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Data is supposed to be the new gold, but how can you unlock the value in your data? Managing large datasets used to be a task for specialists, but you don't have to worry about inconsistencies or errors anymore. OpenRefine lets you clean, link, and publish your dataset in a breeze. Using OpenRefine takes you on a practical tour of all the handy features of this well-known data transformation tool. It is a hands-on recipe book that teaches you data techniques by example. Starting from the basics, it gradually transforms you into an OpenRefine expert. This book will teach you all the necessary skills to handle any large dataset and to turn it into high-quality data for the Web. After you learn how to analyze data and spot issues, we'll see how we can solve them to obtain a clean dataset. Messy and inconsistent data is recovered through advanced techniques such as automated clustering. We'll then show extract links from keyword and full-text fields using reconciliation and named-entity extraction.

Information systems --- Library automation --- Computer architecture. Operating systems --- Data mining. --- Electronic data processing. --- Exploration de données (Informatique) --- Informatique --- COMPUTERS --- General --- Engineering & Applied Sciences --- Computer Science --- Exploration de données (Informatique)

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Sensor networks --- Data mining --- Algorithms --- Coding theory --- Réseaux de capteurs --- Exploration de données (Informatique) --- Algorithmes --- Codage --- EPUB-LIV-FT LIVINFOR SPRINGER-B --- Algorithms. --- Coding theory. --- Data mining. --- Sensor networks.

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"Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences"-- "Opinion and sentiment and their related concepts such as evaluation, appraisal, attitude, affect, emotion and mood are about our subjective feelings and beliefs. They are central to the human psychology and are key influencers of our behaviors. Our beliefs and perceptions of reality, as well as the choices we make, are to a considerable degree conditioned upon how others see and perceive the world. Due to this reason, our views about the world are very much influenced by those of others, and whenever we need to make a decision we often seek out others' opinions. This is not only true for individuals but also true for organizations. From an application point of view, we naturally want to mine people's opinions and feelings toward any subject matter of interest, which is the task of sentiment analysis. More precisely, sentiment analysis, which is also called opinion mining, is a field of study that aims to extract opinions and sentiments from natural language text using computational methods"--

Mathematical linguistics --- Information systems --- Qualitative methods in social research --- COMPUTERS / Database Management / General. --- Natural language processing (Computer science) --- Computational linguistics. --- Public opinion --- Data mining. --- Traitement automatique des langues naturelles --- Linguistique informatique --- Exploration de données (Informatique) --- Data processing. --- Opinion publique --- Data processing --- Informatique --- Exploration de données (Informatique) --- Natural language processing (Computer science).

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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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Artificial intelligence. Robotics. Simulation. Graphics --- Data mining --- Exploration de données (Informatique) --- 681.3*H28 --- 681.3*G3 --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Database applications --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3*H28 Database applications --- Exploration de données (Informatique) --- Acqui 2006