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BDCloud-SocialCom-SustainCom 2016 : 2016 IEEE International Conferences on Big Data and Cloud Computing : Social Computing and Networking : Sustainable Computing and Communications : 8-10 October 2016, Atlanta, Georgia USA / / IEEE Computer Society ; edited by Zhipeng Cai [and eight others]
| BDCloud-SocialCom-SustainCom 2016 : 2016 IEEE International Conferences on Big Data and Cloud Computing : Social Computing and Networking : Sustainable Computing and Communications : 8-10 October 2016, Atlanta, Georgia USA / / IEEE Computer Society ; edited by Zhipeng Cai [and eight others] |
| Pubbl/distr/stampa | Los Alamitos, California : , : IEEE Computer Society, , 2016 |
| Descrizione fisica | 1 online resource (xxii, 628 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Cloud computing |
| ISBN | 1-5090-3936-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910332527003321 |
| Los Alamitos, California : , : IEEE Computer Society, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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BDCloud-SocialCom-SustainCom 2016 : 2016 IEEE International Conferences on Big Data and Cloud Computing : Social Computing and Networking : Sustainable Computing and Communications : 8-10 October 2016, Atlanta, Georgia USA / / IEEE Computer Society ; edited by Zhipeng Cai [and eight others]
| BDCloud-SocialCom-SustainCom 2016 : 2016 IEEE International Conferences on Big Data and Cloud Computing : Social Computing and Networking : Sustainable Computing and Communications : 8-10 October 2016, Atlanta, Georgia USA / / IEEE Computer Society ; edited by Zhipeng Cai [and eight others] |
| Pubbl/distr/stampa | Los Alamitos, California : , : IEEE Computer Society, , 2016 |
| Descrizione fisica | 1 online resource (xxii, 628 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Cloud computing |
| ISBN | 1-5090-3936-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996575465303316 |
| Los Alamitos, California : , : IEEE Computer Society, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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BDIOT 2018 : Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things / / ACM Digital Library ; Association for Computing Machinery-Digital Library
| BDIOT 2018 : Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things / / ACM Digital Library ; Association for Computing Machinery-Digital Library |
| Pubbl/distr/stampa | New York, NY : , : ACM, , 2018 |
| Descrizione fisica | 1 online resource (217 pages) |
| Disciplina | 005.7 |
| Collana | ACM international conference proceedings series |
| Soggetto topico |
Big data
Internet of things |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910375674003321 |
| New York, NY : , : ACM, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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BDIOT2017 : Proceedings of the International Conference on Big Data and Internet of Thing / / Association for Computing Machinery
| BDIOT2017 : Proceedings of the International Conference on Big Data and Internet of Thing / / Association for Computing Machinery |
| Pubbl/distr/stampa | New York, NY : , : ACM, , 2017 |
| Descrizione fisica | 1 online resource (251 pages) |
| Disciplina | 005.7 |
| Collana | ACM international conference proceedings series |
| Soggetto topico |
Big data
Internet of things |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910376280403321 |
| New York, NY : , : ACM, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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BDVA 2018 : 2018 International Symposium on Big Data Visual and Immersive Analytics : Konstanz, Germany, October 17 -19, 2018 / / IEEE Computer Society
| BDVA 2018 : 2018 International Symposium on Big Data Visual and Immersive Analytics : Konstanz, Germany, October 17 -19, 2018 / / IEEE Computer Society |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (889 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Visual analytics |
| ISBN | 1-5386-9194-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996280353303316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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BDVA 2018 : 2018 International Symposium on Big Data Visual and Immersive Analytics : Konstanz, Germany, October 17 -19, 2018 / / IEEE Computer Society
| BDVA 2018 : 2018 International Symposium on Big Data Visual and Immersive Analytics : Konstanz, Germany, October 17 -19, 2018 / / IEEE Computer Society |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (889 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Visual analytics |
| ISBN | 1-5386-9194-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910294551103321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The beginner's guide to data science / / Robert Ball and Brian Rague
| The beginner's guide to data science / / Robert Ball and Brian Rague |
