Knowledge-intensive Processes Research Articles

Towards Integrating the Gene Ontology and the Hierarchical Bayesian Network Classification Model: An Empirical Case Study

Data Mining (DM) is knowledge-intensive process that can be significantly enhanced by integrating the domain knowledge. Recent research claimed that ontology can play various roles in the DM process. Additionally, ontology can facilitate different steps in the Bayesian Network (BN) construction task. To this end, this paper investigates the advantages of consolidating the Gene Ontology (GO) and the Hierarchical Bayesian Network (HBN) classifier in a flexible framework which preserves the advantages of both ontology and Bayesian theory. The proposed Semantically Aware Hierarchical Bayesian Network (SAHBN) classification model introduces a flexible framework that systematically consolidates domain knowledge in the form of ontology and the DM process. Furthermore, it establishes a solid foundation to explore the possibility of integrating more comprehensive ontological knowledge in the DM process. SAHBN is tested using three datasets in the biomedical domain to predict the effect of the DNA repair gene on the human ageing process. DNA repair genes are classified as either ageing-related or non-ageing related based on their GO biological process terms. Overall, SAHBN classifier shows a very competitive performance compared with the existing Bayesian-based classification algorithms. SAHBN has outperformed existing algorithms in more than 50% of the implemented experiments. Six performance criteria were used to evaluate the performance of the proposed SAHBN model.

THE ROLE OF LEADERSHIP IN TACIT KNOWLEDGE TRANSFER IN THE NIGERIAN OIL AND GAS INDUSTRY.

The oil and gas industries are knowledge driven industry. The technology deployed in deep water exploration and production involve knowledge-intensive process by highly technical personnel. The problem was that the leadership of the oil and gas industries have not necessitated early recovery of tacit knowledge transfer from experts to employees managing the plant operations. The purpose of this qualitative multiple case study was to gain an understanding of how oil and gas industry leaders in Nigeria facilitate the transfer of tacit knowledge from experts to employees managing the plant after exploration activities. The conceptual framework was the socialization, externalization, combination, and internalization model developed by Nonaka and Takeuchi and Burns’ transformational leadership theory. A qualitative multiple case study design was used by adopting multiple sources of information including semi-structured interviews, field notes, and review of organizational documents. The unit of analysis was leaders in an oil and gas services organization. The data analysis processes involved coding of the data, categorizing the coded data, and subsequently generating themes in line with the research question using NVivo Version 12 software. Findings indicated that leaders facilitated the transfer of tacit knowledge through the creation of a safe working environment and demonstration of care for the employees. The opportunity to facilitate the transfer of tacit knowledge from expert to employees managing operations after exploration enhance the organization’s stability and promotes healthy communities. Keywords– Knowledge-Transfer, Leadership Style, Oil and Gas Industry

METHODS AND TOOLS TO SUPPORT ON-SITE KNOWLEDGE MANAGEMENT IN INFRASTRUCTURE PROJECTS

A method describes the way of doing something with a definite plan, while a tool can support the method by using information technologies (IT). Knowledge management provides thousands of methods and tools to assist the process of creating, sharing, using, and managing knowledge. This approach is well-known in Austria's industrial sector and only applied in major construction companies ‒ especially not during the execution phase. Even though the building industry is characterized by routine processes, renovation projects cause unpredictable conditions leading to knowledge-intensive processes, which indicates the potential of knowledge management. A case study was conducted to identify the requirements for knowledge management tools or methods suitable for infrastructure projects focusing on highway construction sites. It points out that the needs of the interviewees working on-site are not very complex but very fundamental. The investigation shows that the most important needs include connecting, transparency, and team building. After this first step, research of methods and tools commonly used for knowledge management was completed. Those fulfilling at least one of the identified requirements were considered, evaluated, and examined for their applicability. This paper presents a selection of methods and tools to meet the identified needs on highway construction sites in Austria.

