A pipelined architecture for distributed text query evaluation

Two principal query-evaluation methodologies have been described for cluster-based implementation of distributed information retrieval systems: document partitioning and term partitioning. In a document-partitioned system, each of the processors hosts a subset of the documents in the collection, and executes every query against its local sub-collection. In a term-partitioned system, each of the processors hosts a subset of the inverted lists that make up the index of the collection, and serves them to a central machine as they are required for query evaluation. In this paper we introduce a pipelined query-evaluation methodology, based on a term-partitioned index, in which partially evaluated queries are passed amongst the set of processors that host the query terms. This arrangement retains the disk read benefits of term partitioning, but more effectively shares the computational load. We compare the three methodologies experimentally, and show that term distribution is inefficient and scales poorly. The new pipelined approach offers efficient memory utilization and efficient use of disk accesses, but suffers from problems with load balancing between nodes. Until these problems are resolved, document partitioning remains the preferred method. Alistair Moffat was supported by the Australian Research Council, the ARC Special Research Center for Perceptive and Intelligent Machines in Complex Environments, and the NICTA Victoria Laboratory. William Webber and Justin Zobel were supported by the Australian Research Council. Ricardo Baeza-Yates was supported by Grant P01-029-F from Millennium Initiative of Mideplan, Chile; and by the University of Melbourne as a visiting scholar at the time this project was undertaken.     

文本分类C#实现*

设计并实现一个基于向量空间模型和简单贝叶斯的文本分类系统，系统采用层级多标签的分类策略。详细介绍词语切分统计、终分类器值计算、层级小类校正和兼类判断四个子系统模块。基于向量空间模型分类的第一级大类和层级小类的微平均分别为89.7%和77.8%，简单贝叶斯分别为67.6%和66.5%。     

Risk terrain modeling predicts child maltreatment

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.     

Governing software: networks,databases and algorithmic power in the digital governance of public education

This article examines the emergence of ‘digital governance’ in public education in England. Drawing on and combining concepts from software studies, policy and political studies, it identifies some specific approaches to digital governance facilitated by network-based communications and database-driven information processing software that are being discursively promoted in education by cross-sectoral intermediary organizations. Such intermediaries, including National Endowment for Science, Technology and the Arts, Demos, the Innovation Unit, the Education Foundation and the Nominet Trust, are increasingly seeking to participate in new digitally mediated forms of educational governance. Through their promotion of network-based pedagogies and database-driven analytics software, these organizations are seeking to delegate educational decision-making to socio-algorithmic forms of power that have the capacity to predict, govern and activate learners' capacities and subjectivities.     

国史知识的语义揭示与组织方法研究

深入开展中华人民共和国国史的教育和研究一直是各方高度重视的工作。将国史知识进行语义揭示和组织,对于国史的教育和研究具有重要意义。在参考相关研究成果的基础上,本文提出了“向下挖掘,向上组织”的国史知识语义揭示与组织方法。这一方法以国史本体为基础,在对隐藏于国史资源文本条目中的国史知识对象和相关事实进行语义挖掘和揭示的基础之上,通过国史知识对象的关联,构建国史知识网络,并基于时间、类属、层级及统计等关系,对国史知识内容进行更高层次的多维组织展示,并基于这一思路开发了相应的系统平台,实现了国史知识的揭示、重组和其他创新应用。本文所提出的国史知识语义揭示和组织方法对其他类型知识的开发利用具有参考价值。图7。参考文献12。     

基于数据挖掘的学生投入模型与学习分析

学生投入是高等教育质量和学习成果的重要影响因素,对于高等教育评估与改革具有积极作用,受到了国内外研究者的广泛关注。文章以构建学生投入模型为基础,采用典型相关分析和数据挖掘方法相结合,识别学生投入的相关因素,并对学生学习行为进行分类研究。分析发现学生投入与学生家庭背景、学生入学前特征、学校特征及课程作业之间存在着显著相关关系,不同的学生投入及其学习行为表现有助于加深学校对学生学习行为的了解,更好地研究学习规律的新趋势,为审视高校以生为本、以学为中心的人才培养措施和多元性发展,提供了重要的参考与支持。     

Big and open linked data analytics ecosystem: Theoretical background and essential elements

Big and open linked data are often mentioned together because storing, processing, and publishing large amounts of these data play an increasingly important role in today's society. However, although this topic is described from the political, economic, and social points of view, a technical dimension, which is represented by big data analytics, is insufficient. The aim of this review article was to provide a theoretical background of big and open linked data analytics ecosystem and its essential elements. First, the key terms were introduced including related dimensions. Then, the key lifecycle phases were defined and involved stakeholders were identified. Finally, a conceptual framework was proposed. In contrast to previous research, the new ecosystem is formed by interactions of stakeholders in the following dimensions and their sub-dimensions: transparency, engagement, legal, technical, social, and economic. These relationships are characterized by the most important requisites and public policy choices affecting the data analytics ecosystem together with the key phases and activities of the data analytics lifecycle. The findings should contribute to relevant initiatives, strategies, and policies and their effective implementation.     

学习分析：从源起到实践与研究

学习分析是“大数据”在教育领域的应用,引发了教育技术发展的第三次浪潮,并获得学术界的广泛关注。本文梳理了学习分析的形成过程,然后从利益相关者、研究目标、研究对象、技术方法四个维度,回顾了近五年来国内外学者在学习分析方面的研究成果,并提出未来发展趋势和可能遇到的挑战,便于相关人员制定教育决策、优化教育管理过程以及完善学习过程。研究结果表明,学习分析研究主题主要涵盖学习者知识建模、学习情绪建模、学习行为特征抽取、学习活动跟踪、学习者建模、学位获取分析、教学资源和教学策略优化、自适应学习系统和个性化学习、在线学习影响因素分析九个方面;分析数据主要来源于集中式学习环境、分布式学习环境以及身体活动数据;常用分析方法包括统计分析、信息可视化、数据挖掘、社会网络分析、话语分析和网站分析。目前,学习分析研究遇到的挑战包括教育数据预处理难度大、数据访问权限不明确、学习分析适用性有限。虽然学习分析尚处于发展初期,但由于能够为教育系统各级决策提供科学参考,已经成为教育信息化的重要内容之一。