Knowledge-intensive Processes Research Articles

Understanding collective ownership in agile development: Turbo charging the process

Information systems development is a complex, knowledge-intensive process. Companies struggle with achieving IS development goals of delivering information systems within time, on budget, and with promised functionalities. This paper suggests that to achieve these goals, IS development teams need to cultivate a climate of collective ownership in the team. We collect qualitative data and use a multimethod approach combining multi-case study and grounded theory techniques to understand the collective ownership concept in the context of agile software development. We find that collective ownership in IS development varies from the standard conceptualization found in the literature. Collective ownership is manifested as more of a balanced, collaborative approach where the original developer is involved throughout the process and is empowered through inclusion in the process of change. We find that overall, individual ownership helps collective ownership but, in some scenarios, it may also impede ownership at the collective level. We also find that there is a reciprocal relationship between collective ownership and collaboration, and that perceived control and a shared understanding of the tasks also help develop ownership at the collective level.

Empowering Large Language Models in Hybrid Intelligence Systems through Data-Centric Process Models

Hybrid intelligence systems aim to leverage synergies in closely collaborating teams of humans and artificial intelligence (AI). To guide the realization of such teams, recent research proposed design patterns that capture role-based knowledge on human-AI collaborations. Building on these patterns requires hybrid intelligence systems to provide mechanisms that orchestrate human and AI contributions accordingly. So far, it is unclear if such mechanisms can be provided based on shared representations of the required knowledge. In this regard, we expect ontology-based data-centric process modeling to be a promising direction for hybrid intelligence systems that aim to support knowledge-intensive processes (KiPs). We illustrate this through exemplary process models (realized with our ontology- and data-driven business process model -- ODD-BP) that reflect the team design patterns for hybrid intelligence systems. We point out that relying on such process models enables multiple actors to fulfill roles jointly and allows them to address individual shortcomings. This is examined by discussing integrating large language models (LLMs) into the process models and describing how complementary AI actors could help to empower LLMs to fulfill their role in human-AI collaboration more comprehensively. Future work will extend the provided concepts while their evaluation initially focuses on the KiP of medical emergency call handling.

Wake effects and temperature plumes: Coping with non-knowledge in the expansion of wind and geothermal energy.

Energy transitions are knowledge-intensive processes where a multitude of actors are trying to cope with inevitable knowledge gaps, surprises, and uncertainties. In this context, we focus on two techno-physical phenomena that are gaining practical relevance with the expansion of wind and geothermal energy extraction, and are surrounded by significant unknowns: wake effects and temperature plumes. Both phenomena can potentially affect the efficiency of energy production, but the extent of their impact is not yet known. Based on 28 semi-structured interviews with experts in the fields of wind and geothermal energy, we explore how different central actors perceive and interpret non-knowledge of wake effects and temperature plumes, and how they deal with it. We show that there are strategies for either using non-knowledge as a basis for action or simply ignoring it and sweeping knowledge gaps under the rug. Both strategies serve the function of protecting agency and keeping things going.

Computational Analogies of Machine Learning concept in the Oil and Gas Industry and its Transformation in the Industry 4.0 Era

The fourth Industrial Revolution, commonly known as Industry 4.0 is enabling Oil and Gas industry to transform its business process and operations and adopt digital technologies to drive innovation, enhance production and operate more efficiently. The oil and Gas industry is enhancing Knowledge Management with advancement in digital technologies such as big data analytics, and artificial intelligence to transform vast information gathered historically stored in documents and systems as well as subject matter expert experience into digital knowledge to establish advisory systems to support decisions in the Oil and Gas operations. In organizations, knowledge has become an important success element and so is the case with the oil and gas industry, which is one of the most important sectors in energy. It must be dealt with and leveraged efficiently and effectively for competing in the world market by the creation of a competitive and sustainable environment in organizations. Oil exploration is a knowledge-intensive process in which effective operations and commercial success may be accomplished by identifying and evaluating possibilities early on and making knowledge-based choices. To be productive and competitive, the oil and gas industry must embrace knowledge management processes, where professionals may play a key role in managing information to easily address issues. However, the capacity to handle knowledge cannot be instilled in a single day; it is a culture that has been created over time via specialists and their expertise. The oil and gas sector has undergone significant changes throughout the years, affecting all of its sections, including exploration, production, and refining, and has got significant implications for marketing plans, production strategies, and R&D strategies. Only efficient knowledge management strategies with the aid of big data can encompass knowledge generation, information exchange, transformation, and dissemination of information in present times. The emergence of Generative Artificial Intelligence Chatbot such as ChatGPT is transforming the way knowledge has been managed traditionally. Applying the right strategies to enhance knowledge management is vital for the Oil industry to extract, develop, maintain, preserve and disseminate knowledge for effective operations. This paper introduces and discusses the developments in knowledge management, and explores the interrelationship between AI/ML, big data, and knowledge management in the oil and gas industry.

