term.subject="administration"

Print version: Systematic searching, Foreword / Carol Lefebvre -- Introduction: where are we now? / Paul Levay, Jenny Craven -- Innovative approaches to systematic reviewing / Andrew Booth -- Searching for broad-based topics / Claire Stansfield -- Choosing the right databases and search techniques / Alison Bethel, Morwenna Rogers -- Gathering evidence from grey literature and unpublished data / Shannon Kugley, Richard Epstein -- Social media as a source of evidence / Su Golder -- Text mining for information specialists / Julie Glanville -- Using linked data for evidence synthesis / Andy Mitchell, Chris Mavergames -- Evidence surveillance to keep up to date with new research / James Thomas, Anna Noel-Storr, Steve McDonald -- Training the next generation of information specialists / Michelle Maden, Gil Young -- Collaborative working to improve searching / Siw Waffenschmidt, Elke Hauser -- Communication for information specialists / Margaret Sampson -- The information specialist as an expert searcher / Alison Brettle -- Conclusion: where do we go from here? / Paul Levay, Jenny Craven

Authors: Alok Shrivastwa and five others. Cf. Credits page

Erscheint auch als: Singh, Karan. Learning Ceph, Cover -- Copyright -- Credits -- Foreword -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introducing Ceph Storage -- An overview of Ceph -- The history and evolution of Ceph -- Ceph releases -- Ceph and the future of storage -- Ceph as a cloud storage solution -- Ceph as a software-defined solution -- Ceph as a unified storage solution -- The next generation architecture -- Raid � end of an era -- The compatibility portfolio -- Ceph block storage -- The Ceph filesystem, Ceph object storageCeph versus others -- GPFS -- iRODS -- HDFS -- Lustre -- Gluster -- Ceph -- Summary -- Chapter 2: Ceph Instant Deployment -- Creating a sandbox environment with VirtualBox -- From zero to Ceph � deploying your first Ceph cluster -- Scaling up your Ceph cluster � monitor and OSD addition -- Adding the Ceph monitor -- Adding the Ceph OSD -- Summary -- Chapter 3: Ceph Architecture and Components -- Ceph storage architecture -- Ceph RADOS -- Ceph Object Storage Device -- The Ceph OSD filesystem -- The Ceph OSD journal, OSD commandsCeph monitors -- Monitor commands -- librados -- The Ceph block storage -- Ceph Object Gateway -- Ceph MDS -- Deploying MDS for your Ceph cluster -- The Ceph filesystem -- Summary -- Chapter 4: Ceph Internals -- Ceph under the hood -- Object -- Locating objects -- CRUSH -- The CRUSH lookup -- The CRUSH hierarchy -- Recovery and rebalancing -- Editing a CRUSH map -- Customizing a cluster layout -- Placement groups -- Calculating PG numbers -- Modifying PG and PGP -- PG peering, up and acting sets -- Ceph pools, Pool operationsCreating and listing pools -- Ceph data management -- Summary -- Chapter 5: Deploying Ceph � the Way You Should Know -- Hardware planning for a Ceph cluster -- Monitor requirements -- OSD requirements -- Network requirements -- MDS requirements -- Setting up your VirtualBox environment � again -- Preparing your Ceph installation -- Getting the software -- Getting packages -- Getting Ceph tarballs -- Getting Ceph from GitHub -- Ceph cluster manual deployment -- Installing perquisites -- Deploying the Ceph cluster, Deploying monitorsCreating OSDs -- Scaling up your cluster -- Adding monitors -- Adding OSDs -- Ceph cluster deployment using the ceph-deploy tool -- Upgrading your Ceph cluster -- Upgrading a monitor -- Upgrading OSDs -- Summary -- Chapter 6: Storage Provisioning with Ceph -- The RADOS block device -- Setting up your first Ceph client -- Mapping the RADOS block device -- Resizing Ceph RBD -- Ceph RBD snapshots -- Ceph RBD clones -- The Ceph filesystem -- Mounting CephFS with a kernel driver -- Mounting CephFS as FUSE, Includes index

Print version: Franca, Wilson da Rocha ; MongoDB data modeling : focus on data usage and better design schemas with the help of MongoDB, Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introducing Data Modeling; The relationship between MongoDB and NoSQL; Introducing NoSQL (Not Only SQL); NoSQL databases types; Dynamic schema, scalability, and redundancy; Database design and data modeling; The ANSI-SPARC architecture; The external level; The conceptual level; The internal level; Data modeling; The conceptual model; The logical model; The physical model; Summary; Chapter 2: Data Modeling with MongoDB, Introducing documents and collectionsJSON; BSON; Characteristics of documents; The document size; Names and values for a field in a document; The document primary key; Support collections; The optimistic loop; Designing a document; Working with embedded documents; Working with references; Atomicity; Common document patterns; One-to-one; One-to-many; Many-to-many; Summary; Chapter 3: Querying Documents; Understanding the read operations; Selecting all documents; Selecting documents using criteria; Comparison operators; Logical operators; Element operators; Evaluation operators; Array operators, ProjectionsIntroducing the write operations; Inserts; Updates; Write concerns; Unacknowledged; Acknowledged; Journaled; Replica acknowledged; Bulk writing documents; Summary; Chapter 4: Indexing; Indexing documents; Indexing a single field; Indexing more than one field; Indexing multi-key fields; Indexing for text search; Creating special indexes; Time to live indexes; Unique indexes; Sparse indexes; Summary; Chapter 5: Optimizing Queries; Understanding the query plan; Evaluating queries; Covering a query; The query optimizer; Reading from many MongoDB instances; Summary, Chapter 6: Managing the DataOperational segregation; Giving priority to read operations; Capped collections; Data self-expiration; Summary; Chapter 7: Scaling; Scaling out MongoDB with sharding; Choosing the shard key; Basic concerns when choosing a shard key; Scaling a social inbox schema design; Fan out on read; Fan out on write; Fan out on write with buckets; Summary; Chapter 8: Logging and Real-time Analytics with MongoDB; Log data analysis; Error logs; Access logs; What we are looking for; Measuring the traffic on the web server; Designing the schema; Capturing an event request, A one-document solutionTTL indexes; Sharding; Querying for reports; Summary; Index, Includes index

Print version: Sremack, Joe ; Big Data Forensics - Learning Hadoop Investigations, Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Starting Out with Forensic Investigations and Big Data; Computer forensics overview; The forensic process; Identification; Collection; Analysis; Presentation; Other investigation considerations; Equipment; Evidence management; Investigator training and certification; The post-investigation process; What is Big Data?; The four Vs of Big Data; Big Data architecture and concepts; Big Data forensics; Metadata preservation; Collection methods; Collection verification; Summary, Chapter 2: Understanding Hadoop Internals and ArchitectureThe Hadoop architecture; The components of Hadoop; The Hadoop Distributed File System; The Hadoop configuration files; Hadoop daemons; Hadoop data analysis tools; Hive; HBase; Pig; Managing files in Hadoop; File permissions; Trash; Log files; File compression and splitting; Hadoop SequenceFile; The Hadoop archive files; Data serialization; Packaged jobs and JAR files; The Hadoop forensic evidence ecosystem; Running Hadoop; LightHadoop; Amazon Web Services; Loading Hadoop data; Importing sample data for testing; Summary, Chapter 3: Identifying Big Data EvidenceIdentifying evidence; Locating sources of data; Compiling data requirements; Reviewing the system architecture; Interviewing staff and reviewing the documentation; Assessing data viability; Identify data sources in noncooperative situations; Data collection requirements; Data source identification; Structured and unstructured data; Data collection types; In-house or third-party collection; An investigator-led collection; The chain of custody documentation; Summary; Chapter 4: Collecting Hadoop File System Data; Forensically collecting a cluster system, Physical versus remote collectionsHDFS collections through the host operating system; Imaging the host operating system; Imaging a mounted HDFS partition; Targeted collection from a Hadoop client; The Hadoop shell command collection; Collecting HDFS files; HDFS targeted data collection; Hadoop Offline Image and Edits Viewers; Collection via Sqoop; Other HDFS collection approaches; Summary; Chapter 5: Collecting Hadoop Application Data; Application collection approaches; Backups; Query extractions; Script extractions; Software extractions; Validating application collections, Collecting Hive evidenceLoading Hive data; Identifying Hive evidence; Hive backup collection; Hive query collection; Hive query control totals; Hive metadata and log collection; The Hive script collection; Collecting HBase evidence; Loading HBase data; Identifying HBase evidence; The HBase backup collection; The HBase query collection; HBase collection via scripts; HBase control totals; HBase metadata and log collection; Collecting other Hadoop application data and non-Hadoop data; Summary; Chapter 6: Performing Hadoop File System Analysis; The forensic analysis process, Includes index

Print version: Tiwari, Rajiv ; Hadoop for finance essentials : harness big data to provide meaningful insights, analytics, and business intelligence for your financial institution, Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Big Data Overview; What is big data?; Data volume; Data velocity; Data variety; Big data technology evolution; History; Current; Future; Big data landscape; Storage; NoSQL; NoSQL database types; Resource management; Data governance; Batch computing; Real-time computing; Data integration tools; Machine learning; Business intelligence and virtualization; Careers in big data; Hadoop architecture; HDFS cluster; MapReduce V1; MapReduce V2 -- YARN; The Hadoop jungle explained, Big data tamedHadoop -- the hero; HDFS -- Hadoop Distributed Filesystem; MapReduce; HBase; Hive; Pig; Zookeeper; Oozie; Flume; Sqoop; Hadoop distributions; Distribution -- on premise; Distribution -- cloud; Summary; Chapter 2: Big Data in Financial Services; Big data use cases across industry sectors; Healthcare; Human science; Telecom; Online retailer; Why big data in the financial sector?; Big data use cases in the financial sector; Data archival on HDFS; Regulatory; Fraud detection; Tick data; Risk management; Customer behavior prediction; Sentiment analysis (unstructured); Other use cases, Big data evolution in financeBig data tools -- what to learn; Getting your data into HDFS; Querying data from HDFS; SQL on Hadoop; Real time; Data governance and operations; ETL tools; Data analytics and business intelligence; Big data implementations in finance; Key challenges; Overcoming the challenges; Generate interest (play area); Pilot with a low-cost project; Hadoop is live -- now scale it up; Summary; Chapter 3: Hadoop in the Cloud; The big data cloud story; The why; The when; What's the catch?; Project details -- risk simulations in the cloud; Solution; Current world; Target world, Data collectionConfiguring the Hadoop cluster; Data upload; Data transformation; Data analysis; Summary; Chapter 4: Data Migration Using Hadoop; Project details -- archive your transaction data; Solution; Project Phase 1 -- split trade data into DW and Hadoop; Current world; Target world; Data collection; How to do it; Data analysis; Project Phase 2 -- migrate data from relational DW into Hadoop; Current world; Target world; Data collection; Data analysis; Summary; Chapter 5: Getting Started; Project details -- risk and regulatory reporting; Solution; Current world; Target world; Data collection, Option 1 -- Apache OozieOption 2 -- ETL tool ingestion; Data transformation; Hive or Pig?; Hive; Pig; Java MapReduce; Data analysis; BI tools; Summary; Chapter 6: Getting Experienced; Real-time big data; Project details -- Identifying fraudulent transactions; Solution; Current world; Target world; Markov Chain Model execution (batch mode); Storm architecture; Spark architecture; Data collection; Using Storm; Using Spark; Data transformation; Using Storm; Using Spark; Summary; Chapter 7: Scale It Up; Scale it up -- actually horizontally; Few more big data use cases; Use case -- fraud again, Includes index

Print version: Jacob, Bruce ; Memory systems, Overview: On the Topic of Memory Systems and Their Design Part I: Cache Ch. 1. An Overview of Cache Principles 2. Logical Organization 3. Management of Cache Contents 4. Cache Cohenrence 5. Implementation Issues 6. Cache Case Studies Part II: DRAM 7. Memory Systems Overview 8. DRAM Device: Basic Circuits and Architecture 9. DRAM System Signalling and Timing 10. DRAM Memory System Organization 11. Generic DRAM Memory Access Protocol 12. Evolution of DRAM Devices 13. DRAM Memory Controller 14. Memory System Design Analysis Part II: Disk 15. Overview of Disks 16. The Physical Layer 17. The Data Layer 18. Performance Issues and Design Tradeoffs 19. Drive Interface 20. Operational Performance Improvement 21. The Cache Layer, 23. Performance Testing 24. Storage Subsystems 25. Advanced Topics 26. Case Study Part IV: Cross-Cutting Issues 27. The Holistic Design of Memory Hierarchies 28. Analysis of Cost and Performance 29. Power and Energy 30. Reliability 31. Virtual Memory

Print version: Warner, Julian, 1955- ; Human information retrieval, Introduction -- Selection power and selection labor -- Description and search labor -- A labor theoretic approach -- Retrieval from full text -- A semantics for retrieval from full text -- A syntactics for retrieval from full text -- Semantics and syntactics for retrieval from full text -- Conclusion

Print version: Fenwick, Peter ; Introduction to Computer Data Representation, Cover; Title; EUL; Dedication; Contents; Foreword; Preface; Introduction; Chapter 01; Chapter 02; Chapter 03; Chapter 04; Chapter 05; Chapter 06; Chapter 07; Chapter 08; Chapter 09; Chapter 10; Chapter 11; Chapter 12; Bibliography; Index

Available in another form, Available in another form, ch. 1. High density magnetic data storage / Huimeng Wu, Li Zhang and Yanlin Song -- ch. 2. Optical data storage for the future / Wenfang Yuan and Yanlin Song -- ch. 3. High density electrical data storage / Guiyuan Jiang and Yanlin Song -- ch. 4. Nanoscale data storage / Jianchang Li and Yanlin Song