Data Mining Mobile Devices

  • Filename: data-mining-mobile-devices.
  • ISBN: 9781466555969
  • Release Date: 2016-04-19
  • Number of pages: 328
  • Author: Jesus Mena
  • Publisher: CRC Press



With today’s consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire. Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users. Examines the construction and leveraging of mobile sites Describes how to use mobile apps to gather key data about consumers’ behavior and preferences Discusses mobile mobs, which can be differentiated as distinct marketplaces—including Apple®, Google®, Facebook®, Amazon®, and Twitter® Provides detailed coverage of mobile analytics via clustering, text, and classification AI software and techniques Mobile devices serve as detailed diaries of a person, continuously and intimately broadcasting where, how, when, and what products, services, and content your consumers desire. The future is mobile—data mining starts and stops in consumers' pockets. Describing how to analyze Wi-Fi and GPS data from websites and apps, the book explains how to model mined data through the use of artificial intelligence software. It also discusses the monetization of mobile devices’ desires and preferences that can lead to the triangulated marketing of content, products, or services to billions of consumers—in a relevant, anonymous, and personal manner.

Data Mining Mobile Devices

  • Filename: data-mining-mobile-devices.
  • ISBN: 9781490257013
  • Release Date: 2016-10-17
  • Number of pages: 39
  • Author: CTI Reviews
  • Publisher: Cram101 Textbook Reviews



Facts101 is your complete guide to Data Mining Mobile Devices. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.

Multimedia Data Mining and Analytics

  • Filename: multimedia-data-mining-and-analytics.
  • ISBN: 9783319149981
  • Release Date: 2015-03-31
  • Number of pages: 454
  • Author: Aaron Baughman
  • Publisher: Springer



This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The work describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Features: reviews how innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining; provides practical details on implementing the technology for solving real-world problems; includes chapters devoted to privacy issues in multimedia social environments and large-scale biometric data processing; covers content and concept based multimedia search and advanced algorithms for multimedia data representation, processing and visualization.

Pocket Data Mining

  • Filename: pocket-data-mining.
  • ISBN: 3319346865
  • Release Date: 2016-08-23
  • Number of pages: 108
  • Author: Mohamed Medhat Gaber
  • Publisher: Springer



Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Pocket Data Mining

  • Filename: pocket-data-mining.
  • ISBN: 9783319027111
  • Release Date: 2013-10-19
  • Number of pages: 108
  • Author: Mohamed Medhat Gaber
  • Publisher: Springer Science & Business Media



Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Mobile Technologies for Activity Travel Data Collection and Analysis

  • Filename: mobile-technologies-for-activity-travel-data-collection-and-analysis.
  • ISBN: 9781466661714
  • Release Date: 2014-06-30
  • Number of pages: 325
  • Author: Rasouli, Soora
  • Publisher: IGI Global



"This book concentrates on one particular and fast-growing application of mobile technologies: data acquisition for the tourism industry, providing travel agents, visitors, and hosts with the most advanced data mining methods, empirical research findings, and computational analysis techniques necessary to compete effectively in the global tourism industry"--Provided by publisher.

Advances in Electrical Engineering and Automation

  • Filename: advances-in-electrical-engineering-and-automation.
  • ISBN: 9783642279515
  • Release Date: 2012-02-02
  • Number of pages: 520
  • Author: Anne Xie
  • Publisher: Springer Science & Business Media



EEA2011 is an integrated conference concentration its focus on Electrical Engineering and Automation. In the proceeding, you can learn much more knowledge about Electrical Engineering and Automation of researchers from all around the world. The main role of the proceeding is to be used as an exchange pillar for researchers who are working in the mentioned fields. In order to meet the high quality of Springer, AISC series, the organization committee has made their efforts to do the following things. Firstly, poor quality paper has been refused after reviewing course by anonymous referee experts. Secondly, periodically review meetings have been held around the reviewers about five times for exchanging reviewing suggestions. Finally, the conference organizers had several preliminary sessions before the conference. Through efforts of different people and departments, the conference will be successful and fruitful.

Energy Efficiency in Large Scale Distributed Systems

  • Filename: energy-efficiency-in-large-scale-distributed-systems.
  • ISBN: 9783642405174
  • Release Date: 2013-09-20
  • Number of pages: 312
  • Author: Jean-Marc Pierson
  • Publisher: Springer



This book constitutes revised selected papers from the Conference on Energy Efficiency in Large Scale Distributed Systems, EE-LSDS, held in Vienna, Austria, in April 2013. It served as the final event of the COST Action IC0804 which started in May 2009. The 15 full papers presented in this volume were carefully reviewed and selected from 31 contributions. In addition, 7 short papers and 3 demo papers are included in this book. The papers are organized in sections named: modeling and monitoring of power consumption; distributed, mobile and cloud computing; HPC computing; wired and wireless networking; and standardization issues.

Parallel and Distributed Processing and Applications

  • Filename: parallel-and-distributed-processing-and-applications.
  • ISBN: 3540241280
  • Release Date: 2004-12-02
  • Number of pages: 1058
  • Author: Jiannong Cao
  • Publisher: Springer Science & Business Media



This book constitutes the refereed proceedings of the Second International Symposium on Parallel and Distributed Processing and Applications, ISPA 2004, held in Hong Kong, China in December 2004. The 78 revised full papers and 38 revised short papers presented were carefully reviewed and selected from 361 submissions. The papers are organized in topical sections on parallel algorithms and systems, data mining and management, distributed algorithms and systems, fault tolerance protocols and systems, sensor networks and protocols, cluster systems, grid applications and systems, peer-to-peer and ad hoc networking, grid scheduling and algorithms, data replication and caching, software engineering and testing, grid protocols, context-aware and mobile computing, distributed routing and switching protocols, cluster resource scheduling and algorithms, security, high performance processing, networking and protocols, artificial intelligence systems, hardware architecture and implementations, high performance computing architecture, and distributed systems architecture.

