Knowledge-intensive Tasks Research Articles

Traceability and management of dispersed product knowledge during design and manufacturing

Product development processes comprise highly creative and knowledge-intensive tasks that involve extensive information exchange and communication among geographically distributed teams. Due to the geographical and institutional separation between the different systems involved in the product lifecycle, product knowledge sharing is becoming a key issue in the information systems of extended enterprises. This paper addresses the issue and challenges of product knowledge traceability during the product development. The aim of this research effort is to enhance the sharing and use of product knowledge acquired during the development process using traceability information. A standardized approach is proposed to trace and share product knowledge and key constructs to support traceability during the product development process are identified and formalized. This research effort is based on the premise that an important step towards achieving product knowledge sharing is providing traceability across various product knowledge elements that are used in product development phases, i.e. design and manufacturing. Two disjointed but complementary case studies illustrating the benefit of traceability are presented. The potential role of traceability is described, first to support the decision making process during engineering change management (ECM), and second to support product-oriented modelling for knowledge sharing and exchanging to meet the quality requirements. The proposed approach has been implemented using the MEGA Suite tool and applied to each of the case studies and could be integrated to PLM systems currently in use.

OpenFlyData: An exemplar data web integrating gene expression data on the fruit fly Drosophila melanogaster

Motivation: Integrating heterogeneous data across distributed sources is a major requirement for in silico bioinformatics supporting translational research. For example, genome-scale data on patterns of gene expression in the fruit fly Drosophila melanogaster are widely used in functional genomic studies in many organisms to inform candidate gene selection and validate experimental results. However, current data integration solutions tend to be heavy weight, and require significant initial and ongoing investment of effort. Development of a common Web-based data integration infrastructure (a.k.a. data web), using Semantic Web standards, promises to alleviate these difficulties, but little is known about the feasibility, costs, risks or practical means of migrating to such an infrastructure. Results: We describe the development of OpenFlyData, a proof-of-concept system integrating gene expression data on D. melanogaster, combining Semantic Web standards with light-weight approaches to Web programming based on Web 2.0 design patterns. To support researchers designing and validating functional genomic studies, OpenFlyData includes user-facing search applications providing intuitive access to and comparison of gene expression data from FlyAtlas, the BDGP in situ database, and FlyTED, using data from FlyBase to expand and disambiguate gene names. OpenFlyData’s services are also openly accessible, and are available for reuse by other bioinformaticians and application developers. Semi-automated methods and tools were developed to support labour- and knowledge-intensive tasks involved in deploying SPARQL services. These include methods for generating ontologies and relational-to-RDF mappings for relational databases, which we illustrate using the FlyBase Chado database schema; and methods for mapping gene identifiers between databases. The advantages of using Semantic Web standards for biomedical data integration are discussed, as are open issues. In particular, although the performance of open source SPARQL implementations is sufficient to query gene expression data directly from user-facing applications such as Web-based data fusions (a.k.a. mashups), we found open SPARQL endpoints to be vulnerable to denial-of-service-type problems, which must be mitigated to ensure reliability of services based on this standard. These results are relevant to data integration activities in translational bioinformatics. Availability: The gene expression search applications and SPARQL endpoints developed for OpenFlyData are deployed at http://openflydata.org. FlyUI, a library of JavaScript widgets providing re-usable user-interface components for Drosophila gene expression data, is available at http://flyui.googlecode.com. Software and ontologies to support transformation of data from FlyBase, FlyAtlas, BDGP and FlyTED to RDF are available at http://openflydata.googlecode.com. SPARQLite, an implementation of the SPARQL protocol, is available at http://sparqlite.googlecode.com. All software is provided under the GPL version 3 open source license.

Bounded transparency for automated inspection in agriculture

In agriculture, a major challenge is to automate knowledge-intensive tasks. Task-performing software is often opaque, which has a negative impact on a system’s adaptability and on the end user’s understanding and trust of the system’s operation. A more transparent, declarative way of specifying the expert knowledge required in such software is needed. We argue that a white-box approach is in principle preferred over systems in which the applied expertise is hidden in the system code. Internal transparency makes it easier to adapt the system to new conditions and to diagnose faulty behaviour. At the same time, explicitness comes at a price and is always bounded by practical considerations. Therefore we introduce the notion of bounded transparency, implying a balanced decision between transparency and opaqueness. The method proposed in this paper provides a set of pragmatic objectives and decision criteria to decide on each level of a task’s decomposition whether more transparency is sensible or whether delegation to a black-box component is acceptable. We apply the proposed method in a real-world case study involving a computer vision application for seedling inspection in horticulture and show how bounded transparency is obtained. We conclude that the proposed method offers structure to the application designer in making substantiated implementation decisions.