Developing a Case-based Emergency Response System with Adaptive Case Management

Abstract The emergency responses required during large-scale crises or disasters are extremely knowledge-intensive processes and are usually characterized by a high degree of unpredictability and unrepeatability. An emergency response is mission- and time-critical, unstructured, very dynamic, and it is very difficult to predefine or even to anticipate all possible response scenarios. Therefore, designing and implementing a software system to support such a response system is highly complicated and challenging. This research aims to investigate and discuss how Adaptive Case Management (ACM) can be leveraged in the design and implementation of a case-based emergency response system. In particular, this research considers the best practices of the National Incident Management System (NIMS), which is an essential part of the National Response Framework (NRF) developed in the United States. As a proof-of-concept, a prototype demonstration has been carried out on a leading commercial ACM platform. In addition, a walkthrough scenario is discussed to elaborate how ACM can support emergency response activities in real settings using the Incident Command System (ICS) organizational structure. The key benefit of this research is to guide the development and implementation of cased-based emergency response systems with a flexible and agile approach.

Feature-oriented engineering of declarative artifact-centric process models

Declarative artifact-centric process models are suitable for specifying knowledge-intensive processes. Currently, such models need to be designed from scratch, even though existing model fragments could be reused to gain efficiency in designing and maintaining declarative artifact-centric process models. To address this problem, this paper proposes an approach for composing model fragments, abstracted into features, into fully specified declarative artifact-centric process models. We use Guard-Stage-Milestone (GSM) schemas as modeling language and let each feature denote a GSM schema fragment. The approach supports feature composition at different levels of granularity. Permutability of features is analyzed. Syntactic conditions that ensure permutability are defined and refactoring of non-permutable into permutable features is discussed. The approach has been evaluated by implementing it in a tool based on the Case Management Model Notation (CMMN) and applying it to three real-world processes. Using the approach, declarative artifact-centric process models can be composed from existing model fragments in an efficient, robust and correct way.

A knowledge-intensive adaptive business process management framework

Business process management has been the driving force of optimization and operational efficiency for companies until now, but the digitalization era we have been experiencing requires businesses to be agile and responsive as well. In order to be a part of this digital transformation, delivering new levels of automation-fueled agility through digitalization of BPM itself is required. However, the automation of BPM cannot be achieved by solely focusing on process space and classical planning techniques. It requires a holistic approach that also captures the social aspects of the business environment, such as corporate strategies, organization policies, negotiations, and cooperation. For this purpose, we combine BPM, knowledge-intensive systems and intelligent agent technologies, and yield one consolidated intelligent business process management framework, namely agileBPM, that governs the entire BPM life-cycle. Accordingly, agileBPM proposes a modeling methodology to semantically capture the business interests, enterprise environment and process space in accordance with the agent-oriented software engineering paradigm. The proposed agent-based process execution environment provides cognitive capabilities (such as goal-driven planning, norm compliance, knowledge-driven actions, and dynamic cooperation) on top of the developed business models to support knowledge workers’ multi-criteria decision making tasks. The context awareness and exception handling capabilities of the proposed approach have been presented with experimental studies. Through comparative evaluations, it is shown that agileBPM is the most comprehensive knowledge-intensive process management solution.

Comparing the effectiveness of conventional and Kano model questionnaire for gathering requirement of online bus reservation system

Software development is the activity of creating a software system that when used, solves some unsolved problem. It consists many knowledge intensive processes, among which requirement elicitation process is perhaps the most critical for the success of the software system. The success or failure of the system is related to the quality of the elicitation process and to the method used for gathering user needs. This study examines combined approaches to identify and classify the requirements and investigates the importance of customer satisfaction in requirement elicitation for online bus reservation system. The context of this work is University Malaysia Pahang (UMP). The target audiences are public who uses bus services. A total 63 out of 70 questionnaire are answered by the people. To do this, the design of two types of questionnaires: conventional and Kano questionnaire is done and implemented to identify the correct requirements for the selected system and then to compare the effectiveness of these two approaches. This study found that, both conventional method and Kano model are suitable to find and classify the requirements, but for deeper understanding of requirements which have the greatest influence on the customer's satisfaction, Kano model is more effective, because Kano's method provides valuable help when trade-off is needed for designing the product development.

