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A lot has happened in the cellular world since its original introduction in 1984 In 1984 when cellular communications became the hot button in the industry, all systems used analog radio transmissions Many reasons were used to justify the cellular networks These included very limited service areas, where you just could not get service where you wanted or needed it Poor transmission haunted the operators because of the nature of the radio systems at the time Users experienced excessive call setup delays Heavy demand and limited channels were some of the more common problems in an operating area Analog Cellular radio systems used Frequency Division Multiple Access (FDMA), which is an analog technique designed to support multiple users in an area with a limited number of frequencies Analog radio systems use analog input, such as voice communications Because these systems were designed around voice applications, no one had any thought of the future transmission of data, fax, packet data, and so on, from a vehicle Back then, no one was sure what the acceptance rate would be Currently, there are approximately 63 million cellular users in the United States Approximately 100 150,000 new users sign up every month Estimates are that four of five new telephones sold today are wireless telephones Therefore, acceptance has become a non-issue The new problem is not one of acceptance, but of retaining users The churn ratio has been as high as 15 20% The answer to the retention problem lies in packaging the service with the handset By offering service plans with a usage fee of $10-15/minute, the acceptance rate skyrocketed The cellular industry is still primarily running on an analog backbone Estimates are that 90 95% of the United States has analog coverage, whereas the digital counterpart to the cellular networks only covers between 50 55% Dual mode telephones have become the salvation for cellular providers, because they would not be able to sustain their customer base without this offering.

If your introduction to robot combat has come only from watching TV, you need to know much more before you begin building your first bot First, it s a good idea to get familiar with the current rules for whatever competition you have in mind

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.

The cellular operators build out their networks to provide coverage in certain geographically bounded areas This poses the following dilemmas for the providers:

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The carriers need users (more) to generate higher revenues to pay off their investment They must continue the evolution from analog to digital systems, allowing more efficient bandwidth use Security and protection against theft is putting pressure on the carriers and users alike (cellular carriers lost over $300 million USD in 1997 through fraud and close to $500 million in 1998)

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