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Big Data A Workshop Report (2012) / Chapter Skim
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2 First-Day Presentations
Pages 3-8

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 3...
... The IBM tools for such analyses are refined or modest modifications of rapidly evolving and widely available web technologies so that processing developments using Java, Linux, and XML continue without direct investments from IBM. Using the open source environment is both economical and ensures that efforts continuously remain at the cutting edge of technology.
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From page 4...
... This situation differs from previous eras in which big data tended to be generated as a result of economic activities, wars, and science. The individualization of big data stems primarily from the social networking phenomenon but is also enabled by the credit and debit card industries and the logistics industry, particularly point-of-sale applications.
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From page 5...
... Jetlore is capable of taking in unstructured data from social networking sites and producing detailed analysis. Sadikov noted that one of the challenges is in natural language processing, particularly in using context to recognize entities and relationships in unstructured texts written in less than grammatically correct language.
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From page 6...
... BIG DATA FEEDS -- 2 John Marion of Logos Technologies John Marion of Logos Technologies described a persistent surveillance system that evolved from a prototype developed at Lawrence Livermore National Laboratory into a series of systems that have been operationally deployed. The fundamental technology is a group of high-resolution image sensors mounted on a gimbaled platform carried aboard aircraft.
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From page 7...
... As a result, there is the possibility of false and malicious data being planted in U.S. systems (e.g., false data on stock movements that can drive capital markets or data that can start a panic about a transmittable disease or contamination of food)
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From page 8...
... The firm's LikeNess search engine, which draws data from various sources of information, such as social networking sites, applies machine learning techniques to tease out patterns that are then used to establish recommendations for users, generating a small profit per transaction. Ness Computing describes what it does in the following terms: "Ness creates products that connect our users with new experiences." Its flagship product, Ness, is an application that runs on mobile phones to provide users with restaurant recommendations.
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Key Terms

  • data storage
  • data fusion
  • social networking
  • ness computing
  • sensor data

  • data world
  • social media
  • apache software
  • software foundation
  • representational data

  • data challenges
  • data include
  • data types
  • workshop participant
  • fusion challenges

  • storage architecture
  • massive amounts
  • gladwin noted
  • mathematical techniques
  • challenges related

  • networking sites
  • paul twohey
  • gilman louie
  • previous eras
  • draws data

  • data stems
  • data communication
  • false data
  • dirty data
  • data ingest


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
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