Declarative and Hybrid Process Discovery: Recent Advances and Open Challenges

Knowledge-intensive processes, such as those encountered in health care, finance and government, tend to allow a large degree of flexibility: there are many possible solutions towards a goal, and it is left to the expertise of knowledge workers to find the one most suitable for the particular case at hand. As a result, such processes usually exhibit more varied behaviour than traditional production processes. This poses a challenge for process discovery algorithms that return imperative, flow-based, models. The models tend to become highly complex when representing many alternative paths, and therefore, the miners need to either sacrifice on simplicity, fitness, or precision. It has been proposed that one should discover the constraints of the process instead, based on the assumption that such a constraint-based, declarative process model can describe highly varied behaviour more concisely. More recently, it has been observed that many processes do not neatly fall in one category or the other; instead, they contain both flexible and rigid parts. In such cases, it may be helpful to identify these parts and mine constraints for some and flow for others, resulting in a hybrid model. In this paper, we provide an overview of recent advances in both declarative and hybrid process discovery, discuss a number of open challenges that still remain, and propose directions for future research.

«ЦИФРОВИЗАЦИЯ»: ПРЕДЕЛЫ РОСТА В XXI ВЕКЕ

The paper shows that the main limit to the growth of "digitalization" in the XXI century may be not so much the limits of technology development, but rather the difficulties with staffing such a knowledge-intensive process as digital development. This problem is mainly associated with a decrease in the rate of growth of the World's population, which is determined by other social, economic, and factors. The personnel problem of the development of the digital economy is based on the need to maintain the number of researchers and the intensity of solving the problems of economic and social development through the creation and implementation of digital technologies. Solving this problem in the coming decades may face a chronic shortage of specialists to implement the most ambitious plans.

Method and Notation Application for Case Model Creation in the Social Sector

Adaptive Case Management (ACM) is an approach for the management of knowledge-intensive processes. “Case Management Model and Notation” (CMMN) is an industry standard that can be used as a notation for process models in the context of ACM. However, CMMN only specifies a notation. Methods for process elicitation and management have not been well addressed by scientific investigations. This work assesses the method support and the feasibility of ACM on the example of a social care company. This company, on the one hand, represents a typical environment for ACM application and, on the other hand, contains some characteristics that are common for the social care industry. Thus, future development directions, a combined process and case approach for the social care company and an evaluation of existing and proposed methodology are provided in a qualitative way.

Exploring inter-firm knowledge through contractual governance: A case study of production contracts for faba-bean procurement in France

Abstract A major sustainability challenge in European agriculture is to develop legume production. But organizing new legume supply-chains is a knowledge-intensive process, due to low past investment. In particular, technical knowledge development on crop production requires strong coordination between stakeholders. Even if contractual coordination in the agro-industrial sector is increasing, its role in inter-firm knowledge exchange is poorly understood. Drawing on a qualitative case study, we explore how production contracts strengthened knowledge exchange between a processor and its grain-legume supplier’s in France. Based on Transaction-cost economics and the Relational-view of strategic management, this paper draws an original setting for practitioners and research to analyze contractualization from a learning perspective. The results show that collective contractual governance plays a major role for knowledge development through face-to-face interactions and specific intangible asset investments. This case study opens research agenda to analyze how contractualization brings the stakeholders into a collective progress curve.

How do interorganizational networks and firm group structures matter for innovation in clusters: Different networks, different results

ABSTRACTInnovation requires knowledge-intensive processes. In firms, group structures may lead to better innovation practices because knowledge pooled by the members through their networks enhances creativity and innovation processes. Knowledge sourced from networks is shaped by the specificity of the network. Using data on Spanish clusters, this article confirms that both teams and external knowledge contribute to innovation, and that combining external knowledge with team practices is effective. However, the benefits of this combination are contingent on the idiosyncrasies of the network in terms of density and geography. Cluster characteristics also determine the role of networks and teams.

