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Bartolini C.,University of Ferrara | Bartolini C.,HP Labs Palo Alto | Stefanelli C.,University of Ferrara
Proceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011

Business-driven IT management (BDIM) aims at ensuring successful alignment of business and IT through thorough understanding of the impact of IT on business processes and business results, and vice versa. This thesis reviews the state of the art of BDIM research and advances it by contributing a comprehensive BDIM solution for the process of IT incident management. The solution can be used as a template for applying the BDIM methodology to other IT service management processes. The work presented in this dissertation resulted in three patent applications and is at the core of the HP IT Analytic™ product (formerly HP D ecisionCenter™). This thesis was defended on March 2009. © 2011 IEEE. Source

Wagner C.,University Koblenz | Strohmaier M.,University Koblenz | Singer P.,Graz University of Technology | Huberman B.,HP Labs Palo Alto
HP Laboratories Technical Report

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, images, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources in social tagging systems may become semantically stable over time as more and more users tag them and implicitly agree on the relative importance of tags for a resource. At the same time, previous work has raised an array of new questions such as: (i) How can we assess semantic stability in a robust and methodical way? (ii) Does the semantic stabilization varies across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization in different social tagging systems with distinct domains and properties and (iii) detecting potential causes of stabilization and implicit consensus, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language phenomena alone. Source

Wu S.,HP Labs Palo Alto | Raschid L.,University of Maryland University College
WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining

Microblogs such as Twitter support a rich variety of user interactions using hashtags, urls, retweets and mentions. Microblogs are an exemplar of a hybrid network; there is an explicit network of followers, as well as an implicit network of users who retweet other users, and users who mention other users. These networks are important proxies for influence. In this paper, we develop a comprehensive behavioral model of an individual user and her interactions in the hybrid network. We choose a focal user and predict those users who will be influenced by her, and will retweet and/or mention the focal user, in the near future. We define a potential function, based on a hybrid network, which reflects the likelihood of a candidate user being influenced by, and having a specific type of link to, a focal user, in the future. We show that the potential function based prediction model converges to the Bonacich centrality metric. We develop a fast unsupervised solution which approximates the future hybrid network and the future Bonacich potential. We perform an extensive evaluation over a microblog network and a stream of tweets from Twitter. Our solution outperforms several baseline methods including ones based on singular value decomposition (SVD) and a supervised Ranking SVM. © 2014 ACM. Source

Lukowicz P.,University of Passau | Baker M.G.,HP Labs Palo Alto | Paradiso J.,Massachusetts Institute of Technology
IEEE Pervasive Computing

Pervasive computing technology can save lives by both eliminating the need for humans to work in hostile environments and supporting them when they do. In general, environments that are hazardous to humans are hard on technology as well. This issue contains three articles and a Spotlight column that illustrate the challenges of designing this technology and implementing it in hostile environments. © 2010 IEEE. Source

Fan J.,HP Labs Palo Alto | Luo P.,HP Labs China | Lim S.H.,HP Labs Palo Alto | Liu S.,HP Labs Palo Alto | And 2 more authors.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Many people use the Web as the main source of information in their daily lives. However, most web pages contain non-informative components such as side bars, footers, headers, and advertisements, which are undesirable for certain applications like printing. We demonstrate a system that automatically extracts the informative contents from news- and blog-like web pages. In contrast to many existing methods that are limited to identifying only the text or the bounding rectangular region, our system not only identifies the content but also the structural roles of various content components such as title, paragraphs, images and captions. The structural information enables re-layout of the content in a pleasing way. Besides the article text extraction, our system includes the following components: 1) print-link detection to identify the URL link for printing, and to use it for more reliable analysis and recognition; 2) title detection incorporating both visual cues and HTML tags; 3) image and caption detection utilizing extensive visual cues; 4) multiple-page and next page URL detection. The performance of our system has been thoroughly evaluated using a human labeled ground truth dataset consisting of 2000 web pages from 100 major web sites. We show accurate results using such a dataset. Copyright 2011 ACM. Source

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