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Database access for mobile devices has essentially two meanings: ondevice databases and connectivity to off-device databases. Two on-device database engines currently ship in all S60 phones: Symbian DBMS and SQLite. Symbian DBMS is a legacy engine with very limited SQL support but with a complete API and good performance. Support for SQLite16 on-device databases is offered via the Symbian SQL API and the usual SQLite C API. The latter is likely to be very attractive for porting applications from Linux because SQLite appears to be ubiquitously adopted as a database of choice in Linux MIDs. Finally, we have already mentioned the PAMP stack, which offers a MySQL port to Symbian, among other things. However, MySQL requires several times more memory than SQLite. Also, the MySQL port has not bene ted from the rigorous performance tests, tuning and optimizations that SQLite has been put through for the Symbian platform. For accessing off-device databases, developers must use APIs provided by database vendors. Oracle 10 g Mobile Client17 has been available for Symbian OS since 2006 and Sybase iAnywhere18 Symbian client is available and actively updated.

Resource and Rules Patterns Pattern Name Intent extended. A modeler should take this into consideratio n and separate the core objects from their representati ons. This process is aided by this pattern. Document Documents are used in all businesses, and they can cause a lot of confusion for modelers. One common problem is when a copy is made of a document. This raises the question: Is the copy another document, the same document, or a copy document linked to the original document Also, a document might exist in several different versions; the basic content and purpose of the document may be the same but the details Product Data Manageme nt pattern Geographic al Location pattern 223 Related Patterns Page

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Where distributions required ancillary parameters, we generate these at regular intervals in [1e-4,1e6], constrained by the legal range for the particular parameter Our benchmark is meant to probe only some key aspects of the accuracy of a statistical package It is not comprehensive, and some applications may require accuracy in distributions that we do not test, or beyond the p (or other parameter range) that is covered by our procedure For example, Kn sel (1995, 1998, 2002) u and McCullough (1999a,b) clearly examine a number of areas we do not: the extreme tails t < 10 12 (inaccuracies are reported for some distributions at very extreme tails, eg, 10 70 ), a number of discrete distributions, and a number of noncentral distributions.

The strength of the linkage between the support functions and the CI organization will determine the speed of execution of the improvements, so do not underestimate its signi cance Take the time to ensure that there is no duplication of efforts in your business During one restructuring assignment my rm worked on, the organization seemed to be in complete chaos with cost cutting initiatives, and I assumed the CEO was aware It appeared as though he had no control over the business unit leaders, because he was focused on the customers, while his leaders were all working on initiatives independently and not communicating to each other When we started the project, the PMO began listing all the projects currently being worked on, so we could understand where there were synergies with the formal restructuring project to gain leverage on resources, tools, and standard methodologies.

We believe our benchmarks are adequate for most normal use of a statistical package, but users with nonlinear models or simulations should use the libraries described previously to construct tests that better re ect their particular needs 326 Testing the Accuracy of Data Input and Output In the course of our research, we performed benchmark tests on over a dozen different packages and versions of packages An unexpected lesson from our testing is that loading data into a statistical software package from text or binary les a seemingly straightforward operation may produce unexpected results Most errors were obvious, but a few were subtle and caused statistical packages to fail the benchmarks Once we diagnosed and corrected the problem, what initially appeared as inaccuracies of algorithms were revealed as inaccuracies of data input.

TradeStation Strategy Performance Report - MyStrategy-1 @JY-Daily (4/28/94-4/11/02) (4/28/94-4/11/02)

It took months just to get the lists together, but when we did and identi ed the ones that the restructuring team should take over, there was huge pushback from the leaders When we went to the CEO for support, the CEO said, I don t want to stop what they are doing The problem was, they were taking three times as long to do it, they were not using the right nancial or statistical measurements, and, basically, they were not looking at the whole process, just a piece, so there were huge holes in processes and were not being addressed Merging the projects seemed like a no-brainer, but resistance can overpower logic We ended up duplicating a lot of what the company already was doing in the business unit because we were looking at the processes enterprise wide.

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