6455989
Winners don't take all: Characterizing the competition for links on the web
As a whole, the World Wide Web displays a striking “rich get richer” behavior, with a relatively small number of sites receiving a disproportionately large share of hyperlink references and traffic. However, hidden in this skewed global distribution, we discover a qualitatively different and considerably less biased link distribution among subcategories of pages—for example, among all university homepages or all newspaper homepages. Although the connectivity distribution over the entire web is close to a pure power law, we find that the distribution within specific categories is typically unimodal on a log scale, with the location of the mode, and thus the extent of the rich get richer phenomenon, varying across different categories. Similar distributions occur in many other naturally occurring networks, including research paper citations, movie actor collaborations, and United States power grid connections. A simple generative model, incorporating a mixture of preferential and uniform attachment, quantifies the degree to which the rich nodes grow richer, and how new (and poorly connected) nodes can compete. The model accurately accounts for the true connectivity distributions of category-specific web pages, the web as a whole, and other social networks.
5994026
Extraction and search of chemical formulae in text documents on the web
Often scientists seek to search for articles on the Web related to a particular chemical. When a scientist searches for a chemical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching results in two problems for this domain: a) if the author searches for CH4 and the article has H4C, the article is not returned, and b) ambiguous searches like "He" return all documents where Helium is mentioned as well as documents where the pronoun "he" occurs. To remedy these deficiencies, we propose a chemical formula search engine. To build a chemical formula search engine, we must solve the following problems: 1) extract chemical formulae from text documents, 2) index chemical formulae, and 3) designranking functions for the chemical formulae. Furthermore, query models are introduced for formula search, and for each a scoring scheme based on features of partial formulae is proposed tomeasure the relevance of chemical formulae and queries. We evaluate algorithms for identifying chemical formulae in documents using classification methods based on Support Vector Machines(SVM), and a probabilistic model based on conditional random fields (CRF). Different methods for SVM and CRF to tune the trade-off between recall and precision forim balanced data are proposed to improve the overall performance. A feature selection method based on frequency and discrimination isused to remove uninformative and redundant features. Experiments show that our approaches to chemical formula extraction work well, especially after trade-off tuning. The results also demonstrate that feature selection can reduce the index size without changing ranked query results much.
14757404
Intelligent Methods for File System Optimization
The speed of I/O components is it major limitation of the speed of all other major components in today's computer systems. Motivated by this, we investigated several algorithms for efficient and intelligent organization of files on a hard disk. Total access time may be decreased if files with temporal locality also have spatial locality. Three intelligent methods based on file type, frequency, and transition probabilities information showed up to 60% savings of total I/O time over the naive placement of files. More computationally intensive hill climbing and genetic algorithms approaches did not outperform statistical methods. The experiments were run on a real and simulated hard drive in single and multiple user environments.