Toward a methodology for case modeling

Case management is increasingly used to capture and enact flexible, knowledge-intensive processes in organizations. None of the existing case management approaches provides a methodology for case model elicitation and modeling. In this contribution, three modeling methods for fragment-based case management are presented: one which focuses on the control-flow view, the process-first method, one which has a data-centric view, the object lifecycle-first method, and one which focuses on the goals of a case, the goals-first method. Following the design science process, each of the three methods was evaluated in two case modeling workshops with two different stakeholder groups (PhD students and secretaries), resulting in a total of six workshops. All participants were novices in case management and most of them as well in process modeling. The results indicate that the process-first method can be quickly learned by novices and it might be useful for scenarios where the focus is on the main process with some degree of flexibility. The object lifecycle-first method yields more flexible and consistent case models, but requires a higher initial modeling effort, as the lifecycle of the main case object has to be designed first. The goals-first method leads to a detailed and consistent case model and additionally provides, by means of the defined goals, a checklist what needs to be done for a case. This method requires in addition to the process modeling notation another model type, the goal hierarchy, and therefore is less suited for novice modelers, as found by the workshop results.

The adaptation of business process management maturity models to the context of the knowledge economy

PurposeThe purpose of this paper is to investigate whether the current business process management (BPM) maturity models meet the requirements of evaluating organizations in the knowledge economy (KE) which manage processes in a dynamic way.Design/methodology/approachIn this study, a content analysis of the OMG (2008) Business Process Maturity Model and ten research papers on the practical application of business process management maturity models was conducted. The nature of the study is descriptive and based solely on information from secondary data sources.FindingsThe research results reveal that the current BPM maturity models do not correspond with the knowledge-based organizations and take into account knowledge-intensive (usually dynamic) processes in a very limited way. That is why the adaptation of the current BPM maturity models to the KE is needed.Originality/valueThis paper contributes to the BPM theory and practice in two ways. First, it provides an enhanced insight into the requirements of the KE toward BPM and BPM maturity models by distinguishing between static and dynamic processes. Second, it formulates the recommendations on possible ways of adapting the current BPM maturity models to the requirements of the KE.

THE POTENTIAL OF KNOWLEDGE MANAGEMENT ON CONSTRUCTION SITES

Knowledge management is the process of creating, sharing, using, and managing knowledge, one of the most valuable organizational resources. This approach is well-known in Austria's industrial sector, but applied only in major construction companies. It is mainly used to share knowledge between different departments, but it is not commonly found on construction sites. During the construction phase a variety of separate firms build a temporary multidisciplinary organization, to produce investment goods. To show the potential of knowledge management on building sites in Austria, 78 interviews were conducted. Construction sites of different types (new construction and refurbishment of buildings) were taken into account in order to guarantee a representative outcome. The highest cost-benefit ratio for knowledge management can be seen in knowledge intensive processes. The execution phase is characterized by craftwork which often includes many routine steps. But the survey shows that almost a quarter of the daily business is about knowledge intensive processes while the amount doesn't correlate with the working experience. Furthermore, on construction sites with many trades the lack of information and knowledge transfer is the cause of nearly a quarter of the problems faced. The findings indicate the need for knowledge management on construction sites and the potential grows with the number of trades. The teambuilding process can be seen as the most important step for an efficient knowledge management during the execution phase.

Ontology for safety risk identification in metro construction

Safety risk identification of metro construction is a knowledge-intensive process involving various stakeholders and communities. Currently, safety risk information related to decision making in metro construction is ill-structurally stored in various disordered formats, which hinders knowledge sharing and reuse. This study develops a domain ontology (SRI-Onto) to formalize safety risk knowledge in metro construction to support safety risk identification. An ontology development method with five steps is adopted. The SRI-Onto organizes safety risk knowledge into seven unified classes (i.e. project, construction activity, risk factor, risk, risk grade, risk consequence, and risk prevention measure). Defined classes, together with corresponding properties and relations, are coded using the Protégé platform. Finally, the SRI-Onto is evaluated theoretically and practically, using criteria-based and application-based evaluations respectively. Results indicate that the SRI-Onto possesses the necessary and essential criteria to serve the purpose of knowledge sharing and reuse. Further, the SRI-Onto is predicted to be generally applicable to safety risk identification of metro construction.

