Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

/pdf/ontology-mapping-the-state-of-the-art-91mok9nz44.pdf

Ontology mapping: the state of the art

Recent technological developments have given rise to blended learning classrooms. An inverted (or flipped) classroom is a specific type of blended learning design that uses technology to move lectures outside the classroom and uses learning activities to move practice with concepts inside the classroom. This article compares the learning environments of an inverted introductory statistics class with a traditional introductory statistics class at the same university. This mixed-methods research study used the College and University Classroom Environment Inventory (CUCEI), field notes, interviews and focus groups to investigate the learning environments of these two classrooms. Students in the inverted classroom were less satisfied with how the classroom structure oriented them to the learning tasks in the course, but they became more open to cooperative learning and innovative teaching methods. These findings are discussed in terms of how they contribute to the stability and connectedness of classroom learning communities.

/pdf/how-learning-in-an-inverted-classroom-influences-cooperation-5g9bu8q0ec.pdf

How Learning in an Inverted Classroom Influences Cooperation, Innovation and Task Orientation.

As a prolific research area in data mining, subspace clustering and related problems induced a vast quantity of proposed solutions. However, many publications compare a new proposition—if at all—with one or two competitors, or even with a so-called “naive” ad hoc solution, but fail to clarify the exact problem definition. As a consequence, even if two solutions are thoroughly compared experimentally, it will often remain unclear whether both solutions tackle the same problem or, if they do, whether they agree in certain tacit assumptions and how such assumptions may influence the outcome of an algorithm. In this survey, we try to clarify: (i) the different problem definitions related to subspace clustering in general; (ii) the specific difficulties encountered in this field of research; (iii) the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and (iv) how several prominent solutions tackle different problems.

Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering

Ontology mapping is seen as a solution provider in today's landscape of ontology
research. As the number of ontologies that are made publicly available and
accessible on the Web increases steadily, so does the need for applications to use
them. A single ontology is no longer enough to support the tasks envisaged by a 
distributed environment like the Semantic Web. Multiple ontologies need to be 
accessed from several applications. Mapping could provide a common layer from which
several ontologies could be accessed and hence could exchange information in 
semantically sound manners. Developing such mapping has beeb the focus of a variety
of works originating from diverse communities over a number of years. In this
article we comprehensively review and present these works. We also provide insights
on the pragmatics of ontology mapping and elaborate on a theoretical approach for
defining ontology mapping.

https://drops.dagstuhl.de/opus/volltexte/2005/40/pdf/04391.KalfoglouYannis.Paper.40.pdf/

Ontology Mapping: The State of the Art

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasis on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

Peggy Cellier

Papers

Formal Concept Analysis