Improving sustainability through intelligent cargo and adaptive decision making

In the current society, logistics is faced with the challenge to meet more stringent sustainability goals. Shippers and transport service providers both aim to reduce the carbon footprint of their logistic operations. To do so, optimal use of logistics resources and physical infrastructure should be aimed for. An adaptive decision making process for the selection of a specific transport modality, transport provider and timeslot (aimed at minimisation of the carbon footprint) enables shippers to achieve this. This requires shippers to have access to up-to-date capacity information from transport providers (e.g. current and scheduled loading status of the various transport means and information on carbon footprint) and traffic information (e.g. city logistics and current traffic information). A prerequisite is an adequate infrastructure for collaboration and open exchange of information between the various stakeholders in the logistics value chain to obtain the up-to-date information. This paper gives a view on how such an advanced information infrastructure can be realised, currently being developed within the EU iCargo project. The paper describes a reference logistics value chain, including business benefits for each of the roles in the logistics value chain of aiming for sustainability. A case analysis is presented that reflects a practical situation in which the various roles collaborate and exchange information for realizing sustainability goals, using adaptive decision making for selecting a transport modality, transport provider, and timeslot. A high-level overview is provided of the requirements on and technical implementation of the supporting advanced infrastructure for collaboration and open information exchange.In the current society, logistics is faced with the challenge to meet more stringent sustainability goals. Shippers and transport service providers both aim to reduce the carbon footprint of their logistic operations. To do so, optimal use of logistics resources and physical infrastructure should be aimed for. An adaptive decision making process for the selection of a specific transport modality, transport provider and timeslot (aimed at minimisation of the carbon footprint) enables shippers to achieve this. This requires shippers to have access to up-to-date capacity information from transport providers (e.g. current and scheduled loading status of the various transport means and information on carbon footprint) and traffic information (e.g. city logistics and current traffic information). A prerequisite is an adequate infrastructure for collaboration and open exchange of information between the various stakeholders in the logistics value chain to obtain the up-to-date information. This paper gives a view on how such an advanced information infrastructure can be realised, currently being developed within the EU iCargo project. The paper describes a reference logistics value chain, including business benefits for each of the roles in the logistics value chain of aiming for sustainability. A case analysis is presented that reflects a practical situation in which the various roles collaborate and exchange information for realizing sustainability goals, using adaptive decision making for selecting a transport modality, transport provider, and timeslot. A high-level overview is provided of the requirements on and technical implementation of the supporting advanced infrastructure for collaboration and open information exchange.In the current society, logistics is faced with the challenge to meet more stringent sustainability goals. Shippers and transport service providers both aim to reduce the carbon footprint of their logistic operations. To do so, optimal use of logistics resources and physical infrastructure should be aimed for. An adaptive decision making process for the selection of a specific transport modality, transport provider and timeslot (aimed at minimisation of the carbon footprint) enables shippers to achieve this. This requires shippers to have access to up-to-date capacity information from transport providers (e.g. current and scheduled loading status of the various transport means and information on carbon footprint) and traffic information (e.g. city logistics and current traffic information). A prerequisite is an adequate infrastructure for collaboration and open exchange of information between the various stakeholders in the logistics value chain to obtain the up-to-date information. This paper gives a view on how such an advanced information infrastructure can be realised, currently being developed within the EU iCargo project. The paper describes a reference logistics value chain, including business benefits for each of the roles in the logistics value chain of aiming for sustainability. A case analysis is presented that reflects a practical situation in which the various roles collaborate and exchange information for realizing sustainability goals, using adaptive decision making for selecting a transport modality, transport provider, and timeslot. A high-level overview is provided of the requirements on and technical implementation of the supporting advanced infrastructure for collaboration and open information exchange

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg’s (2005) metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (\u3e0.6), a Fisher’s exact test for nonparametric data was used to determine significance (p \u3c .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes

A framework for developing engineering design ontologies within the aerospace industry

This paper presents a framework for developing engineering design ontologies within the aerospace industry. The aim of this approach is to strengthen the modularity and reuse of engineering design ontologies to support knowledge management initiatives within the aerospace industry. Successful development and effective utilisation of engineering ontologies strongly depends on the method/framework used to develop them. Ensuring modularity in ontology design is essential for engineering design activities due to the complexity of knowledge that is required to be brought together to support the product design decision-making process. The proposed approach adopts best practices from previous ontology development methods, but focuses on encouraging modular architectural ontology design. The framework is comprised of three phases namely: (1) Ontology design and development; (2) Ontology validation and (3) Implementation of ontology structure. A qualitative research methodology is employed which is composed of four phases. The first phase defines the capture of knowledge required for the framework development, followed by the ontology framework development, iterative refinement of engineering ontologies and ontology validation through case studies and experts’ opinion. The ontology-based framework is applied in the combustor and casing aerospace engineering domain. The modular ontologies developed as a result of applying the framework and are used in a case study to restructure and improve the accessibility of information on a product design information-sharing platform. Additionally, domain experts within the aerospace industry validated the strengths, benefits and limitations of the framework. Due to the modular nature of the developed ontologies, they were also employed to support other project initiatives within the case study company such as role-based computing (RBC), IT modernisation activity and knowledge management implementation across the sponsoring organisation. The major benefit of this approach is in the reduction of man-hours required for maintaining engineering design ontologies. Furthermore, this approach strengthens reuse of ontology knowledge and encourages modularity in the design and development of engineering ontologies

Boundary Objects and their Use in Agile Systems Engineering

Agile methods are increasingly introduced in automotive companies in the attempt to become more efficient and flexible in the system development. The adoption of agile practices influences communication between stakeholders, but also makes companies rethink the management of artifacts and documentation like requirements, safety compliance documents, and architecture models. Practitioners aim to reduce irrelevant documentation, but face a lack of guidance to determine what artifacts are needed and how they should be managed. This paper presents artifacts, challenges, guidelines, and practices for the continuous management of systems engineering artifacts in automotive based on a theoretical and empirical understanding of the topic. In collaboration with 53 practitioners from six automotive companies, we conducted a design-science study involving interviews, a questionnaire, focus groups, and practical data analysis of a systems engineering tool. The guidelines suggest the distinction between artifacts that are shared among different actors in a company (boundary objects) and those that are used within a team (locally relevant artifacts). We propose an analysis approach to identify boundary objects and three practices to manage systems engineering artifacts in industry

The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

Towards personalization in digital libraries through ontologies

In this paper we describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the elements which take part in a digital library scenario of use. The main goal of this project is to help the users of a digital library to improve their experience of use by means of two complementary strategies: first, by maintaining a complete history record of his or her browsing and searching activities, which is part of a navigational user profile which includes preferences and all the aspects related to community involvement; and second, by reusing all the knowledge which has been extracted from previous usage from other users with similar profiles. This can be accomplished in terms of narrowing and focusing the search results and browsing options through the use of a recommendation system which organizes such results in the most appropriate manner, using ontologies and concepts drawn from the semantic web field. The complete integration of the experience of use of a digital library in the learning process is also pursued. Both the usage and information organization can be also exploited to extract useful knowledge from the way users interact with a digital library, knowledge that can be used to improve several design aspects of the library, ranging from internal organization aspects to human factors and user interfaces. Although this project is still on an early development stage, it is possible to identify all the desired functionalities and requirements that are necessary to fully integrate the use of a digital library in an e-learning environment