Prefab Light Clay-Timber Elements for Net Zero Whole-Life Carbon Buildings

"Net zero whole life carbon" is an ambitious climate target that refers to neutralizing and offsetting the entire LCA-based carbon footprint of a building, including both operational and embodied greenhouse gas emissions. Especially in the Northern climate, viable building envelope structures must, therefore, provide good thermal insulation and low embodied emissions. Carbon offset is typically based on excess on-site renewable energy or purchased carbon offsets disconnected from the building and the site. Viable strategies for carbon neutrality start by minimizing material-related and energy-related CO2e emissions. As a result, new kinds of building envelope structures have been recently introduced in the academic literature and experimental building projects. Traditional construction materials, such as timber and clay, have been sourced locally and processed manually, providing good results for the embodied emissions in life cycle assessment. Recent studies on clay-based construction materials have concluded that more research on clay as a construction material is needed, in particular considering its environmental performance. One specific concern in the Northern climate is that the weather conditions limit clay construction outdoors and prevent industrial-scale application of these solutions. The methods of prefabrication can address these issues. This study introduces the critical technical and environmental properties of a new prefabricated wall element based on a combination of light timber frame and light clay. In a hybrid light clay-timber structure, a mixture of clay and hemp shives is cast between the timber studs. On the one hand, the novelty of this wall structure is the prefabrication that enables industrial applications and upscaling without the limitations of weather conditions. On the other hand, the study assesses the climate impact of a light clay-timber wall element : cradle-to-gate emissions, thermal insulation, and the climate benefits outside the system boundary (carbon handprint) reported in the D-module of the LCA framework. The study also shows that natural materials require a different approach than synthetic materials from industrial processes. There may be variations in the properties of hemp and clay, especially when local sourcing is prioritized for better environmental performance. Moreover, the mixing and installation processes have a significant impact on the final properties and the performance. We show that constructing a light clay wall is a knowledge-intensive process that may result in very different technical properties.

Научно-технический потенциал предприятия – основа успешного производственного развития, обновления научной и технологической жизни и повышения эффективности производства

The article reveals the issues of scientific and technical potential of the enterprise as the basis for successful industrial development, renewal of scientific and technological life and improvement of production efficiency. Achievements of scientific and technological progress (STP) play a huge role in production processes, increasing efficiency and increasing the positive result relative to the costs associated with the introduction of STP, which, within the company’s indicators, allows it to reach a new level of production, compete in the market of innovations and know-how, and increase profitability. Knowledge-intensive processes and technologies traditionally contribute to the sustainable economic growth of the state and the improvement of the standard of living of the population.

Digital technologies and knowledge processes: new emerging strategies in international business. A systematic literature review

PurposeThis study aims to focus on a key unanswered question about how digitalization and the knowledge processes it enables affect firms’ strategies in the international arena.Design/methodology/approachThe authors conduct a systematic literature review of relevant theoretical and empirical studies covering over 20 years of research (from 2000 to 2023) and including 73 journal papers.FindingsThis review allows us to highlight a relationship between firms’ international strategies and the knowledge processes enabled by applying digital technologies. Specifically, the authors discuss the characteristics of patterns of knowledge flows and knowledge processes (their origin, the type of knowledge they carry on and their directionality) as determinants for the emergence of diverse international strategies embraced by single firms or by populations of firms within ecosystems, networks, global value chains or alliances.Originality/valueDespite digital technologies constituting important antecedents and critical factors for the internationalization process, and international businesses in general, and operating cross borders implies the enactment of highly knowledge-intensive processes, current literature still fails to provide a holistic picture of how firms strategically use what they know and seek out what they do not know in the international environment, using the affordances of digital technologies.