Augmenting paper-based learning with mobile phones

Paper and traditional books have been serving as useful tools in supporting knowledge-intensive tasks and school learning. Although learning strategies such as selective verbatim note-taking or question-asking may foster intentional recall or resolve comprehension difficulties in paper-based learning practice, improvement in learning may depend on the opportunity and quality of which students apply note-taking, review notes, or enhance comprehension through questioning. This study aims to complement a paper textbook with a mobile phone and to treat the combination as a whole to facilitate verbatim note-taking, resolving comprehension questions, and receiving reading recommendations. The textbook paragraphs were augmented with line numbers to facilitate coordination between the mobile phone and the paper textbook. An eight-week comparative study was conducted to explore the use of two reading vehicles. The results and findings show that using a mobile phone to augment paper-based learning is technically feasible and seems to promote the application of verbatim note-taking and posting comprehension questions for discussion. However, the results of two course tests indicate that consequent learning improvement seemed inconsistent among the students. A six-week case study was also conducted to explore the implications of the augmented support to students’ learning practice. The findings show that mobile phones as learning supportive tools to augment paper-based learning could support students’ planning and management of learning strategies or activities. The portability of mobile phones and paper textbooks and the ubiquitous connection of paper-based learning with an online learning community may provide the flexibility in planning ahead for suitable learning strategies or activities and may enhance students’ assessment for management of students’ learning goals.

Coping with large design spaces: design problem solving in fluidic engineering

This paper is about tool support for knowledge-intensive engineering tasks. In particular, it introduces software technology to assist the design of complex technical systems. There is a long tradition in automated design problem solving in the field of artificial intelligence, where, especially in the early stages, the search paradigm dictated many approaches. Later, in the so-called modern period, a better problem understanding led to the development of more adequate problem solving techniques. However, search still constitutes an indispensable part in computer-based design problem solving—albeit many human problem solvers get by without (almost). We tried to learn lessons from this observation, and one is presented in this paper. We introduce design problem solving by functional abstraction which follows the motto: construct a poor solution with little search, which then must be repaired. For the domain of fluidic engineering we have operationalized the paradigm by the combination of several high-level techniques. The red thread of this paper is design automation, but the presented technology does also contribute in the following respects: (a) productivity enhancement by relieving experts from auxiliary and routine tasks; (b) formulation, exchange, and documentation of knowledge about design; (c) requirements engineering, feasibility analysis, and validation.

An ontology-based approach to knowledge management in design processes

Engineering design processes comprise highly creative and knowledge-intensive tasks that involve extensive information exchange and communication among distributed teams. In such dynamic settings, traditional information management systems fail to provide adequate support due to their inflexible data structures and hard-wired usage procedures, as well as their restricted ability to integrate process and product information. In this paper, we advocate the idea of Process Data Warehousing as a means to provide a knowledge management and integration platform for such design processes. The key idea behind our approach is a flexible ontology-based schema with formally defined semantics that enables the capture and reuse of design experience, supported by advanced computer science methods.

Teletrabalho: subjugação e construção de subjetividades

Este artigo apresenta resultados de pesquisa de tese que buscou contribuir para o entendimento do teletrabalho, abordando-o da perspectiva teórica do poder/ saber. Trata-se de entender o teletrabalho como discurso que subjuga os indivíduos, mas que, ao mesmo tempo, constrói novos sujeitos e subjetividades. A pesquisa investigou esse processo de assujeitamento, explorando como os teletrabalhadores fazem sentido da experiência do teletrabalho, pela análise de suas práticas discursivas. Para isso, foram realizadas entrevistas abertas com 25 teletrabalhadores em arranjo de home office, residentes na cidade do Rio de Janeiro. Todos os entrevistados mantêm vínculo empregatício com as empresas para as quais teletrabalham e desenvolvem tarefas que podem ser caracterizadas como intensivas em conhecimento.

