Data Mining_ Concepts and Techniques - Jiawei Han [117]
(c) The movement data may be sparse. Discuss how you would develop a method that constructs a reliable data warehouse despite the sparsity of data.
(d) If you want to drive from A to B starting at a particular time, discuss how a system may use the data in this warehouse to work out a fast route.
4.11 Radio-frequency identification is commonly used to trace object movement and perform inventory control. An RFID reader can successfully read an RFID tag from a limited distance at any scheduled time. Suppose a company wants to design a data warehouse to facilitate the analysis of objects with RFID tags in an online analytical processing manner. The company registers huge amounts of RFID data in the format of (RFID, at_location, time), and also has some information about the objects carrying the RFID tag, for example, (RFID, product_name, product_category, producer, date_produced, price).
(a) Design a data warehouse to facilitate effective registration and online analytical processing of such data.
(b) The RFID data may contain lots of redundant information. Discuss a method that maximally reduces redundancy during data registration in the RFID data warehouse.
(c) The RFID data may contain lots of noise such as missing registration and misread IDs. Discuss a method that effectively cleans up the noisy data in the RFID data warehouse.
(d) You may want to perform online analytical processing to determine how many TV sets were shipped from the LA seaport to BestBuy in Champaign, IL, by month, brand, and price_range. Outline how this could be done efficiently if you were to store such RFID data in the warehouse.
(e) If a customer returns a jug of milk and complains that is has spoiled before its expiration date, discuss how you can investigate such a case in the warehouse to find out what the problem is, either in shipping or in storage.
4.12 In many applications, new data sets are incrementally added to the existing large data sets. Thus, an important consideration is whether a measure can be computed efficiently in an incremental manner. Use count, standard deviation, and median as examples to show that a distributive or algebraic measure facilitates efficient incremental computation, whereas a holistic measure does not.
4.13 Suppose that we need to record three measures in a data cube: min(), average(), and median(). Design an efficient computation and storage method for each measure given that the cube allows data to be deleted incrementally (i.e., in small portions at a time) from the cube.
4.14 In data warehouse technology, a multiple dimensional view can be implemented by a relational database technique (ROLAP), by a multidimensional database technique (MOLAP), or by a hybrid database technique (HOLAP).
(a) Briefly describe each implementation technique.
(b) For each technique, explain how each of the following functions may be implemented:
i. The generation of a data warehouse (including aggregation)
ii. Roll-up
iii. Drill-down
iv. Incremental updating
(c) Which implementation techniques do you prefer, and why?
4.15 Suppose that a data warehouse contains 20 dimensions, each with about five levels of granularity.
(a) Users are mainly interested in four particular dimensions, each having three frequently accessed levels for rolling up and drilling down. How would you design a data cube structure to support this preference efficiently?
(b) At times, a user may want to drill through the cube to the raw data for one or two particular dimensions. How would you support this feature?
4.16 A data cube, C, has n dimensions, and each dimension has exactly p distinct values in the base cuboid. Assume that there are no concept hierarchies associated with the dimensions.
(a) What is the maximum number of cells possible in the base cuboid?
(b) What is the minimum number of cells possible in the base cuboid?
(c) What is the maximum number of cells possible (including both base cells and aggregate cells) in the C data cube?
(d) What is the minimum number of cells possible in C?
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