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That means when NVM finally arrives, just like when you finally passed that kidney stone after three weeks, everyone will be relieved but the transition will be painful. Many of the components of legacy DBMSs will become unnecessary and will degrade the performance of data intensive applications.
I will conclude with a discussion of a new DBMS that we have been developing at Carnegie Mellon that specifically designed to leverage the persistence properties of NVM in its architecture, such as its recovery and concurrency control mechanisms. Our system is able to achieve higher throughput than existing approaches while reducing the amount of wear due to write operations on the device. Adaptivity addresses a class of problems related to database physical design optimization for scenarios where the workload is unknown and immediate availability is a requirement.
The general strategy is to improve physical design by means of incremental changes, each guided by the current workload request.
For instance, adaptive indexing builds partial in- dexes through steps during query processing rather than building full indexes.
Adaptivity can also be applied on data storage layout in order to optimize relevant data exchange between memory hierarchy layers. Instead of having a fixed layout, adaptive storage re- designs data organization to answer incoming queries incrementally based on the current requests or recent workload pattern. Yvson P. O que nos reserva o futuro? Social professional networks provide features not available in other networks. For example, LinkedIn facilitate professional networking, and GitHub enables committing and sharing code.
Such networks also provide data on users, behaviors and interactions. Here, we foster a deeper understanding of the social professional networks types, definitions, features, analyses and applications while providing a useful taxonomy about their use. We also study the strength of ties, a central aspect that allows studying the roles of relationships. Therefore, besides analyzing the strength of co-authorship ties, we also present a set of metrics and algorithms to measure such strength in different contexts.
Mestrado For over 40 years, organization studies have examined human factors in physical workplaces and their influence on the ability of an individual to perform a task, or a set of tasks, alone or in collaboration with others. In a virtual marketplace, the crowd is typically volatile, its arrival and departure asynchronous, and its levels of attention and accuracy diverse.
I will review such work and argue that we need a holistic view to take full advantage of human factors such as skills, expected wage and motivation, in improving the performance of a crowdsourcing platform.
This tutorial will discuss mobile crowdsourcing issues for smart city applications. In particular, it will motivate the use of incentives for better crowd participation from people carrying mobile devices, data quality, data analytics and trust issues among others. It will also discuss some open research problems in that domain.
Doutorado Sentiment analysis is an ongoing field of research in text mining that deals mainly with the task of identifying the polarity positive, negative or neutral expressed in a piece of text.
Applications of sentiment analysis include the monitoring of reviews or opinions about a company, product or a brand; political analyses, such as the tracking of sentiments expressed by voters about candidates; among many others. Data is structured as a network. And now? How to analyze it? Extracting knowledge from network data is not a simple task and requires the use of appropriate tools and techniques, especially in scenarios that take into account the volume and evolving aspects of the network.
There is a vast literature on how to collect, process and model social media data in the form of networks, as well as key metrics of centrality.
However, there is still much to be discussed in relation to the analysis of the underlying network. In this short course we consider that data has already been collected and is already structured as a network.
The goal is to discuss techniques to analyze these network data, especially considering the time perspective. First, concepts related to problem definition, temporal networks and metrics for network analysis will be presented.
Next, in a more practical aspect will be shown techniques of visualization and processing of temporal networks.
In the end, three case studies with real data from music playlists, Twitter and phone calls will be discussed, illustrating how to extract knowledge from temporal social networks. Deep learning, as a subfield of machine learning, uses the strategy of creating models by stacking representation layers whose parameters are learned using known data.
The central idea of this type of technique is not new, but it is recent the hype surrounding the field, caused by impressive results in particular with perception-related tasks, which were historically seen to be very difficult to be tackled by computers. Although seemingly complex methods, those are composed of simple computing elements that perform basically a chain of linear transformations, mapping subsequent vector spaces.
From an algebraic formulation, this short-course presents how deep learning works from its basic components to the algorithms used to achieve learning. As case-studies the problems of classification and feature learning are presented in supervised and non-supervised scenarios using convolutional networks and auto-encoders.
The objective is to provide understanding of the inner workings of such models and what makes them different from non-deep models, their theoretical advantages and limitations, as well as practical instructions for applications. Slides — Material. Elvismary M. Ou seja, um contexto de pesquisas similar ao que se costuma definir por Big Data. Slides In the last decades, researchers have been developing different techniques to understand which factors influence the sporting results and, consequently, the role of predictability and randomness in the games.
Interactive and Transmedia Documentary: Production, Interface, Content and Representation. Laboratory Journalism. Information Quality and Trust: From Traditional Media to Cybermedia. Breiner, Janine C. Context, Investigation and Condemnation: Digital Infographics: The Key to Information Paradigm.
Transparency, Online Information and Pre-election Surveys. Generation Co. Corporate Communication, Marketing, and Video Games.
Travel Igers: Banking Industry Innovation. Social Networks as a New University Venue. About this book Introduction This book explores the disruptive changes in the media ecosystem caused by convergence and digitization, and analyses innovation processes in content production, distribution and commercialisation.