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Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality.

Big data. --- Mobile communication systems. --- Vehicles --- Vehicular communication systems --- Radio --- Wireless communication systems --- Data sets, Large --- Large data sets --- Data sets --- Communication systems --- Data mining. --- Transportation --- Mobile communication systems --- Emergency management --- Data processing --- Exploration de données (Informatique) --- Transport --- Informatique.

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Big data --- Strategic planning --- Data mining --- Données volumineuses --- Planification stratégique --- Exploration de données (Informatique) --- Gestion de l'information --- Bases de données --- Données massives --- Changement organisationnel --- Organisation de l'entreprise --- Gestion --- Données volumineuses --- Planification stratégique --- Exploration de données (Informatique) --- Big data. --- Changement organisationnel. --- Organisation de l'entreprise. --- Bases de données

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Bloom Filter: A Data Structure for Computer Networking, Big Data, Cloud Computing, Internet of Things, Bioinformatics, and Beyond focuses on both the theory and practice of the most emerging areas for Bloom filter application, including Big Data, Cloud Computing, Internet of Things, and Bioinformatics. Sections provide in-depth insights on structure and variants, focus on its role in computer networking, and discuss applications in various research domains, such as Big Data, Cloud Computing, and Bioinformatics. Since its inception, the Bloom Filter has been extensively experimented with and developed to enhance system performance such as web cache.

Data mining. --- Data sets. --- Datasets --- Raw data sets --- Computer files --- Electronic information resources --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Exploration de données (Informatique) --- Jeux de données. --- Data mining --- Data sets --- Computational Biology --- Biometry --- Data Science

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This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners.

Machine learning --- Data mining --- Parallel algorithms --- Parallel programs (Computer programs) --- Apprentissage automatique --- Exploration de données (Informatique) --- Algorithmes parallèles --- Programmes parallèles (Logiciels) --- Exploration de données (Informatique) --- Algorithmes parallèles --- Programmes parallèles (Logiciels) --- Machine Learning --- Machine learning. --- Data mining. --- Parallel algorithms. --- Parallel computer programs --- Parallel processing (Electronic computers) --- Computer programs --- Algorithms --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Learning, Machine --- Artificial intelligence --- Machine theory

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The Information Management Systems group at the University of Padua, led by Maristella Agosti, has been a major contributor to information retrieval (IR) and digital libraries for nearly twenty years. This group has gained an excellent reputation in the IR community and has produced some of the best-known IR researchers, whose work spans a broad range of topics. The papers in this book deal with e.g. automated text categorizations, web link analysis algorithms, retrieval in multimedia digital libraries, and multilingual information retrieval. The presentation of original research results built on the past work of the group which at the same time summarizes past findings and opens up new directions and new areas of possible future research and cooperation will appeal to researchers and developers in institutions and companies working on search engines and information retrieval algorithms.

Information retrieval. --- Digital libraries. --- Libraries --- Multimedia library services. --- Electronic information resource searching. --- Data mining. --- Electronic information resources. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computer searching --- Electronic searching --- Online searching --- Searching electronic information resources --- Information retrieval --- Public services (Libraries) --- Documentation --- Public institutions --- Librarians --- Digital curation --- Digital media collections --- Digital media libraries --- Digital repositories --- Electronic libraries --- Electronic publication collections --- Electronic publication libraries --- Electronic text collections --- Repositories, Digital --- Virtual libraries --- Information storage and retrieval systems --- Web archives --- Data retrieval --- Data storage --- Discovery, Information --- Information discovery --- Information storage and retrieval --- Retrieval of information --- Information science --- Recherche de l'information --- Bibliothèques virtuelles --- Bibliothèques --- Multimédia dans les bibliothèques --- Recherche de l'information électronique --- Exploration de données (Informatique) --- Sources d'information électroniques --- EPUB-LIV-FT LIVINFOR SPRINGER-B --- Information storage and retrieval. --- Natural language processing (Computer science). --- Library science. --- Information Storage and Retrieval. --- Natural Language Processing (NLP). --- Library Science. --- Librarianship --- Library economy --- Bibliography --- NLP (Computer science) --- Artificial intelligence --- Electronic data processing --- Human-computer interaction --- Semantic computing --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization