Beautiful Data
(Sprache: Englisch)
In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging...
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Klappentext zu „Beautiful Data “
In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.With Beautiful Data, you will:Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web
Learn how to visualize trends in urban crime, using maps and data mashups
Discover the challenges of designing a data processing system that works within the constraints of space travel
Learn how crowdsourcing and transparency have combined to advance the state of drug research
Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data
Learn about the massive infrastructure required to create, capture, and process DNA dataThat's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:Nathan Yau
Jonathan Follett and Matt Holm
J.M. Hughes
Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava
Jeff Hammerbacher
Jason Dykes and Jo Wood
Jeff Jonas and Lisa Sokol
Jud Valeski
Alon Halevy and Jayant Madhavan
Aaron Koblin with Valdean Klump
Michal Migurski
Jeff Heer
Coco Krumme
Peter Norvig
Matt Wood and Ben Blackburne
Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen
Lukas Biewald and Brendan O'Connor
Hadley Wickham, Deborah Swayne, and David Poole
Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza
Toby Segaran
In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.
With Beautiful Data, you will:
- Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web
- Learn how to visualize trends in urban crime, using maps and data mashups
- Discover the challenges of designing a data processing system that works within the constraints of space travel
- Learn how crowdsourcing and transparency have combined to advance the state of drug research
- Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data
- Learn about the massive infrastructure required to create, capture, and process DNA data
That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:
- Nathan Yau
- Jonathan Follett and Matt Holm
- J.M. Hughes
- Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava
- Jeff Hammerbacher
- Jason Dykes and Jo Wood
- Jeff Jonas and Lisa Sokol
- Jud Valeski
- Alon Halevy and Jayant Madhavan
- Aaron Koblin with Valdean Klump
- Michal Migurski
- Jeff Heer
- Coco Krumme
- Peter Norvig
- Matt Wood and Ben Blackburne
- Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen
- Lukas Biewald and Brendan O'Connor
- Hadley Wickham, Deborah Swayne, and David Poole
- Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza- Toby Segaran
With Beautiful Data, you will:
- Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web
- Learn how to visualize trends in urban crime, using maps and data mashups
- Discover the challenges of designing a data processing system that works within the constraints of space travel
- Learn how crowdsourcing and transparency have combined to advance the state of drug research
- Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data
- Learn about the massive infrastructure required to create, capture, and process DNA data
That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:
- Nathan Yau
- Jonathan Follett and Matt Holm
- J.M. Hughes
- Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava
- Jeff Hammerbacher
- Jason Dykes and Jo Wood
- Jeff Jonas and Lisa Sokol
- Jud Valeski
- Alon Halevy and Jayant Madhavan
- Aaron Koblin with Valdean Klump
- Michal Migurski
- Jeff Heer
- Coco Krumme
- Peter Norvig
- Matt Wood and Ben Blackburne
- Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen
- Lukas Biewald and Brendan O'Connor
- Hadley Wickham, Deborah Swayne, and David Poole
- Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza- Toby Segaran
Inhaltsverzeichnis zu „Beautiful Data “
From the contents:Chapter 1 Seeing Your Life in DataPersonal Environmental Impact Report (PEIR)your.flowingdata (YFD)Personal Data CollectionData StorageData ProcessingData VisualizationThe PointHow to ParticipateChapter 2 The Beautiful People: Keeping Users in Mind When Designing Data Collection MethodsIntroduction: User Empathy Is the New BlackThe Project: Surveying Customers About a New Luxury ProductSpecific Challenges to Data CollectionDesigning Our SolutionResults and ReflectionChapter 3 Embedded Image Data Processing on MarsAbstractIntroductionSome BackgroundTo Pack or Not to PackThe Three TasksSlotting the ImagesPassing the Image: Communication Among the Three TasksGetting the Picture: Image Download and ProcessingImage CompressionDownlink, or, It's All Downhill from HereConclusionChapter 4 Cloud Storage Design in a PNUTShellIntroductionUpdating DataComplex QueriesComparison with Other SystemsConclusionAcknowledgmentsReferencesChapter 5 Information Platforms and the Rise of the Data ScientistLibraries and BrainsFacebook Becomes Self-AwareA Business Intelligence SystemThe Death and Rebirth of a Data WarehouseBeyond the Data WarehouseThe Cheetah and the ElephantThe Unreasonable Effectiveness of DataNew Tools and Applied ResearchMAD Skills and CosmosInformation Platforms As DataspacesThe Data ScientistConclusionChapter 6 The Geographic Beauty of a Photographic ArchiveBeauty in Data: GeographVisualization, Beauty, and TreemapsA Geographic Perspective on Geograph Term UseBeauty in DiscoveryReflection and ConclusionAcknowledgmentsReferencesChapter 7 Data Finds DataIntroductionThe Benefits of Just-in-Time DiscoveryCorruption at the Roulette WheelEnterprise DiscoverabilityFederated Search Ain't All ThatDirectories: PricelessRelevance: What Matters and to Whom?Components and Special ConsiderationsPrivacy ConsiderationsConclusionChapter 8 Portable Data in Real TimeIntroductionThe State of the ArtSocial Data NormalizationConclusion: Mediation via GnipChapter 9
... mehr
Surfacing the Deep WebWhat Is the Deep Web?Alternatives to Offering Deep-Web AccessConclusion and Future WorkReferencesChapter 10 Building Radiohead's House of Cards How It All StartedThe Data Capture EquipmentThe Advantages of Two Data Capture SystemsThe DataCapturing the Data, aka "The Shoot"Processing the DataPost-Processing the DataLaunching the VideoConclusionChapter 11 Visualizing Urban Data...
... weniger
Autoren-Porträt von Jeff Hammerbacher, Toby Segaran
Toby Segaran is the author of Programming Collective Intelligence, a very popular O'Reilly title. He was the founder of Incellico, a biotech software company later acquired by Genstruct. He currently holds the title of Data Magnate at Metaweb Technologies and is a frequent speaker at technology conferences.
Jeff Hammerbacher is the Vice President of Products and Chief Scientist at Cloudera. Jeff was an Entrepreneur in Residence at Accel Partners immediately prior to joining Cloudera. Before Accel, he conceived, built, and led the Data team at Facebook. The Data team was responsible for driving many of the statistics and machine learning applications at Facebook, as well as building out the infrastructure to support these tasks for massive data sets. The team produced several academic papers and two open source projects: Hive, a system for offline analysis built above Hadoop, and Cassandra, a structured storage system on a P2P network. Before joining Facebook, Jeff was a quantitative analyst on Wall Street. Jeff earned his Bachelor's Degree in Mathematics from Harvard University.
Bibliographische Angaben
- Autoren: Jeff Hammerbacher , Toby Segaran
- 382 Seiten, Masse: 17,9 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: O'Reilly Media
- ISBN-10: 0596157118
- ISBN-13: 9780596157111
- Erscheinungsdatum: 01.08.2009
Sprache:
Englisch
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