Tags have recently become popular as a means of annotating and organizing Web pages and blog entries. Advocates of tagging argue that the use of tags produces a 'folksonomy', a system in which the meaning of a tag is determined by its use among the community as a whole. We analyze the effectiveness of tags for classifying blog entries by gathering the top 350 tags from Technorati and measuring the similarity of all articles that share a tag. We find that tags are useful for grouping articles into broad categories, but less effective in indicating the particular content of an article. We then show that automatically extracting words deemed to be highly relevant can produce a more focused categorization of articles. We also show that clustering algorithms can be used to reconstruct a topical hierarchy among tags, and suggest that these approaches may be used to address some of the weaknesses in current tagging systems.

No abstract

Scientific applications, often expressed as workflows are making use of large-scale national cyberinfrastructure to explore the behavior of systems, search for phenomena in large-scale data, and to conduct many other scientific endeavors. As the complexity of the systems being studied grows and as the data set sizes increase, the scale of the computational workflows increases as well. In some cases, workflows now have hundreds of thousands of individual tasks. Managing such scale is difficult from the point of view of workflow description, execution, and analysis. In this paper, we describe the challenges faced by workflow management and performance analysis systems when dealing with an earthquake science application, CyberShake, executing on the TeraGrid. The scientific goal of the SCEC CyberShake project is to calculate probabilistic seismic hazard curves for sites in Southern California. For each site of interest, the CyberShake platform includes two large-scale MPI calculations and approximately 840,000 embarrassingly parallel postprocessing jobs. In this paper, we show how we approach the scalability challenges in our workflow management and log mining systems.

This article describes the process, practice, and challenges of using predictive modelling analytics (LA) predictive modelling has become a core practice of researchers, largely with chapter, we provide a general overview of considerations when using predictive modelling, the steps that an educational data scientist must consider when engaging in the process,

No abstract