Data Mining Practical Machine Learning Tools and Techniques

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing.We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. We have classified the trending topics based on the active period and the tweets and show that the majority (over 85%) of topics are headline news or persistent news in nature. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1,000 users no matter what the number of followers is of the original tweet. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet.To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7104&context=sis_research

What is Twitter, a social network or a news media?

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

/pdf/linked-data-the-story-so-far-2a9zvmaysa.pdf

Linked Data - the story so far

Obesity now affects one in five children in the United States. Discrimination against overweight children begins early in childhood and becomes progressively institutionalized. Because obese children tend to be taller than their nonoverweight peers, they are apt to be viewed as more mature. The inappropriate expectations that result may have an adverse effect on their socialization. Many of the cardiovascular consequences that characterize adult-onset obesity are preceded by abnormalities that begin in childhood. Hyperlipidemia, hypertension, and abnormal glucose tolerance occur with increased frequency in obese children and adolescents. The relationship of cardiovascular risk factors to visceral fat independent of total body fat remains unclear. Sleep apnea, pseudotumor cerebri, and Blount's disease represent major sources of morbidity for which rapid and sustained weight reduction is essential. Although several periods of increased risk appear in childhood, it is not clear whether obesity with onset early in childhood carries a greater risk of adult morbidity and mortality. Obesity is now the most prevalent nutritional disease of children and adolescents in the United States. Although obesity-associated morbidities occur more frequently in adults, significant consequences of obesity as well as the antecedents of adult disease occur in obese children and adolescents. In this review, I consider the adverse effects of obesity in children and adolescents and attempt to outline areas for future research. I refer to obesity as a body mass index greater than the 95th percentile for children of the same age and gender.

Health Consequences of Obesity in Youth: Childhood Predictors of Adult Disease

Background: Prostate cancer is one of the most common types of cancer with a high mortality rate. The current study was conducted to investigate the relationship between dietary patterns and prostate cancer risk among Iranian men. Methods: This case-control study was conducted in Kermanshah province in western Iran in November 2016. Fifty patients with prostate cancer were selected as cases and 150 healthy men matched for age and body mass index (BMI) were selected as controls. Dietary intake data were collected by a semi-quantitative food frequency questionnaire (FFQ). Food items were grouped according to the similarity of nutrient profiles. The main dietary patterns were identified by factor analysis. Results: After adjustment for potential confounders, a healthy dietary pattern was associated with decreased risk of prostate cancer (highest versus lowest tertile OR:0.24; 95% CI: 0.07-0.81;trend p: 0.025). An unhealthy dietary pattern was related to increased risk of prostate cancer(highest versus lowest tertile OR:3.4; 95% CI: 1.09-10.32; trend p: 0.037). Conclusion: This study shows that an unhealthy dietary pattern was associated with increased risk of prostate cancer. However, a healthy dietary pattern was associated with decreased risk of prostate cancer.

https://hpp.tbzmed.ac.ir/PDF/hpp-8-133.pdf

Dietary patterns and risk of prostate cancer: a factor analysis study in a sample of Iranian men.

The Internet Archive’s (IA) Wayback Machine is the largest and oldest public web archive and has become a significant repository of our recent history and cultural heritage. Despite its importance, there has been little research about how it is discovered and used. Based on web access logs, we analyze what users are looking for, why they come to IA, where they come from, and how pages link to IA. We find that users request English pages the most, followed by the European languages. Most human users come to web archives because they do not find the requested pages on the live web. About 65% of the requested archived pages no longer exist on the live web. We find that more than 82% of human sessions connect to the Wayback Machine via referrals from other web sites, while only 15% of robots have referrers. Most of the links (86%) from websites are to individual archived pages at specific points in time, and of those 83% no longer exist on the live web.

Who and What Links to the Internet Archive

We use the ServiceWorker (SW) API to intercept HTTP requests for embedded resources and reconstruct Composite Mementos without the need for conventional URL rewriting typically performed by web archives. URL rewriting is a problem for archival replay systems, especially for URLs constructed by JavaScript, that frequently results in incorrect URI references. By intercepting requests on the client using SW, we are able to strategically reroute instead of rewrite. Our implementation moves rewriting to clients, saving servers' computing resources and allowing servers to return responses more quickly. In our experiments, retrieving the original instead of rewritten pages from the archive resulted in a one- third reduction in time overhead and a one-fifth reduction in data overhead. Our system, reconstructive.js, prevents the live web from leaking into Composite Mementos while being easy to distribute and maintain.

Client-side reconstruction of composite mementos using serviceworker

We present the Smart Object, Dumb Archive (SODA) model for digital libraries (DLs). The SODA model transfers functionality traditionally associated with archives to the archived objects themselves. We are exploiting this shift of responsibility to facilitate other DL goals, such as interoperability, object intelligence and mobility, and heterogeneity. Objects in a SODA DL negotiate presentation of content and handle their own terms and conditions. In this paper we present implementations of our smart objects, buckets, and our dumb archive (DA). We discuss the status of buckets and DA and how they are used in a variety of DL projects.

/pdf/soda-smart-objects-dumb-archives-53wcvows5m.pdf

SODA: Smart Objects, Dumb Archives

We show that state-of-the-art services for creating trusted timestamps in blockchain-based networks do not adequately allow for timestamping of web pages. They accept data by value (e.g., images and text), but not by reference (e.g., URIs of web pages). Also, we discuss difficulties in repeatedly generating the same cryptographic hash value of an archived web page. We then introduce several requirements to be fulfilled in order to produce repeatable hash values for archived web pages.

Michael L. Nelson

Papers

Dietary patterns and risk of prostate cancer: a factor analysis study in a sample of Iranian men.

Who and What Links to the Internet Archive

Client-side reconstruction of composite mementos using serviceworker

SODA: Smart Objects, Dumb Archives

Difficulties of Timestamping Archived Web Pages.