Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Filtering: a method for solving graph problems in MapReduce

Abstract: The MapReduce framework is currently the de facto standard used throughout both industry and academia for petabyte scale data analysis. As the input to a typical MapReduce computation is large, one of the key requirements of the framework is that the input cannot be stored on a single machine and must be processed in parallel. In this paper we describe a general algorithmic design technique in the MapReduce framework called filtering. The main idea behind filtering is to reduce the size of the input in a distributed fashion so that the resulting, much smaller, problem instance can be solved on a single machine. Using this approach we give new algorithms in the MapReduce framework for a variety of fundamental graph problems for sufficiently dense graphs. Specifically, we present algorithms for minimum spanning trees, maximal matchings, approximate weighted matchings, approximate vertex and edge covers and minimum cuts. In all of these cases, we parameterize our algorithms by the amount of memory available on the machines allowing us to show tradeoffs between the memory available and the number of MapReduce rounds. For each setting we will show that even if the machines are only given substantially sublinear memory, our algorithms run in a constant number of MapReduce rounds. To demonstrate the practical viability of our algorithms we implement the maximal matching algorithm that lies at the core of our analysis and show that it achieves a significant speedup over the sequential version.

Generating summaries and visualization for large collections of geo-referenced photographs

Abstract: We describe a framework for automatically selecting a summary set of photos from a large collection of geo-referenced photographs. Such large collections are inherently difficult to browse, and become excessively so as they grow in size, making summaries an important tool in rendering these collections accessible. Our summary algorithm is based on spa-tial patterns in photo sets, as well as textual-topical patterns and user (photographer) identity cues. The algorithm can be expanded to support social, temporal, and other factors. The summary can thus be biased by the content of the query, the user making the query, and the context in which the query is made.A modified version of our summarization algorithm serves as a basis for a new map-based visualization of large collections of geo-referenced photos, called Tag Maps. Tag Maps visualize the data by placing highly representative textual tags on relevant map locations in the viewed region, effectively providing a sense of the important concepts embodied in the collection.An initial evaluation of our implementation on a set of geo-referenced photos shows that our algorithm and visualization perform well, producing summaries and views that are highly rated by users.

Proceedings of the 17th ACM conference on Information and knowledge management

Abstract: It is our great pleasure to welcome you to the 17th ACM Conference on Information and Knowledge Management -- CIKM'08. Since 1992, the ACM Conference on Information and Knowledge Management (CIKM) has been successfully bringing together leading researchers and developers from the database, information retrieval, and knowledge management communities. The purpose of the conference is to identify challenging problems facing the development of future knowledge and information systems, and to shape future research directions through the publication of high quality, applied and theoretical research findings. In CIKM 2008, we continued the tradition of promoting collaboration among the general areas of databases, information retrieval, and knowledge management. This year's call for papers attracted almost 800 submissions from Asia, Canada, Europe, Africa, and the United States. The program committee accepted 132 papers and 103 posters giving CIKM'08 an acceptance rate of 17%.

Green synthesis of silver nanoparticles through reduction with Solanum xanthocarpum L. berry extract: characterization, antimicrobial and urease inhibitory activities against Helicobacter pylori

Abstract: A green synthesis route for the production of silver nanoparticles using methanol extract from Solanum xanthocarpum berry (SXE) is reported in the present investigation. Silver nanoparticles (AgNps), having a surface plasmon resonance (SPR) band centered at 406 nm, were synthesized by reacting SXE (as capping as well as reducing agent) with AgNO3 during a 25 min process at 45 °C. The synthesized AgNps were characterized using UV–Visible spectrophotometry, powdered X-ray diffraction, and transmission electron microscopy (TEM). The results showed that the time of reaction, temperature and volume ratio of SXE to AgNO3 could accelerate the reduction rate of Ag+ and affect the AgNps size and shape. The nanoparticles were found to be about 10 nm in size, mono-dispersed in nature, and spherical in shape. In vitro anti-Helicobacter pylori activity of synthesized AgNps was tested against 34 clinical isolates and two reference strains of Helicobacter pylori by the agar dilution method and compared with AgNO3 and four standard drugs, namely amoxicillin (AMX), clarithromycin (CLA), metronidazole (MNZ) and tetracycline (TET), being used in anti-H. pylori therapy. Typical AgNps sample (S1) effectively inhibited the growth of H. pylori, indicating a stronger anti-H. pylori activity than that of AgNO3 or MNZ, being almost equally potent to TET and less potent than AMX and CLA. AgNps under study were found to be equally efficient against the antibiotic-resistant and antibiotic-susceptible strains of H. pylori. Besides, in the H. pylori urease inhibitory assay, S1 also exhibited a significant inhibition. Lineweaver-Burk plots revealed that the mechanism of inhibition was noncompetitive.

Efficient algorithms for ranking with SVMs

Abstract: RankSVM (Herbrich et al. in Advances in large margin classifiers. MIT Press, Cambridge, MA, 2000; Joachims in Proceedings of the ACM conference on knowledge discovery and data mining (KDD), 2002) is a pairwise method for designing ranking models. SVMLight is the only publicly available software for RankSVM. It is slow and, due to incomplete training with it, previous evaluations show RankSVM to have inferior ranking performance. We propose new methods based on primal Newton method to speed up RankSVM training and show that they are 5 orders of magnitude faster than SVMLight. Evaluation on the Letor benchmark datasets after complete training using such methods shows that the performance of RankSVM is excellent.