282,918 research outputs found

    Semantic data mining and linked data for a recommender system in the AEC industry

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    Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

    Using recommendations to help novices to reuse design knowledge

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    This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-21530-8_35. Copyright @ Springer 2011.The use of pattern languages is not so straightforward since its users have to identify the patterns they need, browsing the language and understanding both the benefits and trade-offs of each pattern as well as the relations and interactions it has with other patterns. Novice designers might benefit from tools that assist them in this learning task. In this paper we describe a recommendation tool embedded in a visual environment for pattern-based design which aims at suggesting patterns to help novice designers to produce better designs and understand the language.Spanish Ministry of Science and Innovatio

    Algorithm Selection Framework for Cyber Attack Detection

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    The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

    Recommendation, collaboration and social search

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    This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology. Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly. We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements

    IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems

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    Recommendation systems have proven to be useful in various application domains. However, current solutions are usually ad-hoc systems which are tightly-coupled with the application domain. We present the IC-Service, a recommendation service that can be included in any system in a loosely coupled way. The implementation follows the principles of service oriented computing and provides a solution to various problems arising in recommendation systems, e.g. to the problem of meta-recommendation systems development. Moreover, when properly configured, the IC-Service can be used by different applications (clients), and several independent instances of the IC-Service can collaborate to produce better recommendations. Service architecture and communication protocols are presented. The paper describes also ongoing work and applications based on the IC-Service

    A Physiologically Based System Theory of Consciousness

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    A system which uses large numbers of devices to perform a complex functionality is forced to adopt a simple functional architecture by the needs to construct copies of, repair, and modify the system. A simple functional architecture means that functionality is partitioned into relatively equal sized components on many levels of detail down to device level, a mapping exists between the different levels, and exchange of information between components is minimized. In the instruction architecture functionality is partitioned on every level into instructions, which exchange unambiguous system information and therefore output system commands. The von Neumann architecture is a special case of the instruction architecture in which instructions are coded as unambiguous system information. In the recommendation (or pattern extraction) architecture functionality is partitioned on every level into repetition elements, which can freely exchange ambiguous information and therefore output only system action recommendations which must compete for control of system behavior. Partitioning is optimized to the best tradeoff between even partitioning and minimum cost of distributing data. Natural pressures deriving from the need to construct copies under DNA control, recover from errors, failures and damage, and add new functionality derived from random mutations has resulted in biological brains being constrained to adopt the recommendation architecture. The resultant hierarchy of functional separations can be the basis for understanding psychological phenomena in terms of physiology. A theory of consciousness is described based on the recommendation architecture model for biological brains. Consciousness is defined at a high level in terms of sensory independent image sequences including self images with the role of extending the search of records of individual experience for behavioral guidance in complex social situations. Functional components of this definition of consciousness are developed, and it is demonstrated that these components can be translated through subcomponents to descriptions in terms of known and postulated physiological mechanisms

    Detailed empirical studies of student information storing in the context of distributed design team-based project work

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    This paper presents the findings of six empirical case studies investigating the information stored by engineering design students in distributed team-based Global Design Projects. The aim is to understand better how students store distributed design information in order to prepare them for work in today‟s international and global context. This paper outlines the descriptive element of the work, the qualitative and quantitative research methods used and the results. It discusses the issues around the emergent themes of information storing; information storing systems; information storing patterns; and information strategy, making recommendations; establishing that there is a need for more prescriptive measures to supporting distributed design information management. This work will be of great value to industry also
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