| Autore | Ball Robert |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (251 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Data mining Dades massives Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-07865-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910631100303321 |
Ball Robert
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The beginner's guide to data science / / Robert Ball and Brian Rague
| The beginner's guide to data science / / Robert Ball and Brian Rague |
| Autore | Ball Robert |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (251 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Data mining Dades massives Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-07865-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996499869603316 |
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Ball Robert
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Beginning Apache Spark 2 [[electronic resource] ] : With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library / / by Hien Luu
| Beginning Apache Spark 2 [[electronic resource] ] : With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library / / by Hien Luu |
| Autore | Luu Hien |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 |
| Descrizione fisica | 1 online resource (XI, 393 p. 86 illus.) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Java (Computer program language) Data mining Open source software Computer programming Big Data Java Data Mining and Knowledge Discovery Open Source |
| ISBN | 1-4842-3579-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction to Apache Spark -- 2. Working with Apache Spark -- 3. Resilient Distributed Dataset -- 4. Spark SQL - Foundation -- 5. Spark SQL - Advanced -- 6. Spark Streaming -- 7. Spark Streaming Advanced -- 8. Machine Learning with Spark. |
| Record Nr. | UNINA-9910300752003321 |
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Luu Hien
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library / / Hien Luu
| Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library / / Hien Luu |
| Autore | Hien Luu |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | New York, New York : , : Apress, , [2021] |
| Descrizione fisica | 1 online resource (445 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Distributed databases Open source software Machine learning |
| ISBN | 1-4842-7383-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Apache Spark -- Overview -- History -- Spark Core Concepts and Architecture -- Spark Cluster and Resource Management System -- Spark Applications -- Spark Drivers and Executors -- Spark Unified Stack -- Spark Core -- Spark SQL -- Spark Structured Streaming -- Spark MLlib -- Spark GraphX -- SparkR -- Apache Spark 3.0 -- Adaptive Query Execution Framework -- Dynamic Partition Pruning (DPP) -- Accelerator-aware Scheduler -- Apache Spark Applications -- Spark Example Applications -- Apache Spark Ecosystem -- Delta Lake -- Koalas -- MLflow -- Summary -- Chapter 2: Working with Apache Spark -- Downloading and Installation -- Downloading Spark -- Installing Spark -- Spark Scala Shell -- Spark Python Shell -- Having Fun with the Spark Scala Shell -- Useful Spark Scala Shell Command and Tips -- Basic Interactions with Scala and Spark -- Basic Interactions with Scala -- Spark UI and Basic Interactions with Spark -- Spark UI -- Basic Interactions with Spark -- Introduction to Collaborative Notebooks -- Create a Cluster -- Create a Folder -- Create a Notebook -- Setting up Spark Source Code -- Summary -- Chapter 3: Spark SQL: Foundation -- Understanding RDD -- Introduction to the DataFrame API -- Creating a DataFrame -- Creating a DataFrame from RDD -- Creating a DataFrame from a Range of Numbers -- Creating a DataFrame from Data Sources -- Creating a DataFrame by Reading Text Files -- Creating a DataFrame by Reading CSV Files -- Creating a DataFrame by Reading JSON Files -- Creating a DataFrame by Reading Parquet Files -- Creating a DataFrame by Reading ORC Files -- Creating a DataFrame from JDBC -- Working with Structured Operations -- Working with Columns -- Working with Structured Transformations.
select(columns) -- selectExpr(expressions) -- filler(condition), where(condition) -- distinct, dropDuplicates -- sort(columns), orderBy(columns) -- limit(n) -- union(otherDataFrame) -- withColumn(colName, column) -- withColumnRenamed(existingColName, newColName) -- drop(columnName1, columnName2) -- sample(fraction), sample(fraction, seed), sample(fraction, seed, withReplacement) -- randomSplit(weights) -- Working with Missing or Bad Data -- Working with Structured Actions -- describe(columnNames) -- Introduction to Datasets -- Creating Datasets -- Working with Datasets -- Using SQL in Spark SQL -- Running SQL in Spark -- Writing Data Out to Storage Systems -- The Trio: DataFrame, Dataset, and SQL -- DataFrame Persistence -- Summary -- Chapter 4: Spark SQL: Advanced -- Aggregations -- Aggregation Functions -- Common Aggregation Functions -- count(col) -- countDistinct(col) -- min(col), max(col) -- sum(col) -- sumDistinct(col) -- avg(col) -- skewness(col), kurtosis(col) -- variance(col), stddev(col) -- Aggregation with Grouping -- Multiple Aggregations per Group -- Collection Group Values -- Aggregation with Pivoting -- Joins -- Join Expression and Join Types -- Working with Joins -- Inner Joins -- Left Outer Joins -- Right Outer Joins -- Outer Joins (a.k.a. Full Outer Joins) -- Left Anti-Joins -- Left Semi-Joins -- Cross (a.k.a. Cartesian) -- Dealing with Duplicate Column Names -- Use Original DataFrame -- Renaming Column Before Joining -- Using Joined Column Name -- Overview of Join Implementation -- Shuffle Hash Join -- Broadcast Hash Join -- Functions -- Working with Built-in Functions -- Working with Date Time Functions -- Working with String Functions -- Working with Math Functions -- Working with Collection Functions -- Working with Miscellaneous Functions -- Working with User-Defined Functions (UDFs) -- Advanced Analytics Functions. Aggregation with Rollups and Cubes -- Rollups -- Cubes -- Aggregation with Time Windows -- Window Functions -- Exploring Catalyst Optimizer -- Logical Plan -- Physical Plan -- Catalyst in Action -- Project Tungsten -- Summary -- Chapter 5: Optimizing Spark Applications -- Common Performance Issues -- Spark Configurations -- Different Ways of Setting Properties -- Different Kinds of Properties -- Viewing Spark Properties -- Spark Memory Management -- Spark Driver -- Spark Executor -- Leverage In-Memory Computation -- When to Persist and Cache Data -- Persistence and Caching APIs -- Persistence and Caching Example -- Understanding Spark Joins -- Broadcast Hash Join -- Shuffle Sort Merge Join -- Adaptive Query Execution -- Dynamically Coalescing Shuffle Partitions -- Dynamically Switching Join Strategies -- Dynamically Optimizing Skew Joins -- Summary -- Chapter 6: Spark Streaming -- Stream Processing -- Concepts -- Data Delivery Semantics -- Notion of Time -- Windowing -- Stream Processing Engine Landscape -- Spark Streaming Overview -- Spark DStream -- Spark Structured Streaming -- Overview -- Core Concepts -- Data Sources -- Output Modes -- Trigger Types -- Data Sinks -- Watermarking -- Structured Streaming Applications -- Streaming DataFrame Operations -- Selection, Project, Aggregation Operations -- Join Operations -- Working with Data Sources -- Working with a Socket Data Source -- Working with a Rate Data Source -- Working with a File Data Source -- Working with a Kafka Data Source -- Working with a Custom Data Source -- Working with Data Sinks -- Working with a File Data Sink -- Working with a Kafka Data Sink -- Working with a foreach Data Sink -- Working with a Console Data Sink -- Working with a Memory Data Sink -- Output Modes -- Triggers -- Summary -- Chapter 7: Advanced Spark Streaming -- Event Time. Fixed Window Aggregation over an Event Time -- Sliding Window Aggregation over Event Time -- Aggregation State -- Watermarking: Limit State and Handle Late Data -- Arbitrary Stateful Processing -- Arbitrary Stateful Processing with Structured Streaming -- Handling State Timeouts -- Arbitrary State Processing in Action -- Extracting Patterns with mapGroupsWithState -- User Sessionization with flatMapGroupsWithState -- Handling Duplicate Data -- Fault Tolerance -- Streaming Application Code Change -- Spark Runtime Change -- Streaming Query Metrics and Monitoring -- Streaming Query Metrics -- Monitoring Streaming Queries via Callback -- Monitoring Streaming Queries via Visualization UI -- Streaming Query Summary Information -- Streaming Query Detailed Statistics Information -- Troubleshooting Streaming Query -- Summary -- Chapter 8: Machine Learning with Spark -- Machine Learning Overview -- Machine Learning Terminologies -- Machine Learning Types -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Machine Learning Development Process -- Spark Machine Learning Library -- Machine Learning Pipelines -- Transformers -- Estimators -- Pipeline -- Pipeline Persistence: Saving and Loading -- Model Tuning -- Speeding Up Model Tuning -- Model Evaluators -- Machine Learning Tasks in Action -- Classification -- Model Hyperparameters -- Example -- Regression -- Model Hyperparameters -- Example -- Recommendation -- Model Hyperparameters -- Example -- Deep Learning Pipeline -- Summary -- Chapter 9: Managing the Machine Learning Life Cycle -- The Rise of MLOps -- MLOps Overview -- MLflow Overview -- MLflow Components -- MLflow in Action -- MLflow Tracking -- MLflow Projects -- MLflow Models -- MLflow Model Registry -- Model Deployment and Prediction -- Summary -- Index. |
| Record Nr. | UNINA-9910506385403321 |
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Hien Luu
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| New York, New York : , : Apress, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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