Special issue on agent-mediated knowledge management

The main goal of Knowledge Management (KM) is to provide relevant knowledge to assist users in executing knowledge intensive tasks. That is, KM aims at facilitating an environment where work critical information can be created, structured, shared, distributed and used. To be effective, KM must provide users with relevant knowledge, at the right time and in the right form, that enables users to better perform their tasks. Knowledge Management (KM) has been a predominant trend in business in the recent years. Though KM is primarily a management discipline (with a background in human resource management, strategy, and organizational behavior), the role of information technology as an enabling factor is widely recognized, and – after a first phase where merely general purpose technology like Internet/Intranets or email were found to be useful to facilitate KM – variety of proposals exist to support KM with specialized information systems [3]. Often, IT research for KM focused on the comprehensive use of an organization’s knowledge, thus aiming at the completeness of distribution of relevant information. Technically, this is typically supported by centralized approaches: knowledge about people, processes or domain knowledge is represented and maintained in global repositories which serve as sources to meet a knowledge worker’s (potentially complex) information needs. Such repositories may be structured by global ontologies (e.g., in form of knowledge portals) or they may be rather flat and processed by weak (i.e. not knowledge–intensive) methods like statistics–based information retrieval or collaborative filtering. However, as is often mentioned in the literature, knowledge tasks have a collaborative aspect, that is, an individual can best acquire and use knowledge by making use of existing relations among people (communities) or by reusing and personalizing information already collected and annotated by others. Furthermore, a KM system must be able to adapt to changes in the environment, to the different needs and preferences of users, and to integrate naturally with existing work methods, tools and processes. That is, KM systems must be reactive (able to respond to user requests or environment changes) and proactive (able to take initiatives to attend to user needs). These aspects also characterise intelligent software agents, what seems to indicate the applicability of agent technology in the KM area. Intelligent agents as a paradigm for developing software applications are currently the focus of intense interest on the part of many fields of computer science and artificial intelligence. A software agent is an autonomous entity that perceives and acts on its environment in order to achieve its goals. Wooldridge and Jennings [4] defined four properties that form a weak definition of Agency:

Software maintenance seen as a knowledge management issue

Creating and maintaining software systems is a knowledge intensive task. One needs to have a good understanding of the application domain, the problem to solve and all its requirements, the software process used, technical details of the programming language(s), the system’s architecture and how the different parts fit together, how the system interacts with its environment, etc. All this knowledge is difficult and costly to gather. It is also difficult to store and usually lives only in the mind of the software engineers who worked on a particular project. If this is a problem for development of new software, it is even more for maintenance, when one must rediscover lost information of an abstract nature from legacy source code among a swarm of unrelated details. In this paper, we submit that this lack of knowledge is one of the prominent problems in software maintenance. To try to solve this problem, we adapted a knowledge extraction technique to the knowledge needs specific to software maintenance. We explain how we explicit the knowledge discovered on a legacy software during maintenance so that it may be recorded for future use. Some applications on industry maintenance projects are reported.

Using ontologies linked with geometric models to reason about penetrating injuries

Medical assessment of penetrating injuries is a difficult and knowledge-intensive task, and rapid determination of the extent of internal injuries is vital for triage and for determining the appropriate treatment. Physical examination and computed tomographic (CT) imaging data must be combined with detailed anatomic, physiologic, and biomechanical knowledge to assess the injured subject. We are developing a methodology to automate reasoning about penetrating injuries using canonical knowledge combined with specific subject image data. In our approach, we build a three-dimensional geometric model of a subject from segmented images. We link regions in this model to entities in two knowledge sources: (1) a comprehensive ontology of anatomy containing organ identities, adjacencies, and other information useful for anatomic reasoning and (2) an ontology of regional perfusion containing formal definitions of arterial anatomy and corresponding regions of perfusion. We created computer reasoning services ("problem solvers") that use the ontologies to evaluate the geometric model of the subject and deduce the consequences of penetrating injuries. We developed and tested our methods using data from the Visible Human. Our problem solvers can determine the organs that are injured given particular trajectories of projectiles, whether vital structures--such as a coronary artery--are injured, and they can predict the propagation of injury ensuing after vital structures are injured. We have demonstrated the capability of using ontologies with medical images to support computer reasoning about injury based on those images. Our methodology demonstrates an approach to creating intelligent computer applications that reason with image data, and it may have value in helping practitioners in the assessment of penetrating injury.

Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning

Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring the information needs of workers during the problem-solving process. Such processes are usually knowledge intensive tasks; therefore, workers need effective knowledge support that gives them the information necessary to identify the causes of a problem and enables them to take appropriate action to resolve the situation. In this paper, we propose a mining-based knowledge support system for problem-solving. In addition to adopting case-based reasoning to identify similar situations and the action taken to solve them, the proposed system employs text mining (information retrieval) techniques to extract the key concepts of situations and actions. These concepts form profiles that model workers’ information needs when handling problems. Effective knowledge support can thus be facilitated by providing workers with situation/action-relevant information based on the profiles. Moreover, association rule mining is used to discover hidden knowledge patterns from historical problem-solving logs. The discovered patterns identify frequent associations between situations and actions, and can therefore provide decision-making knowledge, i.e., appropriate actions for handling specific situations. We develop a prototype system to demonstrate the effectiveness of providing situation/action relevant information and decision-making knowledge to help workers solve problems.