Transformer models for mining intents and predicting activities from emails in knowledge-intensive processes

Process mining is an interdisciplinary field that combines Artificial Intelligence and Business Process Management to extract insights from historical event data. Knowledge-intensive processes, which predominantly involve knowledge work, are often inadequately monitored by process-aware information systems. Consequently, the event data necessary for applying process mining techniques are frequently unavailable. Emails are widely used in knowledge-intensive processes for scheduling meetings, sharing documents, and reporting on the completion of outstanding tasks, which makes them suitable candidates for replacing event logs as the primary data source. In this work, we focus on the task of extracting the set of next recommended activities from incoming emails. Yet, we face two major challenges. Firstly, emails do not express process information explicitly but rather contain subtext that implies what the next best actions would be. Secondly, email data lacks domain-specific labels that would enable the use of machine learning. We overcome these limitations by utilizing an email taxonomy to represent user intents, thus bridging the gap between textual information and process semantics, as well as leveraging pre-trained transformer models applied in zero-shot and few-shot settings that require little to no labeled email data. An evaluation of our method on real-world unlabeled email communications demonstrates its effectiveness in recognizing intents and extracting activities.

Enhancing learning and exploratory search with concept semantics in online healthcare knowledge management systems: An interactive knowledge visualization approach

Healthcare is a knowledge intensive process, which requires large amount of information from various healthcare knowledge providers. However, this growing information makes challengeable for users in finding useful healthcare information to support their healthcare decision making. Knowledge management and knowledge management systems are drawing increasing attention in healthcare community. This paper proposes an online healthcare knowledge management framework and system prototype, featured with integrating knowledge learning and exploratory search for solving problems efficiently. Inspired by the knowledge to action framework in healthcare and the iterative process in software engineering, an iterative and incremental knowledge management model is designed to organize the activities during the knowledge learning and searching procedure. Supported by the knowledge-enhanced infrastructure of online healthcare information designed, domain knowledge learning and task knowledge learning environment can work collaboratively and interactively to improve the result of learning and exploratory search. Knowledge base also provides helpful information in an interactive way for solving problems. Given exploiting probabilistic information retrieval model and concept semantics described in Description Logics, a search intent discovery method and search intent guided learning and searching mechanism are designed within this environment for achieving the learning and search goal. The results of the experiments have proved the efficacy of the methods and process model used in the leaning-focused healthcare knowledge management system.

An ontology-based knowledge framework for selection of joining process in plastic assembly

Selection of assembly process is one of the important stage of product development which need to tackle at the earlier stage product realization. Selecting the most appropriate joining process needs various assembly joint information (liaison) which includes both the geometric and non-geometric information. To capture, represent, and reuse this information across domains is a highly knowledge-intensive process that necessitates the use of an active artificial intelligence (AI) tool. In this research, an AI tool called ontology-based knowledge framework is used to assist with identifying the best plastic assembly process to effectively support designers and process planners. The methodology for selecting a joining process and developing a knowledge-based framework is described, together with industrial application of the suggested approach is proposed. To express many key concepts like liaison, process, requirement for variant product etc., a joining process selection (JPS) ontology is created. To get the necessary knowledge for process selection that integrates several instances and knowledge rules, ontology mapping of joining method selection concepts is done using Semantic Web Rule Language (SWRL). The suggested method uses rule-based reasoning to automatically infer the best joining methods. Finally, industrial application of the suggested framework is proposed to assess the applicability of this approach.

Towards Hybrid Automation by Bootstrapping Conversational Interfaces for IT Operation Tasks

Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where bots perform some activities and humans serve other activities. In the context of knowledge-intensive processes such as IT operations, implementing hybrid automation is a natural choice where robots can perform certain mundane functions, with humans taking over the decision of when and which IT systems need to act. Recently, ChatOps, which refers to conversation-driven collaboration for IT operations, has rapidly accelerated efficiency by providing a cross-organization and cross-domain platform to resolve and manage issues as soon as possible. Hence, providing a natural language interface to bots is a logical progression to enable collaboration between humans and bots. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with bots executing automation scripts. The bots identify the intent of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request. We further detail our process of mining the conversations between humans and bots to monitor performance and identify the scope for improvement in service quality.

Development of Knowledge Graph Based on Risk Register to Support Risk Management of Construction Projects

