2,413 research outputs found

    Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

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    Rule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call “causal forces.” Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF’s forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF’s rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals

    09251 Abstracts Collection -- Scientific Visualization

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    From 06-14-2009 to 06-19-2009, the Dagstuhl Seminar 09251 ``Scientific Visualization \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, over 50 international participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

    Context Aware Middleware Architectures: Survey and Challenges

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    Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

    Review of Research on Human Trust in Artificial Intelligence

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    Artificial Intelligence (AI) represents today\u27s most advanced technologies that aim to imitate human intelligence. Whether AI can successfully be integrated into society depends on whether it can gain users’ trust. We conduct a comprehensive review of recent research on human trust in AI and uncover the significant role of AI’s transparency, reliability, performance, and anthropomorphism in developing trust. We also review how trust is diversely built and calibrated, and how human and environmental factors affect human trust in AI. Based on the review, the most promising future research directions are proposed

    A Survey on Actionable Knowledge

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    Actionable Knowledge Discovery (AKD) is a crucial aspect of data mining that is gaining popularity and being applied in a wide range of domains. This is because AKD can extract valuable insights and information, also known as knowledge, from large datasets. The goal of this paper is to examine different research studies that focus on various domains and have different objectives. The paper will review and discuss the methods used in these studies in detail. AKD is a process of identifying and extracting actionable insights from data, which can be used to make informed decisions and improve business outcomes. It is a powerful tool for uncovering patterns and trends in data that can be used for various applications such as customer relationship management, marketing, and fraud detection. The research studies reviewed in this paper will explore different techniques and approaches for AKD in different domains, such as healthcare, finance, and telecommunications. The paper will provide a thorough analysis of the current state of AKD in the field and will review the main methods used by various research studies. Additionally, the paper will evaluate the advantages and disadvantages of each method and will discuss any novel or new solutions presented in the field. Overall, this paper aims to provide a comprehensive overview of the methods and techniques used in AKD and the impact they have on different domains

    A novel hybrid recommendation system for library book selection

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    Abstract. Increasing number of books published in a year and decreasing budgets have made collection development increasingly difficult in libraries. Despite the data to help decision making being available in the library systems, the librarians have little means to utilize the data. In addition, modern key technologies, such as machine learning, that generate more value out data have not yet been utilized in the field of libraries to their full extent. This study was set to discover a way to build a recommendation system that could help librarians who are struggling with book selection process. This thesis proposed a novel hybrid recommendation system for library book selection. The data used to build the system consisted of book metadata and book circulation data of books located in Joensuu City Library’s adult fiction collection. The proposed system was based on both rule-based components and a machine learning model. The user interface for the system was build using web technologies so that the system could be used via using web browser. The proposed recommendation system was evaluated using two different methods: automated tests and focus group methodology. The system achieved an accuracy of 79.79% and F1 score of 0.86 in automated tests. Uncertainty rate of the system was 27.87%. With these results in automated tests, the proposed system outperformed baseline machine learning models. The main suggestions that were gathered from focus group evaluation were that while the proposed system was found interesting, librarians thought it would need more features and configurability in order to be usable in real world scenarios. Results indicate that making good quality recommendations using book metadata is challenging because the data is high dimensional categorical data by its nature. Main implications of the results are that recommendation systems in domain of library collection development should focus on data pre-processing and feature engineering. Further investigation is suggested to be carried out regarding knowledge representation

    A new fuzzy ontology development methodology (FODM) proposal

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    There is an upsurge in applying fuzzy ontologies to represent vague information in the knowledge representation field. Current research in the fuzzy ontologies paradigm mainly focuses on developing formalism languages to represent fuzzy ontologies, designing fuzzy ontology editors, and building fuzzy ontology applications in different domains. Less focus falls on establishing a formal methodological approach for building fuzzy ontologies. Existing fuzzy ontology development methodologies, such as the IKARUS-Onto methodology and Fuzzy Ontomethodology, provide formalized schedules for the conversion from crisp ontologies into fuzzy ones. However, a formal guidance on how to build fuzzy ontologies from scratch still lacks in current research. Therefore, this paper presents the first methodology, named FODM, for developing fuzzy ontologies from scratch. The proposed FODM can provide a very good guideline for formally constructing fuzzy ontologies in terms of completeness, comprehensiveness, generality, efficiency, and accuracy. To explain how the FODM works and demonstrate its usefulness, a fuzzy seabed characterization ontology is built based on the FODM and described step-by-step

    Management-By-Objectives in Healthcare

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