Knowledge integration using problem spaces: A study in resource-constrained project scheduling

The need to develop schedules for projects with resource constraints and cash flows arises in organizational settings ranging from construction planning to research and development. Given the intractable nature of the problem, a variety of knowledge sources relevant to the project scheduling task have been identified in the Operations Management literature. These include a large number of heuristic procedures that can be used to generate feasible project schedules as well as recent neural network-based approaches that can select appropriate heuristic procedures to apply to a specific instance of the project scheduling problem. While integrated application of these knowledge sources is required to effectively support scheduling, previous work has focussed on developing and implementing them in isolation. The problem space computational model presented in this paper addresses this shortcoming by integrating these various knowledge sources, thus enabling the development of decision support systems for resource constrained project scheduling. More generally, the modeling approach used in this paper can be applied to create systems to assist knowledge intensive tasks that arise in many organizational settings.

A Hybrid Approach to Nonconformance Tracking and Recovery

The nonconformance diagnosis problem has been a major issue facing industry and academia over the years. Research has been carried out on technologies for different aspects of nonconformance diagnosis such as nonconformance monitoring, prediction, prevention, classification, tracking, and recovery. Despite these advances, nonconformance tracking and recovery still receive many concerns due to the fact that they are knowledge intensive and experience-based tasks, which in complex manufacturing environments can sometimes be beyond the capabilities of skilled operators and engineers. In addition, the existing systems for nonconformance tracking and recovery are usually special purpose systems. They lack the capabilities to migrate to new working domains. This paper proposes a generic intelligent nonconformance tracking and recovery (GINTR) system. In conjunction with computational intelligent techniques such as Artificial Neural Networks (ANN) and Genetic Algorithm (GA), the system identifies the root causes of a nonconformance and provides timely corrective actions. The drive towards designing such a system is motivated by the need to implement a generic base of system capabilities that is reliable, economical, scalable, and provides a stable foundation for migrating the system to different domains.

The Semantic Electronic Government: knowledge management for citizen relationship and new assessment scenarios

The case of governance in the internet age requires a critical revision of assumptions. Government and citizens are not just two entities but through their interaction provide a wide context of analysis. Citizen Relationship Management (CRM) is a knowledge-intensive task, which requires an in-depth analysis of knowledge infrastructures, knowledge flows and dynamic transformations. In this paper, we will concentrate on the description of the basic aspects of the Knowledge Management (KM) convergence in e-government. Further, the diversity of structures, regulations and procedures affecting networks of heterogeneous administrative units represents a challenge for semantic integration. Semantic Web technologies have been proposed as a potential solution for alleviating such integration problems. However, Semantic Web initiatives must be based on clear and realistic objectives that can be assessed in the framework of political decisions. This paper attempts to delineate the main aspects of a framework for assessment, in an attempt to delineate a roadmap for future applied research on the area.

A multiagent and service-oriented architecture for developing adaptive e-learning systems

This paper presents an architecture for developing adaptive e-learning systems using intelligent agent technology and Web Services (WS) technology. Intelligent software agents are designed for supporting users to accomplish knowledge-intensive tasks. WS are designed for the integration of distributed knowledge and information resources and exposed as standard services using widely accepted protocols. In particular, the Domain Model Ontology in the architecture can be shared between applications in the Semantic Web setting for reuse and interoperability. To demonstrate the feasibility of the proposed approach, we implement a prototype of a programme planning and scheduling system for adaptive e-learning.

Controle em novas formas de trabalho: teletrabalhadores e o discurso do empreendimento de si

As tecnologias de controle nas organizações contemporâneas caracterizam-se não mais pela supervisão direta sobre os atos do trabalhador, mas pelo controle à distância, pela formatação dos sujeitos "adequados" para o desempenho dos papéis organizacionais. Até o momento, o teletrabalho pode ser considerado a iniciativa gerencial que põe em prática, de maneira mais contundente, o afastamento indivíduo-organização. Este artigo busca explorar como o esforço de controle à distância se dá nessa situação em que a separação física organização-indivíduo é clara e estabelecida. Foram entrevistados 25 teletrabalhadores residentes na cidade do Rio de Janeiro, todos mantendo vínculo empregatício com as empresas para as quais teletrabalham e desenvolvendo tarefas que podem ser caracterizadas como intensivas em conhecimento.

