied, the number of network elements, nodes and interfaces monitored increases by 39%, 105% and 1%, respectively. While the alerts and events generated decreased by 77% and 49%, respectively. Qualitatively based on the identification of value achievement, the system can deliver the value expected by the customer, in which case the customer is a user of monitoring system.

Recent decades have experienced a breakthrough in the study of Wireless Sensor Networks (WSNs) as a key technology for Internet of Things (IoT). Operating Systems (OSes) for resource-constraint WSN devices have been developed during the last years, facilitating application development for these networks. However, these OSes are static software modules and do not take into account environmental changes to optimize themselves. In this paper we present YetiOS, a novel OS that can change its properties dynamically through an adaptive core. This way, the OS adapts itself to the environment and can optimize the performance, power consumption, security or tasks latency on different scenarios while maintaining the same developing interface. YetiOS also includes a dynamic layered network stack architecture, allowing different sensor network protocols to be implemented. Besides, it features Linux-like device drivers, a FreeRTOS based process management module, an I/O interface module and an energy management module. Performance tests presented in this work demonstrate YetiOS suitability for low-power resource-constraint WSN nodes. In addition, a use case of the adaptive core is presented, and its performance test demonstrates the dynamic optimization achieved.

The article proposes an approach to modeling business processes of economic systems based on resource visualization on a business process diagram. The concept of management of economic systems processes based on machine learning has been developed. The method for predicting the route of execution of business processes using machine learning models based on naive Bayesian classifiers is being studied. This will significantly improve the efficiency of planning all the types of resources in the process management. In order to use trained models in real practice, the scheme for deploying machine learning models as web services is presented. The solutions offered by the author will let automatically send data to the trained model using REST API web service and get results in real time.

The implementation of virtual assistants, the automation of homes, smart cities, management of remote sensors, among other technologies related to the internet of things (IoT). They allow to improve the control, monitoring and management of your processes. The present research work employs a Message Oriented Middleware (MOM) for interconnecting devices allowing to monitor the domestic energy consumption of a household (KWH, fees to be paid by day, week, month) in real time, using a wizard virtual (chatbot) which facilitates the Administration and control of the connected electrical devices. Through a combination of ubiquitous distributed sensing units. Adding data, reasoning and awareness of the context.

Software development for large projects can be time consuming. Software frameworks offer basic tools to speed up and streamline the development process. The Robot Operating System (ROS) is a framework and middleware for software development in robotics applications. It is a framework because it provides a series of tools that enhance software development, but it is also a middleware since it includes operating system-like capabilities, such as process management, data transfer management and more. This chapter discusses the advantages and disadvantages of ROS as an integration medium for service robotics, and the describes the ROS part of the RADIO Home system and discusses the methodologies followed and how they can be applied to any large scale ROS project.

This paper presents the implementation of cybersecurity procedures in remote calibration for time and frequency dissemination to be used in position, navigation and timing services. The remote calibration method uses a P3 algorithm that combines P1 and P2 codes from satellite link and turn it free of ionosphere refraction to compare distant timescales. The proposed algorithm is also implemented in the calculation of the reference time scale UTC (Coordinated Universal Time) on the key comparison CCTF-KOOl.UTC. Results of satellite comparison between UTC(INXE) and UTC(OP) has been used to prove the suitability and reliability of the method. This work demonstrated that the communication exchange between client and provider needs to be secure in three dimensions: confidentiality, integrity and authenticity and the process management of the results should take care of these cybersecurity pillars.

Recent device hardware trends enable a new approach to the design of network server operating systems. In a traditional operating system, the kernel mediates access to device hardware by server applications to enforce process isolation as well as network and disk security. We have designed and implemented a new operating system, Arrakis, that splits the traditional role of the kernel in two. Applications have direct access to virtualized I/O devices, allowing most I/O operations to skip the kernel entirely, while the kernel is re-engineered to provide network and disk protection without kernel mediation of every operation. We describe the hardware and software changes needed to take advantage of this new abstraction, and we illustrate its power by showing improvements of 2 to 5 × in latency and 9 × throughput for a popular persistent NoSQL store relative to a well-tuned Linux implementation.

The main goal of the study is to know how multiple processes [1] of a computer system is managed. Early computers used to work on uniprocessing while currently all computers work on multiprocessing. Many programs could be executed at a time concurrently, while earlier computers used to allow execution of only one program at a time. Computers now can load and execute multiple programs from different processes. These tasks could be performed concurrently and managed properly. A system consists of number of jobs and processes related to operating system and user has to execute system code and user code. Now the query is this- How all these processes are managed along with management of Operating system? There are numerous such queries which rise in one’s mind. A CPU can be made more productive by any operating system if CPU switches properly between processes. It is possible if CPU is synchronized by synchronization of processes.