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Since areas represent probabilities, using the area of a triangle, we nd that P(1/2 X 3/2) = 3/4, so the sums do in fact cluster around their expected value If we increase the number of spins to 3 and record the sum, it can be shown that the probability density function for X = X1 + X2 +X3 is 1 x2 for 0 x 1 2 3 2 3 f3 (x) = for 1 x 2 x 4 2 1 (x 3)2 for 2 x 3 2 This is also a probability density function, although nding the total area, or nding probabilities by nding areas, is usually done using calculus We can nd here, for example, that P(1/2 x 5/2) = 23/24 Probabilities, or areas, are now dif cult for us to nd and we cannot do this by simple geometry However, we will soon nd a remarkable approximation to this probability density function that will enable us to determine areas and hence probabilities A graph of f3 (x) is shown in Figure 85 The graph in Figure 85 resembles a bell-shaped or normal curve that occurs frequently in probability and statistics We have encountered this curve before

Class CContactItemFieldDef public CArrayFixFlat<TUid> De ned in cntfield.h This class represents an array of eld types. It is used to specify a subset of elds when searching. CContactItemFieldDef is derived from CArrayFixFlat<TUid> and adds no methods apart from a constructor. Constructor inline CContactItemFieldDef() : CArrayFixFlat<TUid>(8) This constructor creates the CContactItemFieldDef object, with an array granularity of 8.

If we were to continue adding spins, we would nd that the graphs come closer and closer to a normal curve This is an illustration of the central limit theorem We will discuss the normal curve rst and then state the central limit theorem

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controlled vocabulary, classi cation codes, publication types etc The subject headings are taken from hierarchical listings of preferred terms that describe the topics covered by the database The Inspec thesaurus, for example, contains 9000 preferred terms As might be expected given Inspec s focus on physics, electronics and computing, the Inspec thesaurus provides a rich set of terms covering these areas and a much higher level of description of more general topics ABI/INFORM covers the whole breadth of business and management issues and has a vocabulary of about 8000 preferred terms The schemas used by ABI/ INFORM and Inspec will provide the basis for the initial set of topics to be used to categorise documents semantically, see the discussion of topic hierarchies in 7 However, these schemas are similar but different and will need to be merged.

Class TStorageType De ned in cntdef.h This class is a TUInt that represents the storage type of a eld. The allowed values are de ned in cntdef.hrh: KStorageTypeText (used by CContactTextFields) KStorageTypeStore (used by CContactStoreFields) KStorageTypeContactItemId (used by CContactAgentFields) KStorageTypeDateTime (used by CContactDateFields) Note that numeric elds are not supported. Numbers (e.g. telephone numbers) are stored in the database using text elds.

The normal probability distribution is a bell-shaped curve that is completely speci ed by its mean and its standard deviation Its probability density function is

Moreover, we believe that these schemas will not be adequate to generate a suf ciently ne-grained set of topics We are taking the set of topics generated from the database schemas and re ning this set with ontology learning software developed in SEKT In addition we will incorporate data already marked up with the RSS standard used for news syndication The end result will be a topic hierarchy, that is some topics will be de ned as sub-topics of other topics Thus knowledge management might be a sub-topic of information management , and at the same time have communities of interest as its own sub-topic It is hoped to add documents from other sources, for example the Web, which do not come with prede ned metadata In this case the associated topics need to be inferred automatically from the text.

1 1 2 f (x) = e 2 (x ) , 2

Class CContactFieldStorage De ned in cntfldst.h This abstract class is the base class for the different types of eld classes. Member Methods virtual TBool IsFull() const=0 This method returns ETrue if the eld contains some data, EFalse otherwise.

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Addition of material to the library might be prompted by a user nding a page of interest on the intranet or Internet, and wanting to share this with colleagues We call this jotting Alternatively, SEKT has developed a focussed crawling facility which, given a seed page, can search the Internet for related pages Again, these additional pages need to be categorised against the library topics..

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