CREATING A NEW SERVICE ON THE WEB

Service functions are major component of every industry. The evolution of service functions have seen manually intensive tasks being replaced by a combination of mental and interactive tasks. As a part of this transformation, we have seen an increasing dependency on the high technology, actually information technology, mostly with computer-communications and the web. This high technology will finally replace the service functions by a combination of knowledge intensive, interactive and customer's individual tasks in the future. However, these knowledge intensive, interactive and customer's individual service functions could be enjoyable for the restricted person, due to the difficulties of the man (customers)-machine (PCs, web-server machines) interface and also the service providers are not able to obtain much more customer's individual and abstruse information for providing the customer's individual services, due to the customers' privacy problem. In this paper we focus on of knowledge intensive, interactive and customer's individual services in the future. We emphasize the importance of developing the mechanism of humanization for making these services enjoyable for anyone and the personalization for enjoyable the more individual services, and show some examples for these mechanisms in which humanization is realized by humanoid robots and personalization is realized by customers' privacy preserving scheme. We present a feedback system including the mechanism for humanization and personalization. By performing this feedback system, anyone who is not familiar to machines, can interact with machines like human-beings and can poll or report even though privacy related information anonymously to service providers. By periodically performing this feedback system, the customers' knowledge, information, suggestions and ideas can could be accumulated and the service providers could provide more individual services to customers, Once humanization and personalization mechanism are developed, by performing our feedback systems, we enhance customers' convenience and reduce the incidence of operation errors and thereby improve their productivity and quality of their life.

A generic distributed architecture for nonconformance diagnosing systems

The nonconformance diagnosis problem has been a major issue facing industry and academia over the years. Nonconformance diagnosis is a knowledge-intensive and experience-based task, which in a complex manufacturing environment can sometimes be beyond the capabilities of skilled operators and engineers. The existing systems for nonconformance diagnosis are usually special purpose systems. They lack the capabilities to adapt to new working domains. In addition, since there is no standard protocol for the information exchange among different diagnosis systems, the existing systems cannot cooperate with each other to form an integrated solution to complex nonconformance diagnosis problems. This paper proposes a generic distributed architecture for nonconformance diagnosis. The drive towards designing such a system is motivated by the need to implement a generic base of system capabilities that is reliable, economical and provides a stable foundation for adding more functionality as diagnosis needs grow and change. The proposed architecture is designed using the multi-agent systems (MAS) technology and has the capacities of integration, cooperation, coordination, robustness and viability. Under such an open distributed architecture, different nonconformance diagnosis systems designed for special diagnosis applications can communicate with each other and share information. In addition, it is possible to dynamically integrate new subsystems into or remove existing subsystems from the system without stopping and reinitializing the working environment. A prototype system based on Common Object Request Broker Architecture (CORBA) is being developed.

Information Processing Moderators of the Effectiveness of Trust-Based Governance in Interfirm R&D Collaboration

It is generally assumed that improved outcomes accompany the use of trust as a governance mechanism in an interfirm relationship. Briefly, trust is a social lubricant that reduces the friction costs of existing trade and/or serves to increase the scope of trade. In contrast to this universalistic view, we posit that the performance of trust-based governance is contingent on the ability of trading partners to “read” each other and learn about counterpart behavior. These information-processing abilities allow firms to assess partner trustworthiness better, which reduces the risk of misplaced trust. The increased efficacy of communication and learning from one another also enables them to better capitalize from the adaptation and revision possibilities uncovered through trust-based governance during the task-execution phase. Given the central role of these cognitive requirements, we assess these contingent effects with data from a knowledge-intensive task setting. Using a sample of 129 firms that have engaged outside contractors on client-sponsored R&D projects, we find strong support for our thesis. Specifically, we find that trust-based governance has a larger positive impact on task performance when the client is more skilled at understanding the outsourced tasks at hand, the task itself requires skills that are relatively more readily taught (less tacit), and the task itself is organized in parallel with work being done at the contractor as well as the client. As a corollary, we also find that firms adopt trust-based governance to a greater extent as these information-processing abilities increase, as well as with the colocation of the contractor and client. Suggestions for engineering trust-based governance are offered.

A formal framework of knowledge to support rational psychoactive drug selection

Rational psychoactive drug selection is a data and knowledge intensive task which requires true expertise from clinical, pathophysiological and pharmacotherapeutic knowledge. This paper presents a framework of knowledge, which relates concepts from several disciplines required for psychoactive drug selection in a formal way. A framework, when based on formal semantics, avoids ambiguity and gives conceptual clarity and supports precise use of terminology which is required when many domain experts (clinicians, pharmacologists and basic science researchers) are involved. A formal framework permits linking of existing classification systems. It furthermore can serve as a knowledge base for drug selection decision support systems.