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.

Ozone modifies associations between temperature and cardiovascular mortality: analysis of the NMMAPS data.

Abstract: Objectives: Both ambient ozone and temperature are associated with human health. However, few data are available on whether ozone modifies temperature effects. This study aims to explore whether ozone modified associations between maximum temperature and cardiovascular mortality in the USA. Methods: The authors obtained data from the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) website. They used two time-series Poisson regression models (a response surface model and a stratification model) to examine whether ozone modified associations between maximum temperature and cardiovascular mortality (CVM) in 95 large US communities during 1987–2000 in summer (June to September). Bayesian meta-analysis was used to pool estimates in each community. Results: The response surface model was used to examine the joint effects of temperature and ozone on CVM in summer. Results indicate that ozone positively modified the temperature-CVM associations across the different regions. The stratification model quantified the temperature-CVM associations across different levels of ozone. Results show that in general the higher the ozone concentration, the stronger the temperature-CVM associations across the communities. A 10uC increase in temperature on the same day was associated with an increase in CVM by 1.17% and 8.31% for the lowest and highest level of ozone concentrations in all communities, respectively. Conclusion: Ozone modified temperature effects in different regions in the USA. It is important to evaluate the modifying role of ozone when estimating temperaturerelated health impacts and to further investigate the reasons behind the regional variability and mechanism for the interaction between temperature and ozone.

A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering

Abstract: Clustering has been widely used to partition data into groups so that the degree of association is high among members of the same group and low among members of different groups. Though many effective and efficient clustering algorithms have been developed and deployed, most of them still suffer from the lack of automatic or online decision for optimal number of clusters. In this paper, we define clustering gain as a measure for clustering optimality, which is based on the squared error sum as a clustering algorithm proceeds. When the measure is applied to a hierarchical clustering algorithm, an optimal number of clusters can be found. Our clustering measure shows good performance producing intuitively reasonable clustering configurations in Euclidean space according to the evidence from experimental results. Furthermore, the measure can be utilized to estimate the desired number of clusters for partitional clustering methods as well. Therefore, the clustering gain measure provides a promising technique for achieving a higher level of quality for a wide range of clustering methods.

Contextual Bandit Algorithms with Supervised Learning Guarantees

Abstract: We address the problem of learning in an online, bandit setting where the learner must repeatedly select among $K$ actions, but only receives partial feedback based on its choices. We establish two new facts: First, using a new algorithm called Exp4.P, we show that it is possible to compete with the best in a set of $N$ experts with probability $1-\delta$ while incurring regret at most $O(\sqrt{KT\ln(N/\delta)})$ over $T$ time steps. The new algorithm is tested empirically in a large-scale, real-world dataset. Second, we give a new algorithm called VE that competes with a possibly infinite set of policies of VC-dimension $d$ while incurring regret at most $O(\sqrt{T(d\ln(T) + \ln (1/\delta))})$ with probability $1-\delta$. These guarantees improve on those of all previous algorithms, whether in a stochastic or adversarial environment, and bring us closer to providing supervised learning type guarantees for the contextual bandit setting.

Online aggregation and continuous query support in MapReduce

Abstract: MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, the output of each MapReduce task and job is materialized to disk before it is consumed. In this demonstration, we describe a modified MapReduce architecture that allows data to be pipelined between operators. This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We demonstrate a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see "early returns" from a job as it is being computed. Our Hadoop Online Prototype (HOP) also supports continuous queries, which enable MapReduce programs to be written for applications such as event monitoring and stream processing. HOP retains the fault tolerance properties of Hadoop, and can run unmodified user-defined MapReduce programs.

Sketching probabilistic data streams

Abstract: The management of uncertain, probabilistic data has recently emerged as a useful paradigm for dealing with the inherent unreliabilities of several real-world application domains, including data cleaning, information integration, and pervasive, multi-sensor computing. Unlike conventional data sets, a set of probabilistic tuples defines a probability distribution over an exponential number of possible worlds (i.e., "grounded", deterministic databases). This "possibleworlds" interpretation allows for clean query semantics but also raises hard computational problems for probabilistic database query processors. To further complicate matters, in many scenarios (e.g., large-scale process and environmental monitoring using multiple sensor modalities), probabilistic data tuples arrive and need to be processed in a streaming fashion; that is, using limited memory and CPU resources and without the benefit of multiple passes over a static probabilistic database. Such probabilistic data streams raise a host of new research challenges for stream-processing engines that, to date, remain largely unaddressed. In this paper, we propose the first space- and time-efficient algorithms for approximating complex aggregate queries (including, the number of distinct values and join/self-join sizes) over probabilistic data streams. Following the possible-worlds semantics, such aggregates essentially define probability distributions over the space of possible aggregation results, and our goal is to characterize such distributions through efficient approximations of their key moments (such as expectation and variance). Our algorithms offer strong randomized estimation guarantees while using only sublinear space in the size of the stream(s), and rely on novel, concise streaming sketch synopses that extend conventional sketching ideas to the probabilistic streams setting. Our experimental results verify the effectiveness of our approach.