Risk management (RM) in construction projects is a knowledge-intensive process that requires an efficient mechanism for capturing, storing, exploiting, and managing risk-related knowledge. Although some existing studies have proposed various knowledge-based risk management (KBRM) tools. However, not all tools suit all projects, as every project is unique. Besides, each tool is developed based on certain assumptions with methodological drawbacks. Hence, new tools are required in response to the continuous change in the project environment, complexity, and dynamism by adopting new technologies to improve the RM processes. This study proposes a knowledge graph technique to improve risk-related knowledge storage, sharing, and reuse in managing construction project risks. This study constructed a risk knowledge graph (RisKG) based on a project risk register. The proposed RisKG was then used to develop a construction risk dashboard (ConRisk Dashboard) that can be used for quick risk information retrieval to support the RM of construction projects. The effectiveness of the proposed ConRisk Dashboard was verified through its use for the search and retrieval of risk assessment details of a particular risk source associated with the case project used in RisKG. The results indicate that the ConRisk Dashboard can improve the RM processes by saving time spent on project documentation reviews and cost expended on expert workshops at the beginning of every new project. Conclusively, the contribution of this study to the body of knowledge is that it provides a framework for developing knowledge-based risk management systems (KBRMS) and intelligent tools that can facilitate integrated risk management and digital collaboration among project stakeholders to support informed decision-making in construction projects. In addition, this work paves the way for research towards developing and adopting digital tools that can facilitate the digital transformation of RM and CM practices to improve project performance and productivity in the construction industry.

Extracting Reusable Fragments From Data-Centric Process Variants

Data-centric process management supports knowledge workers in performing knowledge-intensive processes in a flexible way. An essential ingredient of data-centric process management are process templates that are manually modified for a specific case to suit the context of that case. Modifying templates results in many different yet related process variants. However, manually modifying a template is time consuming and may lead to errors. This paper defines an approach to extract reusable fragments from data-centric process variants. The set of extracted fragments is minimal. By composing the fragments not only the input variants but many more process variants can be derived. We have implemented the approach in a prototype and evaluated it on several business processes. Using the fragment extraction approach, complex data-centric process variants can be designed more efficiently and their quality can improve, since well-known modifications are applied.

A cognitive BPM theory for knowledge-intensive processes

PurposeA literature review was conducted in order to establish a detailed definition of a knowledge-intensive process (KiP). Moreover, relevant theories from humanities – especially the fields of linguistics and philosophy – were thoroughly researched and adapted for establishing descriptive and analytical foundations for the phenomena involved. Finally, a cognitive business process management (BPM) theory was proposed in order to assess how sufficient are its respective explanatory and predictive powers. This study aims to discuss the aforementioned objective.Design/methodology/approachThis study proposes a novel theory for KiPs that describe the process flow based on the participants' interactions and their beliefs, desires and intentions as the main drivers of the process enactment and execution.FindingsThe proposal puts forth a comprehensive definition of a KiP, depicting knowledge intensity, participant interaction and decision-making. The inner dynamics of each of these two elements (as well as other associated elements) are described as an information systems (IS) theory that enables the study of KiPs in detail, going beyond the typical techniques of the BPM field and common obstacles.Originality/valueA theory proposal for KiP that applies concepts from speech act theory and intentional states as the main drivers for understanding the process dynamics are, to the best of the authors' knowledge, not present at the literature. Being an original proposal, the real-world scenario discussed brings up the explanatory and predictive powers of the theory as well as its innovative value for research in the field.

Gathering Expert Knowledge in Process Industry

Knowledge intensive processes are often driven and constrained by mental models of experts. Especially in industrial processes, the knowledge is not explicit but tacit in many cases, which makes it difficult to provide verbal descriptions. Modeling and prediction tasks are often not compatible or differ from the view of experts if no additional knowledge regarding the process itself or important parts of the process are identified and integrated. In industrial environments, the required knowledge is present in operators, which have to deal with the task or process on a frequent basis. Including operators during the development process may increase the quality and performance as well as trust in the resulting application. Data scientists face great difficulties in eliciting and representing the knowledge of the experts in these systems for solving the given tasks. In this paper, we will provide a review of strategies and approaches for knowledge elicitation in industrial environments. The approaches focus on applicability in industrial use-cases, whereat the operators are the main source for knowledge. The differences of the approaches are highlighted and use-cases for their application are presented. Furthermore, we will provide a discussion on using these approaches at different stages during a project as usually multiple knowledge elicitation phases are required to reach a certain goal.

Exploration and practice of teaching methods of basic specialized courses for postgraduate majors based on the characteristics of “Three Teaching Processes”

The ability of engineering graduate students to use their knowledge and independently engage in scientific research is one of the important indicators to measure the quality of postgraduate training. This paper takes the comprehensive improvement of graduate students’ ability to engage in scientific research independently as the starting point, and improves the teaching quality of basic courses of graduate majors in an all-round way by constructing teaching methods of basic knowledge intensive teaching, knowledge application and case process teaching, so that students can deeply grasp the course knowledge system and flexibly apply the knowledge learned in the course learning process. Finally, it lays a good foundation for students to engage in scientific research independently.

