IS 4410- Exam 3

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IS 4410- Exam 3
2011-11-23 10:51:18

Everything I need to Know about chapter 9
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  1. Alerts 346
    Message delivered via email or phone whenever a particular event occurs.
  2. Business Intelligence 320
    Information that Contains Patters, relationships, and trends.
  3. business intelligence application 322
    the use of a tool on a particular type of data for a particular purpose.
  4. Business intelligence application server 344
    A computer program that deliver BI application results in a variety of formats to various devices for consumption by BI users
  5. Business intelligence system 321
    An informations system, having all five IS components, that provides the right information, to the right user, at the right time.
  6. Business intelligence tool 321
    A computer program that implements a particular BI technique. BI tools include reporting tools, data-mining tools, and knowledge-management tools.
  7. Clickstream data 338
    E-commerce data tha describes a customer's clicking behaviour. Such data includes everything the customer does at the Web site.
  8. Cluster Analysis 329
    An unsupervised data-mining technique whereby statistical techniques are used to identify groups of entities that have similar characteristics. A common use for cluster analysis is to find groups of similar customers in data about customer orders and customer demographics.
  9. Confidence 331
    In market-basket terminology, the probability estimate that two items will be purchased together.
  10. Cross-Selling 331
    The sale of related products: salespeople try to get customers who buy product X to also buy product Y.
  11. Curse of dimensionality 339
    The more attributes there are, the easier it is to build a data model that fits the sample data but that is worthless as a predictor.
  12. Data Marts 337
    Facilities that prepare, store, and manage data for reporting and data mining for specific business functions.
  13. Data Mining 327
    The application of statistical techniques to find patterns and relationships among data for classification and prediction.
  14. Data-mining tools 321
    Tools that process data using statistical techniques, many of which are mathematically sophisticated.
  15. Data warehouses 337
    Facilities that prepare, stor, and manage data specifically for reporting and data mining.
  16. Decision tree 332
    A hierarchical arrangement of criteria for classifying customers, items, and other business objects.
  17. Dimension 326
    A characteristic of an OLAP measure. Purchase date, customer type, customer location, and sales region are examples of dimensions.
  18. Dirty data 338
    Problematic data. Examples are a value of B for customer gender and a value of 213 for customer age. Other examples are a value of 999-999-9999 for a U.S. phone number, a part color of green, and an email address of All these values are problematic when data mining.
  19. Drill down 326
    With an OLAP report, to further divide the data into more detail.
  20. Exabyte 320
    10^18 bytes
  21. Exception alert 346
    A message that notifies a system user of an out-of-the-ordinary-exceptional-event.
  22. Expert systems 342
    Knowledge-sharing system that is created by interviewing experts in a given business domain and codifying the rules used by those experts.
  23. Granularity 338
    The level of detail in data. Customer name and account balance is large-granularity data. Customer name, balance, and the other order details and payment history of every customer order is smaller granularity.
  24. If...Then... 333
    Format for rules derived from a decision tree (data mining) or by interviewing a human expert (expert systems)
  25. Indexing 341
    The most important content function of knowledge-management applications, which uses keyword search to determine whether content exists and provides a link to its location.
  26. Knowledge management(KM) 340
    The process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others who need it.
  27. Knowledge-management tools 322
    Computer applications used to store employee knowledge and to make that knowledge available to employees, customers, vendors, and other who neet it. The source of KM tools is human knowledge, rather than recorded facts and figures.
  28. Lift 332
    In market-basket terminology, the ratio of confidence to the base probability of uying an item. Lift whows how much the base probability changes when other products are purchased. If the lift is greater than 1, the change is positive: if it is less than 1, the change is negative.
  29. Market-basket analysis 331
    A data-mining technique for determining sales patterns. A market-basket analysis shows the products that customers ten to buy together.
  30. Measue 326
    The data item of interest on an OLAP report. It is the item that is to be summed, averaged, or otherwise processed in the OLAP cube. Total sales, average sales, and average cost are examples of measures.
  31. Neural networks 330
    A popular supervised data-mining technique used to predict values and make clsssifications such as "good prospect" or "poor prospect.."
  32. OLAP cube 326
    A presentation of an OLAP measure with associated dimensions. The reason for this term is that some products show these displays using three axes, like a cube in geometry. Same as OLAP report
  33. OLAP servers 327
    Computer server running software that performs OLAP analysis. an OLAP server reads data from an operational database, performs preliminary calculations, and stores the results of those calculations in an OLAP databse.
  34. Online analytical processing (OLAP) 326
    A dynamic type of reporting system that provides that ability to sum, count, average, and perform other simple arithmetic operations on groups of data. Such reports are dynamic becase users can change the format of the reports while viewing them.
  35. Petabyte 320
    10^15 bytes
  36. Portal servers 345
    Program similar to a web server, but with a customizable user interface.
  37. Pull (results) 344
    Reports that are produced on request by users.
  38. Push (results) 346
    Reports that are published on a scheduled basis to a list of subscribers.
  39. Real Simple Syndication (RSS) 341
    feed- A data soure that transmits using an RSS standard. The output of an RSS feed is consumed by an RSS reader.
  40. Regression analysis 330
    A type of supervised data miing that estimates the values of parameters in a linear equation. Used to determine the relative influence of variables on an outcome and also to predict future values of that outcome.
  41. Report server 346
    A special case of a business intelligence applications server that serves only reports.
  42. Reporting application 322
    A business intelligence applications that produces information from data by applying reporting tools to that data.
  43. Reporting system 322
    A business intelligence system that delivers reports to authorized users at appropriate times.
  44. Reporting tools 321
    A type of business intelligence tool, these programs read data from a variety of sources, process that data, format the data into structured reports, and deliver those reports to the users who need them.
  45. RFM analysis 323
    A way to of analyzing and ranking customers according to the recency, frequency, and monetary value of their purchases.
  46. RSS reader 342
    A program by which users can subscribe to magazines, blogs, Web sites, and other content sources: the reader will periodically check the sources, and if there has been a change since the last check, it will place a summary of the change and a link to the new content in an inbox.
  47. Semantic security 348
    Concerns the unintended release of protected information through the release of a combination of reports or documents that are independently not protected.
  48. Supervised data mining 330
    A form of data mining in which data miners develop a model prior to the analysis and apply statistical techniques to data to estimate values of the parameters of the model.
  49. Support 331
    In market-basket terminology, the probability that two items will be purchased together.
  50. Unsupervised data mining 329
    A form of data mining whereby the analysts do not create a model or hypothesis before running the analysis. Instead, they apply the data-mining technique to the data and observe the results. With this method, analysts create hypotheses after the analysis to explain the patterns found.