Steinfield, Charles, Joan M. Social Networking and Social Capital within the Organization. Proceedings of the fourth international conference on Communities and technologies. Social capital, self-esteem, and use of online social network sites:
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 in transportation networks.
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. 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.
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.
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.
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. We collaborate closely with world-class research partners to help solve important problems with large scientific or humanitarian benefit. The smallest part is your smartphone, a machine that is over ten times faster than the iconic Cray-1 supercomputer.
The capabilities of these remarkable mobile devices are amplified by orders of magnitude through their connection to Web services running on building-sized computing systems that we call Warehouse-scale computers WSCs. The tight collaboration among software, hardware, mechanical, electrical, environmental, thermal and civil engineers result in some of the most impressive and efficient computers in the world.
We declare success only when we positively impact our users and user communities, often through new and improved Google products. We are engaged in a variety of HCI disciplines such as predictive and intelligent user interface technologies and software, mobile and ubiquitous computing, social and collaborative computing, interactive visualization and visual analytics.
Many projects heavily incorporate machine learning with HCI, and current projects include predictive user interfaces; recommenders for content, apps, and activities; smart input and prediction of text on mobile devices; user engagement analytics; user interface development tools; and interactive visualization of complex data.ACM/IEEE ANCS is the premier forum for presenting and discussing original research that explores the relationship between the algorithms and architectures of data communication networks and the hardware and software elements from which these networks are built, including both experimental and theoretical analysis.
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