Process bottlenecks identification and its root cause analysis using fusion-based clustering and knowledge graph

Drawing the strengths of data science and machine learning, process mining has recently emerged as an effective research approach for process management and its decision support. Bottleneck identification and analysis is a key problem in process mining which is considered a critical component for process improvement. While previous studies focusing on bottlenecks have been reported, visible gaps remain. Most of these studies considered bottleneck identification from local perspectives by quantitative metrics, such as machine operation and resource requirement, which can not be applied to knowledge-intensive processes. Moreover, the root cause of such bottlenecks has not been given enough attention, which limits the impact of process optimisation. This paper proposes an approach that utilises fusion-based clustering and hyperbolic neural network-based knowledge graph embedding for bottleneck identification and root cause analysis. Firstly, a fusion-based clustering is proposed to identify bottlenecks automatically from a global perspective, where the execution frequency of each stage at different periods is calculated to reveal the abnormal stage. Secondly, a process knowledge graph representing tasks, organisations, workforce and relation features as hierarchical and logical patterns is established. Finally, a hyperbolic cluster-based community detection mechanism is researched, based on the process knowledge graph embedding trained by a hyperbolic neural network, to analyse the root cause from a process perspective. Experimental studies using real-world data collected from a multidisciplinary design project revealed the merits of the proposed approach. The execution of the proposed approach is not limited to event logs; it can automatically identify bottlenecks without local quantitative metrics and analyse the causes from a process perspective.

Model-based decision support for knowledge-intensive processes

Process-aware information systems guide participants through the execution of processes. However, existing systems have limited support for knowledge-intensive processes, which are multi-variant and shaped by informed decisions of knowledge workers. Yet, making such decisions causes high cognitive load, as the effect of the decision on the future process execution must be considered. This may cause errors and/or slow down the process execution. We present an approach based on fragment-based Case Management. It supports the iterative decision making by (i) enabling knowledge workers to define goals and (ii) by giving recommendations on which decision outcomes align with the goals and which do not. For that, we use information from the process model and the running process instance. We show the technical feasibility with a proof-of-concept implementation and the value for knowledge workers in a preliminary user study.

Context-aware modeling for knowledge-intensive medicinal product development processes

Due to their unique characteristics, knowledge-intensive processes (KiPs) are difficult to capture with conventional modeling and management approaches. One such KiP is the advanced therapy medicinal product (ATMP) development process. ATMPs are highly innovative medicinal products that are based on biomedical technology. ATMP development processes need to comply with complex regulatory frameworks. Currently, biomedical scientists that develop ATMPs manage the regulatory aspects of the ATMP development processes in an ad hoc fashion, resulting in inefficiencies such as reworks or even withdrawal of ATMPs from the market. This paper presents an explorative case study in which we use Enterprise Modeling and Context-aware Business Processes to support ATMP scientists in managing the regulatory aspects of ATMP development processes more efficiently and effectively. In our explorative case study, we use enterprise models to describe the important concepts and views in ATMP development processes. By introducing context-awareness to the models, we support ATMP scientists in performing relevant tasks to address the regulatory requirements efficiently and effectively under different contexts. We introduce the novel concept of execution-dependent dynamic context to properly define the context in ATMP development processes. Additionally, this paper takes a broader perspective on the case study by discussing the relevance of the solutions derived for the case study for other KiPs. Thereby this paper aims to present an exemplary approach for context-aware modeling of KiPs. The practical contribution of this paper are the models realized in a real-life ATMP development project. The scientific contribution of this paper is providing an exemplary approach for supporting knowledge workers who perform flexible, KiPs under dynamic contexts and introducing the notion of execution-dependent dynamic context.

RESEARCH AND DEVELOPMENT OF A SOFTWARE TOOL FOR PARAMETRIC MODELING OF ROBOTIZED WORKPLACES

Designing a robotic workplace is a time and knowledge intensive process. It requires systems engineers who specialise in the design of this type of workplace and have the appropriate know-how in this area. Due to the time-consuming nature and the small number of experts, a small number of solution options are often created, from which the most suitable one is subsequently developed. The aim of the development was to create a tool that would simplify the design of robotic workplaces. The result is a software tool integrated into the SolidWorks CAD system. The tool uses a database of models. The connection between the developed software tool and the database is realized through the API of the SolidWorks CAD system. Thanks to this tool, it is possible to simplify and speed up the work of workplace design. More variant solutions can be developed and overall better results can be achieved.