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- Leader election in synchronous networks - Semantic Scholar
- Leader election in synchronous networks
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Leader election in synchronous networks - Semantic Scholar
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Book details Author : Nancy A. Synopsis book In Distributed Algorithms , Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts.
However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services.
Leader election in synchronous networks
Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users.
This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media.
Mastering Elastic Stack. Yuvraj Gupta.
Get the most out of the Elastic Stack for various complex analytics using this comprehensive and practical guideAbout This BookYour one-stop solution to perform advanced analytics with Elasticsearch, Logstash, and KibanaLearn how to make better sense of your data by searching, analyzing, and logging data in a systematic wayThis highly practical guide takes you through an advanced implementation on the ELK stack in your enterprise environmentWho This Book Is For This book cater to developers using the Elastic stack in their day-to-day work who are familiar with the basics of Elasticsearch, Logstash, and Kibana, and now want to become an expert at using the Elastic stack for data analytics.
In Detail Even structured data is useless if it can't help you to take strategic decisions and improve existing system. Style and approach This practical guide shows you how to perform advanced analytics with the Elastic stack through real-world use cases. Similar ebooks. Chi Wang. This book explores U. Part II chronologically details the major events in U. Finally, the book ends with timely suggestions for how to improve the U.
Probabilistic Approaches to Recommendations. Nicola Barbieri. The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products.
Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data.
These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available.
Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy.
We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.
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In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to "check in" at a real-world POI point of interest, e.
The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks LBSNs. Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before.
This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility.