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Distributed Hash Table

Author: Hao Zhang
Publisher: Springer Science & Business Media
ISBN: 1461490081
Size: 60.14 MB
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This SpringerBrief summarizes the development of Distributed Hash Table in both academic and industrial fields. It covers the main theory, platforms and applications of this key part in distributed systems and applications, especially in large-scale distributed environments. The authors teach the principles of several popular DHT platforms that can solve practical problems such as load balance, multiple replicas, consistency and latency. They also propose DHT-based applications including multicast, anycast, distributed file systems, search, storage, content delivery network, file sharing and communication. These platforms and applications are used in both academic and commercials fields, making Distributed Hash Table a valuable resource for researchers and industry professionals.

Database And Expert Systems Applications

Author: Qiming Chen
Publisher: Springer
ISBN: 3319228528
Size: 30.69 MB
Format: PDF, Docs
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This two volume set LNCS 9261 and LNCS 9262 constitutes the refereed proceedings of the 26th International Conference on Database and Expert Systems Applications, DEXA 2015, held in Valencia, Spain, September 1-4, 2015. The 40 revised full papers presented together with 32 short papers, and 2 keynote talks, were carefully reviewed and selected from 125 submissions. The papers discuss a range of topics including: temporal, spatial and high dimensional databases; semantic Web and ontologies; modeling, linked open data; NoSQLm NewSQL, data integration; uncertain data and inconsistency tolerance; database system architecture; data mining, query processing and optimization; indexing and decision support systems; modeling, extraction, social networks; knowledge management and consistency; mobility, privacy and security; data streams, Web services; distributed, parallel and cloud databases; information retrieval; XML and semi-structured data; data partitioning, indexing; data mining, applications; WWW and databases; data management algorithms. These volumes also include accepted papers of the 8th International Conference on Data Management in Cloud, Grid and P2P Systems, Globe 2015, held in Valencia, Spain, September 2, 2015. The 8 full papers presented were carefully reviewed and selected from 13 submissions. The papers discuss a range of topics including: MapReduce framework: load balancing, optimization and classification; security, data privacy and consistency; query rewriting and streaming.

Basics Of Computer Networking

Author: Thomas Robertazzi
Publisher: Springer Science & Business Media
ISBN: 9781461421047
Size: 50.15 MB
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Springer Brief Basics of Computer Networking provides a non-mathematical introduction to the world of networks. This book covers both technology for wired and wireless networks. Coverage includes transmission media, local area networks, wide area networks, and network security. Written in a very accessible style for the interested layman by the author of a widely used textbook with many years of experience explaining concepts to the beginner.

Distributed Computing

Author: Ajay D. Kshemkalyani
Publisher: Cambridge University Press
ISBN: 9781139470315
Size: 51.67 MB
Format: PDF
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Designing distributed computing systems is a complex process requiring a solid understanding of the design problems and the theoretical and practical aspects of their solutions. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science. Practitioners in data networking and sensor networks will also find this a valuable resource. Additional resources are available online at www.cambridge.org/9780521876346.

Data Mining Techniques In Sensor Networks

Author: Annalisa Appice
Publisher: Springer Science & Business Media
ISBN: 1447154541
Size: 48.97 MB
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Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data. This work introduces a recently defined spatio-temporal pattern, called trend cluster, to summarize, interpolate and identify anomalies in a sensor network. As an example, the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants is discussed. The work closes with remarks on new possibilities for surveillance enabled by recent developments in sensing technology.

Visualizing The Data City

Author: Paolo Ciuccarelli
Publisher: Springer Science & Business Media
ISBN: 3319021958
Size: 54.36 MB
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This book investigates novel methods and technologies for the collection, analysis and representation of real-time user-generated data at the urban scale in order to explore potential scenarios for more participatory design, planning and management processes. For this purpose, the authors present a set of experiments conducted in collaboration with urban stakeholders at various levels (including citizens, city administrators, urban planners, local industries and NGOs) in Milan and New York in 2012. It is examined whether geo-tagged and user-generated content can be of value in the creation of meaningful, real-time indicators of urban quality, as it is perceived and communicated by the citizens. The meanings that people attach to places are also explored to discover what such an urban semantic layer looks like and how it unfolds over time. As a conclusion, recommendations are proposed for the exploitation of user-generated content in order to answer hitherto unsolved urban questions. Readers will find in this book a fascinating exploration of techniques for mining the social web that can be applied to procure user-generated content as a means of investigating urban dynamics.

Spatio Temporal Recommendation In Social Media

Author: Hongzhi Yin
Publisher: Springer
ISBN: 9811007489
Size: 22.44 MB
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This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users’ behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users’ behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.

Big Data

Author: Min Chen
Publisher: Springer
ISBN: 331906245X
Size: 59.76 MB
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This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore several representative applications of big data such as enterprise management, online social networks, healthcare and medical applications, collective intelligence and smart grids. This book concludes with a thoughtful discussion of possible research directions and development trends in the field. Big Data: Related Technologies, Challenges and Future Prospects is a concise yet thorough examination of this exciting area. It is designed for researchers and professionals interested in big data or related research. Advanced-level students in computer science and electrical engineering will also find this book useful.

Structural Plasticity

Author: Wai Fah Chen
Publisher: Springer
ISBN: 9781461277460
Size: 39.94 MB
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This book is designed for use as a supplement to the textbook "Plasticity for Structural Engineers" by W.F. Chen and D.J. Han (Springer-Verlag, 1988) or other plasticity texts. The purpose is to help students and structural engineers learn and practice how to solve typical engineering plasticity problems in general and, more importantly, how to use computers to solve plasticity problems in structural engineering in particular. To this end, specific numerical algorithms in the computer software implementation of the theory together with actual code development are given. A number of solved and supplementary problems are provided, as well as two computer-aided-education (CAE) programs, to enhance the students' understanding of these subjects.

Cognitive Computing For Big Data Systems Over Iot

Author: Arun Kumar Sangaiah
Publisher: Springer
ISBN: 3319706888
Size: 75.62 MB
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This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.