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These include optimizing internal systems such as scheduling the machines that power the numerous computations done each day, as well as optimizations that affect core products and users, from online allocation of ads to page-views to automatic management of ad campaigns, and from clustering large-scale graphs to finding best paths Search for research papers transportation networks.

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Other than employing new algorithmic ideas to impact millions of users, Google researchers contribute to the state-of-the-art research in these areas by publishing in top conferences and journals.

We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search e.

The overarching goal is to create a plethora of structured data on the Web that maximally help Google users consume, interact and explore information. Through those projects, we study various cutting-edge data management research issues including information extraction and integration, large scale data analysis, effective data exploration, etc.

A major research effort involves the management of structured data within the enterprise. The goal is to discover, index, monitor, and organize this type of data in order to make it easier to access high-quality datasets.

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This type of data carries different, and often richer, semantics than structured data on the Web, which in turn raises new opportunities and technical challenges in their management. Furthermore, Data Management research across Google allows us to build technologies that power Google's largest businesses through scalable, reliable, fast, and general-purpose infrastructure for large-scale data processing as a service.

Some examples of such technologies include F1the database serving our ads infrastructure; Mesaa petabyte-scale analytic data warehousing system; and Dremelfor petabyte-scale data processing with interactive response times.

However, questions in practice are rarely so clean as to just to use an out-of-the-box algorithm.

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A big challenge is in developing metrics, designing experimental methodologies, and modeling the space to create parsimonious representations that capture the fundamentals of the problem. Data mining lies at the heart of many of these questions, and the research done at Google is at the forefront of the field.

Whether it is finding more efficient algorithms for working with massive data sets, developing privacy-preserving methods for classification, or designing new machine learning approaches, our group continues to push the boundary of what is possible.

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Sometimes this is motivated by the need to collect data from widely dispersed locations e. Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.

We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our hybrid research model.

Not surprisingly, it devotes considerable attention to research in this area. Topics include 1 auction design, 2 advertising effectiveness, 3 statistical methods, 4 forecasting and prediction, 5 survey research, 6 policy analysis and a host of other topics. This research involves interdisciplinary collaboration among computer scientists, economists, statisticians, and analytic marketing researchers both at Google and academic institutions around the world.

A major challenge is in solving these problems at very large scales. For example, the advertising market has billions of transactions daily, spread across millions of advertisers.

It presents a unique opportunity to test and refine economic principles as applied to a very large number of interacting, self-interested parties with a myriad of objectives. It is remarkable how some of the fundamental problems Google grapples with are also some of the hardest research problems in the academic community.

At Google, this research translates direction into practice, influencing how production systems are designed and used. Many scientific endeavors can benefit from large scale experimentation, data gathering, and machine learning including deep learning.Total References: Total number of references to other papers that have been resolved to date, for papers in the SSRN eLibrary.

Total Citations: Total number of cites to papers in the SSRN eLibrary whose links have been resolved to date.

Total Footnotes: Total number of footnotes resolved in the SSRN eLibrary. Search engines do not necessarily contain the full text of the paper for you to read.

Search for research papers

A few, like PubMed, do provide links to free online versions of the paper, when . Jan 18,  · Superb Paper – custom writing service & a free catalogue of essays and research paper samples Google Scholar – is a meta-search engine returning only reliable search results that can be cited in term papers (books, scholarly articles, educational sites pages).

Find + million publication pages, 15+ million researchers, and k+ projects. ResearchGate is where you discover scientific knowledge and share your work. Enter an author name or pertinent keywords you would like to search for. It is a search tool that finds scholarly articles–academic journals, patents, theses, court proceedings, and more.

Google Scholar displays how many times an academic piece of literature was cited, which is a rough numerical indicator of how influential the research was.

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