Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection or information diffusion. In this paper, we propose study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the "appLicAtion orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network structure can be applied to compute the node "diffusive proximity" scores, which capture both local and global network structures. Based on these computed scores, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl

Heterogeneous Effects in Education: The Promise and Challenge of Incorporating Intersectionality into Quantitative Methodological Approaches

To date, the theory of intersectionality has largely guided qualitative efforts in social science and education research. Translating the construct to new methodological approaches is inherently complex and challenging, but offers the possibility of breaking down silos that keep education researchers with similar interests—but different methodological approaches—from sharing knowledge. Quantitative approaches that emphasize the varied impacts of individual identities on educational outcomes move beyond singular dimensions capturing individual characteristics, drawing a parallel to intersectionality. Scholars interested in heterogeneous effects recognize the shortcomings of focusing on the effect of a single social identity. This integrative review explores techniques used in quantitative research to examine heterogeneous effects across individual background, drawing on methodological literature from the social sciences and education. I examine the goals and challenges of the quantitative techniques and explore how they relate to intersectionality. I conclude by discussing what education researchers can learn from other applied fields that are working to develop a crosswalk across the two disparate, but interconnected, literatures.Educational Leadership and Polic

Linguistic Structures and Economic Outcomes

Linguistic structures have recently started to attract attention from economists as determinants of economic phenomena. This paper provides the first comprehensive review of this nascent literature and its achievements so far. First, we explore the complex connections between language, culture, thought and behaviour. Then, we summarize the empirical evidence on the relationship between linguistic structures and economic and social outcomes. We follow up with a discussion of data, empirical design and identification. The paper concludes by discussing implications for future research and policy