Service Oriented Distributed Knowledge Discovery

  • Filename: service-oriented-distributed-knowledge-discovery.
  • ISBN: 9781439875339
  • Release Date: 2012-10-05
  • Number of pages: 230
  • Author: Domenico Talia
  • Publisher: CRC Press



A new approach to distributed large-scale data mining, service-oriented knowledge discovery extracts useful knowledge from today’s often unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures. Service-Oriented Distributed Knowledge Discovery presents techniques, algorithms, and systems based on the service-oriented paradigm. Through detailed descriptions of real software systems, it shows how the techniques, models, and architectures can be implemented. The book covers key areas in data mining and service-oriented computing. It presents the concepts and principles of distributed knowledge discovery and service-oriented data mining. The authors illustrate how to design services for data analytics, describe real systems for implementing distributed knowledge discovery applications, and explore mobile data mining models. They also discuss the future role of service-oriented knowledge discovery in ubiquitous discovery processes and large-scale data analytics. Highlighting the latest achievements in the field, the book gives many examples of the state of the art in service-oriented knowledge discovery. Both novices and more seasoned researchers will learn useful concepts related to distributed data mining and service-oriented data analysis. Developers will also gain insight on how to successfully use service-oriented knowledge discovery in databases (KDD) frameworks.

Transactions on Large Scale Data and Knowledge Centered Systems V

  • Filename: transactions-on-large-scale-data-and-knowledge-centered-systems-v.
  • ISBN: 9783642281471
  • Release Date: 2012-02-10
  • Number of pages: 223
  • Author: Abdelkader Hameurlain
  • Publisher: Springer Science & Business Media



This fifth issue of the LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems offers nine full-length focusing on such hot topics as data management, knowledge discovery, and knowledge processing.

Knowledge Discovery and Data Mining

  • Filename: knowledge-discovery-and-data-mining.
  • ISBN: 9783642277085
  • Release Date: 2012-02-04
  • Number of pages: 798
  • Author: Honghua Tan
  • Publisher: Springer Science & Business Media



The volume includes a set of selected papers extended and revised from the 4th International conference on Knowledge Discovery and Data Mining, March 1-2, 2011, Macau, Chin. This Volume is to provide a forum for researchers, educators, engineers, and government officials involved in the general areas of knowledge discovery and data mining and learning to disseminate their latest research results and exchange views on the future research directions of these fields. 108 high-quality papers are included in the volume.

The Analytical Puzzle

  • Filename: the-analytical-puzzle.
  • ISBN: 9781634620338
  • Release Date: 2012-07-01
  • Number of pages: 346
  • Author: David Haertzen
  • Publisher: Technics Publications



Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organization. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualization and mobile devices. Here are the main objectives for each of the book’s 19 chapters: • Chapter 1: Develop a foundational knowledge of data warehousing, business intelligence and analytics • Chapter 2: Build the business case needed to sell your data warehousing project, and then produce a project plan that avoids common pitfalls • Chapter 3: Elicit and organize business intelligence and data warehousing business requirements • Chapter 4: Specify the technical architecture of the data warehousing system, including software and infrastructure components, technology stack, and non-functional requirements. Gain an understanding of cloud based data warehousing and data warehouse appliances • Chapter 5: Learn about data attributes including metrics and key performance indicators (KPIs), the raw material of data warehousing and business intelligence • Chapter 6: Learn about data modeling and how to apply design patterns for each part of the data warehouse • Chapter 7: Speak the dimensional modeling language of measures, dimensions, facts, cubes, stars, and snowflakes • Chapter 8: Organize a successful data governance program. Learn how to manage metadata for your data warehousing and business intelligence project • Chapter 9: Identify useful data sources and implement a data quality program • Chapter 10: Use database technology for your data warehousing project, and understand the impact of data warehouse appliances, big data, in memory databases, columnar databases and OnLine Analytical Processing (OLAP) • Chapter 11: Apply data integration and understand the role data mapping, data cleansing, data transformation, and loading data play in a successful data warehouse • Chapter 12: Use the business intelligence (BI) operations of slice, dice, drill down, roll up, and pivot to analyze and present data • Chapter 13: Learn about descriptive and predictive statistics, and calculate mean, median, mode, variance and standard deviation • Chapter 14: Harness analytical methods such as regression analysis, data mining, and statistics to make profitable decisions and anticipate the future • Chapter 15: Appreciate the components and design patterns that compose a successful analytic application • Chapter 16: Gain an understanding of the uses and benefits of scorecards and dashboards including support of mobile device users • Chapter 17: Gain insight into applications of business intelligence that could profit your organization, including risk management, finance, marketing, government, healthcare, science and sports • Chapter 18: Perform customer analytics to better understand and segment your customers • Chapter 19: Test, roll out, and sustain the data warehouse

Mobile Computing Concepts Methodologies Tools and Applications

  • Filename: mobile-computing-concepts-methodologies-tools-and-applications.
  • ISBN: 160566054X
  • Release Date: 2008-11-30
  • Number of pages: 3721
  • Author: Taniar, David
  • Publisher: IGI Global



"This multiple-volume publication advances the emergent field of mobile computing offering research on approaches, observations and models pertaining to mobile devices and wireless communications from over 400 leading researchers"--Provided by publisher.

Data Warehousing and Mining Concepts Methodologies Tools and Applications

  • Filename: data-warehousing-and-mining-concepts-methodologies-tools-and-applications.
  • ISBN: 9781599049526
  • Release Date: 2008-05-31
  • Number of pages: 4092
  • Author: Wang, John
  • Publisher: IGI Global



In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.

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