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Groups Overview We conduct research in the area of algorithms and systems for processing massive amounts of data. Research Areas Database queries — How can we efficiently resolve database queries on massive amounts of input data? Here the input data may be presented in the form of a distributed data stream.
Machine learning — How can we efficiently solve large-scale machine learning problems? Here the input data may be massive, stored in a distributed cluster of machines. Distributed computing — How can we efficiently solve large-scale optimization problems in distributed computing environments?
For example, how can we efficiently solve large-scale combinatorial problems, e. In dealing with big data, we often need to look at a small summary to get the big picture. Over recent years, many new techniques have been developed which allow important properties of large distributions to be extracted from compact and easy-to-build summaries.
In this talk, we will survey some algorithmic results for several fundamental statistical inference tasks.
The algorithms are given access only to i. The main focus of this research is the sample complexity of each task as a function of the domain size for the underlying discrete probability distributions. The inference tasks studied include i similarity to a fixed distribution i.
For Research paper on big data of these tasks, an algorithm with sublinear sample complexity is presented e. Given certain extra information on the distributions such as the distribution is monotone or unimodal with respect to a fixed total order on the domainthe sample complexity of these tasks become polylogarithmic in the domain size.
We present a fully online randomized algorithm for the classical pattern matching problem that uses merely O log m space, breaking the O m barrier that held for this problem for a long time. Our method can be used as a tool in many practical applications, including monitoring Internet traffic and firewall applications.
In our online model we first receive the pattern P of size m and preprocess it. After the preprocessing phase, the characters of the text T of size n arrive one at a time in an online fashion.
For each index of the text input we indicate whether the pattern matches the text at that location index or not.
Our goal is to provide such answers while using minimal space, and while spending as little time as possible on each character time and space which are in O poly log n.
The focus will be on approaches that lend themselves to thorough mathematical analysis but that, due to their simplicity and general easiness on assumptions, may be considered to be good, all-purpose performers.
The talk will mention theoretical results and occasionally hint at proof strategies but most parts will be accessible to a general audience. The talk will start by a brief introduction on semidefinite programming.
It will discuss some recent advances in large scale semidefinite programming solvers, detailing in particular stochastic smoothing techniques for the maximum eigenvalue function. Joint work with Noureddine El Karoui at U. Online learning has become a standard tool in machine learning and large-scale data analysis.
Learning is viewed as a repeated game between an adaptive agent and an ever-changing environment. Within this simple paradigm, one can model a variety of sequential decision tasks simply by specifying the interaction protocol and the resource constraints on the agent.
In the talk we will first highlight some of the main features of online learning algorithms, such as simplicity, locality, scalability, and robustness. Then, we will describe algorithmic applications to specific learning scenarios partial feedback, attribute-efficient, multitask, semi-supervised, transductive, and more showing how diverse settings can be effectively captured within the same conceptual framework.
A prime driver for much database research over the past decade has been providing unified structured relational query interfaces on top of web-based datasources.
There are a range of issues that come up in doing this I will talk try to give an idea of a few of them, focusing on several we have worked on at Oxford:Jul 14, · What is a good research topic related to big data for my final year project?
How can I select a research topic after I have selected Big Data as a research field? What is the best topic for research in big data?
Read the latest articles of Big Data Research at rutadeltambor.com, Elsevier’s leading platform of peer-reviewed scholarly literature.
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research.
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