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When data warehousing was originally born, the star schema was the data warehouse In fact, it s still often called the relational data warehouse There s nothing wrong with this at all, and many companies query directly against the relational warehouse The star schema is designed for the speed of retrieval, so running reports against it is certainly acceptable One thing that star schemas may not have, however, are summary levels Imagine that a particular schema has a daily grain, and that there are two billion fact records in the fact table, representing 4 years of sales If a customer wants to see yearly sales totals, this requires performing a Sum across all two billion records, summing approximately 500 million records at a time in order to get the values for each year This can be an expensive operation on large warehouses, especially when there are many users of the system One solution was to preaggregate the data in the relational system Remember that in a star schema the concern with normalization is gone, so adding calculated data is acceptable Adding preaggregated data could be accomplished by setting some dummy values For example, if the Year was 2007 but the Quarter was 5, the Month was Z and the Day was 99, that record represented the year total for 2007 If the Year was 2007, the Quarter was 1, the Month was Z and the Day was 99, that record represented the first quarter totals for 2007 This magic number approach could get messy quickly, so many people opted to build summary tables There was a year table, a quarter table, a month table, and then the real fact table at the daily grain This had the advantage of not having messy codes, but queries had to be much smarter and know when to get different values from different tables In addition, if there were monthly totals for each individual product and customer, even the summary tables could be quite large and require a significant amount of extra disk space This is where cube building engines come in Cubes are simply a different way of storing data Different engines work differently, but the focus here is on SQL Server Analysis Services, also called just Analysis Services, SSAS, or AS Analysis Services does several things when creating a cube First, it reads the data from the dimension tables and the fact table It stores the dimension and fact records in a binary format, completely independent of the relational database engine As it stores records, it compresses them so that less disk storage is required How much it compresses them depends on many factors, but a general rule of thumb is that a cube will normally take up about 30 40 percent of the size of the relational warehouse Therefore, a 1TB relational data warehouse would translate to a cube of approximately 300 400GB.

Leak (in dBmV) 39 35 30 20 15 35 40 35 32 30

.

Leak (in mV/m) 20 305 545 172 306 305 172 306 432 545

.

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When it comes to private-sector actions, the Constitution embodies what is essentially a laissez-faire, or hands-off spirit With respect to the conduct of private individuals, the Supreme Court has not found a constitutional privacy right in personal information given voluntarily by an individual to private parties This reluctance to find such a privacy right bears on private-sector use of biometrics because biometric identifiers may be categorized as personal information that an individual, or the data subject, gives voluntarily to private parties, or the data collectors Generally, as a matter of law, a private party in possession of information has the right to disclose it Two landmark cases demonstrate the Supreme Court s view In Smith v Maryland, decided in 1979, the defendant claimed that information in the form of telephone numbers he dialed from his home telephone (which is known as a pen register) could not be turned over to the police without a search warrant Mr Smith desperately wanted his pen register records excluded as evidence because they helped establish that he had been stalking a woman Rejecting this argument, the Supreme Court noted that it consistently has held that a person has no legitimate expectation of privacy in information he voluntarily turns over to third parties 28 United States v Miller, decided in 1976, involved a bootlegger s private financial records, which his bank gave to United States Treasury agents Mr Miller, the bootlegger, failed in his attempt to have this evidence excluded He wanted his financial records excluded as evidence because the government could use these records to prove that Miller was evading taxes The Supreme Court found that the bootlegger had no expectation of privacy in the bank records, reasoning that The depositor takes the risk, in revealing his affairs to another, that the information will be conveyed by that person to the Government 29 The records could not therefore be considered confidential communications because Miller had voluntarily conveyed them to the bank in the ordinary course of business Analogizing to the private-sector s use of biometrics, the Supreme Court s reasoning in Smith and Miller suggests that the Court would be reluctant to find a reasonable expectation of privacy in biometric identification information provided in connection with a private-sector activity Rather, the Court would likely view the biometric identification information as having been voluntarily provided by.

Leak 2 400 930 2970 29,584 93,636 930 296 930 1866 2970 134,512

Smith v Maryland, 442 US 735 (1979) United States v Miller, 425 US 435 (1976)

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