18,341 research outputs found

    Global Innovations in Measurement and Evaluation

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    We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas

    Data analytics 2016: proceedings of the fifth international conference on data analytics

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    Explain3D: Explaining Disagreements in Disjoint Datasets

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    Data plays an important role in applications, analytic processes, and many aspects of human activity. As data grows in size and complexity, we are met with an imperative need for tools that promote understanding and explanations over data-related operations. Data management research on explanations has focused on the assumption that data resides in a single dataset, under one common schema. But the reality of today's data is that it is frequently un-integrated, coming from different sources with different schemas. When different datasets provide different answers to semantically similar questions, understanding the reasons for the discrepancies is challenging and cannot be handled by the existing single-dataset solutions. In this paper, we propose Explain3D, a framework for explaining the disagreements across disjoint datasets (3D). Explain3D focuses on identifying the reasons for the differences in the results of two semantically similar queries operating on two datasets with potentially different schemas. Our framework leverages the queries to perform a semantic mapping across the relevant parts of their provenance; discrepancies in this mapping point to causes of the queries' differences. Exploiting the queries gives Explain3D an edge over traditional schema matching and record linkage techniques, which are query-agnostic. Our work makes the following contributions: (1) We formalize the problem of deriving optimal explanations for the differences of the results of semantically similar queries over disjoint datasets. (2) We design a 3-stage framework for solving the optimal explanation problem. (3) We develop a smart-partitioning optimizer that improves the efficiency of the framework by orders of magnitude. (4)~We experiment with real-world and synthetic data to demonstrate that Explain3D can derive precise explanations efficiently

    The State, the Market, Economic Growth and Poverty in China

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    Accelerating Action Against Child Labour

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    [Excerpt] The focus of this third Global Report on child labour under the follow-up to the ILO Declaration on Fundamental Principles and Rights at Work is about honouring the commitments made in 2006 with the adoption of a Global Action Plan. The Action Plan set out time-bound targets for the elimination of child labour. Important obligations and commitments were entered into by ILO constituents. Four years is a short time to ensure major change, but it does, however, permit the Organization to take stock and gauge progress. In particular we need to know whether the world is on track to meeting the target of eliminating all the worst forms of child labour by 2016. To this end, this Global Report adopts the same four-part structure as the two previous Reports. Part I presents a dynamic global picture, updating global trends introduced in the 2006 Global Report and setting out progress in the ratification of ILO standards. Part II reviews the progress made in implementing the Global Action Plan. From this review, Part III examines key challenges and gaps that remain and how these can be addressed. Finally, Part IV sets out an agenda to accelerate progress towards meeting the 2016 target

    Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

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    Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided
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