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The usual method for determining and acting on what the user has selected requires the getSelection() function. getSelection() returns 2-byte offsets that mark the beginning and end of the user s selection. The difficulty appears when you try to extract the character data that corresponds to those byte offsets. The offsetsToNode() function, which is used to make this translation, expands the offsets to the nearest tag the innerHTML, in other words. For example, the following function attempts to get the user s selection and report it in an alert:

In 1astminute.com they sell gifts, travel tickets, and other items for last minute shoppers who want to purchase these items at a very short notice. Generally, when one purchases an item at a very short notice (e.g. travel), he often pays a premium, which is an extra amount for the convenience of booking the travel at the last minute. Now, this means that the air ticket is likely to cost much more than if he had purchased it some time before travelling and made use of different discounts or promotions. The producers of the web site 1astminute.com realized that there are groups of customers who make these purchases at the last minute and feel some degree of angst at having to pay the premium for doing this shopping at the last minute. On the other hand, you will find that you may have sellers, e.g. airline companies, that have empty seats at the last minute which they are unable to fill. So, what 1astminute.com does is bring together travellers who want to book at the last minute and an airline which has got spare capacity at the last minute, and allow the former to buy from the latter at the last minute. In this situation, the purchaser may get his airline ticket at a reduced price. So, there is a win-win situation for both the purchaser and the seller. This is a unique kind of specialization. It is very difficult to do this unless one utilizes the internet to carry out this kind of specialization. Now, let us go on to discuss individual case studies under these two categories, namely specialization by product line and specialization by function. Let us start by considering specialization by product lines.

The results of the subquery are stored in a transient table. Then the value of the id column is checked against this table for each row of the outer query to determine if that row should be included in the result set. Sometimes subqueries in the FROM clause of a query result in a transient table. This is not always the case because SQLite tries very hard to convert subqueries in the FROM clause into a join that does not use subqueries. SQLite uses the term attening to describe the conversion of FROM clause subqueries into joins. Flattening is an optimization that makes queries run faster. In some cases, attening cannot occur. When the attening optimization is inhibited, the results of the subqueries are stored in transient tables and then a separate query is run against those transient tables to generate the nal results. Consider the following schema and query:

Example 4.24 Consider again the (7,4) Hamming code shortened to a (5,2) code. The error pattern at position xn-'-' = x4 appearing on the right-hand side as x2n-z-k-1 = x7. The syndrome to watch for is = R , ( , ) [ ~ ~ I1. =

Baker, BN, Fisher, D and Murphy DC (1988) Project management in the public sector: success and failure patterns compared to private sector projects In DI Cleland & WR King (Eds) Project Management Handbook, 2nd edition Van Nostrand Reinhold: New York, pp 920 934 Banxia (1999) Decision Explorer software is supplied by Banxia Software, 141 St James Road, Glasgow G4 0LT It runs in the Windows environment Barlas Y (1996) Formal aspects of model validity and validation in System Dynamics System Dynamics Review 12(3): 183 210 Barnes M (1988) Construction project management Project Management 6: 69 79 Barnes NML and Wearne SH (1993) The future for major project management International Journal of Project Management 11(3): 135 242 Bernstein PL (1996) Against the Gods John Wiley: New York Berny J (1989) A new distribution function for risk analysis Journal of the Operational Research Society 40: 1121 1127.