Realizing Social-Media-Based Analytics for Smart Agriculture

Continuous fluctuation of the daily prices of vegetables and pulses is destabilizing to a country’s economy and hinders the nation’s monetary planning. One factor that affects the fluctuation of the prices is changes in the production of the vegetables and pulses. Early detection and treatment of crop and plant diseases will substantially improve production and constitutes an attempt to introduce smart technology into the system. There is an immediate need for an intelligent solution that advises farmers on how to address crop and plant diseases at the point of demand. In this paper, we have explored social media analytics as a knowledge-intensive process and proposed a new framework for identifying the relevant details of plants from both text and images. This approach will generate knowledge about plant diseases and other areas of development that are related to precision agriculture. The community of farmers within social media is expected to provide immediate suggestions to questions that are posted by members regarding plant diseases and other problems. The newly designed innovative framework identifies the relevant features, finds similarities among the farmers’ heuristics, and ranks their suggestions related to merit and relevance. Our system employs deep learning, natural language processing and other predictive models to compare the combination of text and image extracts and suggest a suitable solution to the farmer’s query. The efficiency of the employed models is evaluated to avoid false positives, and the models are implemented on social media to address plant diseases and other relevant details as a part of evolving smart agriculture.

Measuring Performance in Knowledge-intensive Processes

Knowledge-intensive Processes (KIPs) are processes whose execution is heavily dependent on knowledge workers performing various interconnected knowledge-intensive decision-making tasks. Among other characteristics, KIPs are usually non-repeatable, collaboration-oriented, unpredictable, and, in many cases, driven by implicit knowledge, derived from the capabilities and previous experiences of participants. Despite the growing body of research focused on understanding KIPs and on proposing systems to support these KIPs, the research question on how to define performance measures thereon remains open. In this article, we address this issue with a proposal to enable the performance management of KIPs. Our approach comprises an ontology that allows us to define process performance indicators (PPIs) in the context of KIPs, and a methodology that builds on the ontology and the concepts of lead and lag indicators to provide process participants with actionable guidelines that help them conduct the KIP in a way that fulfills a set of performance goals. Both the ontology and the methodology have been applied to a case study of a real organization in Brazil to manage the performance of an Incident Troubleshooting Process within an ICT (Information and Communications Technology) Outsourcing Company.

Enabling flexible task compositions, orders and granularities for knowledge-intensive business processes

ABSTRACTDuring the last decades, many approaches and tools were developed that enable a flexible enactment of repetitive business processes. By contrast, knowledge-intensive processes (KIPs) have not been effectively supported yet. This paper proposes a data-centric and goal-driven approach that enables flexible KIP enactment with dynamically evolving task structure and varying task granularity. The approach utilises artificial intelligence planning techniques in order to enable recommendations regarding the next tasks to be enacted. Furthermore, recommendations may be dynamically adapted when mismatches between the digital process and the real-world KIP emerge. Overall, the approach provides flexibility and guidance during KIP enactment.

KiPN: a visual notation for knowledge-intensive processes

Modelling knowledge-intensive processes (KiP) is not an easy task, due to their essential characteristics of being unstructured, based on social interactions, experience and intentions of the actors involved. The knowledge-intensive process ontology (KiPO) provides a structural conceptualisation for KiPs, precisely specifying all concepts and relations that are necessary to make a KiP explicit. However, KiPO does not provide a graphical notation, which is crucial for organisation managers, as well as process managers, modellers and executors. Some visual notations exist for the visual representation of KiP, but they miss at least one of these relevant features in the KiP representation. In this paper, we propose the knowledge-intensive process notation (KiPN), a notation for modelling KiP. KiPN is based on the principles of the theory of visual notations to develop the cognitive efficacy of a visual representation. The evaluation of KiPN indicates the cognitive effectiveness and perceived usefulness of KiPN.