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1. |
Record Nr. |
UNINA9910466092903321 |
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Autore |
Ankam Venkat |
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Titolo |
Big data analytics : a handy reference guide for data analysts and data scientists to help obtain value from big data analytics using Spark on Hadoop clusters / / Venkat Ankam |
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Pubbl/distr/stampa |
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Birmingham, England : , : Packt Publishing, , 2016 |
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©2016 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (326 pages) : illustrations |
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Disciplina |
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Soggetti |
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Big data - Security measures |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Sommario/riassunto |
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A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark |
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SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components ? Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components ? HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learni... |
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2. |
Record Nr. |
UNISA996466120403316 |
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Titolo |
Combinatorial Pattern Matching [[electronic resource] ] : 16th Annual Symposium, CPM 2005, Jeju Island, Korea, June 19-22, 2005, Proceedings / / edited by Alberto Apostolico, Maxime Crochemore, Kunsoo Park |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 |
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ISBN |
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3-540-31562-4 |
3-540-26201-6 |
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Edizione |
[1st ed. 2005.] |
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Descrizione fisica |
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1 online resource (XII, 452 p.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 3537 |
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Disciplina |
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Soggetti |
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Algorithms |
Artificial intelligence—Data processing |
Information storage and retrieval systems |
Natural language processing (Computer science) |
Pattern recognition systems |
Bioinformatics |
Data Science |
Information Storage and Retrieval |
Natural Language Processing (NLP) |
Automated Pattern Recognition |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Sharper Upper and Lower Bounds for an Approximation Scheme for Consensus-Pattern -- On the Longest Common Rigid Subsequence Problem -- Text Indexing with Errors -- A New Compressed Suffix Tree Supporting Fast Search and Its Construction Algorithm Using Optimal Working Space -- Succinct Suffix Arrays Based on Run-Length Encoding -- Linear-Time Construction of Compressed Suffix Arrays Using o(n log n)-Bit Working Space for Large Alphabets -- Faster Algorithms for ?,?-Matching and Related Problems -- A Fast Algorithm for Approximate String Matching on Gene Sequences -- Approximate Matching in the L 1 Metric -- An Efficient Algorithm for Generating Super Condensed |
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Neighborhoods -- The Median Problem for the Reversal Distance in Circular Bacterial Genomes -- Using PQ Trees for Comparative Genomics -- Hardness of Optimal Spaced Seed Design -- Weighted Directed Word Graph -- Construction of Aho Corasick Automaton in Linear Time for Integer Alphabets -- An Extension of the Burrows Wheeler Transform and Applications to Sequence Comparison and Data Compression -- DNA Compression Challenge Revisited: A Dynamic Programming Approach -- On the Complexity of Sparse Exon Assembly -- An Upper Bound on the Hardness of Exact Matrix Based Motif Discovery -- Incremental Inference of Relational Motifs with a Degenerate Alphabet -- Speeding up Parsing of Biological Context-Free Grammars -- A New Periodicity Lemma -- Two Dimensional Parameterized Matching -- An Optimal Algorithm for Online Square Detection -- A Simple Fast Hybrid Pattern-Matching Algorithm -- Prefix-Free Regular-Expression Matching -- Reducing the Size of NFAs by Using Equivalences and Preorders -- Regular Expression Constrained Sequence Alignment -- A Linear Tree Edit Distance Algorithm for Similar Ordered Trees -- A Polynomial Time Matching Algorithm of Ordered Tree Patterns Having Height-Constrained Variables -- Assessing the Significance of Sets of Words -- Inferring a Graph from Path Frequency -- Exact and Approximation Algorithms for DNA Tag Set Design -- Parametric Analysis for Ungapped Markov Models of Evolution -- Linear Programming for Phylogenetic Reconstruction Based on Gene Rearrangements -- Identifying Similar Surface Patches on Proteins Using a Spin-Image Surface Representation -- Mass Spectra Alignments and Their Significance. |
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