Abstract:
A data processing apparatus is operated to optimize the amount of information represented by entries in a database. The operation comprises forming prior distributions over the parameters of entries in the database and subjecting a potential entry for the database to a process of evaluation. The evaluation includes, (a) sampling parameters from the prior distributions, (b) calculating a utility function from the samples and from the potential entry, and (c) calculating a utility value from the utility function. The process of evaluation is repeated for each of a plurality of further potential entries and the potential entry having the highest calculated utility value is selected.

Description:
BACKGROUND OF THE INVENTION  
         [0001]    The present invention relates to a method and apparatus to optimize the amount of information represented by entries in a database. The invention has particular application to databases in which the entries represent both the attributes of individuals who access the database for advice and the advice given to those individuals.  
           [0002]    The Internet provides a channel for the supply of advice in a wide variety of fields, all of which require expert advice not normally available to most members of the general public. The advice available through this means can include advice on medical problems and advice on the purchase of financial products such as mortgages, pensions and shares.  
           [0003]    The expert advice available by accessing an automated system must necessarily be trustworthy. It is therefore important to the supplier of information and advice to obtain accurate predictions for the best advice to be given to any particular customer. Statistical prediction methods typically involve fitting a statistical model using some database or other data source. The database will have entries relating to the attributes of a collection of people or data subjects, real or imaginary, and the respective advice given by an expert or group of experts for each person or data subject.  
           [0004]    The database must necessarily be compiled to include a large number of people or data subjects and one or more experts are required to suggest a particular piece of advice (e.g. the purchase of a particular financial product) that is considered best suits the needs of each individual in the database. Advice for any future individual will then be made by reference to the database.  
           [0005]    The size of the database may be limited by including a selection only of all the possible entries that could be included in the database. Such a limitation in size is advantageous in improving the efficiency of the database. A database of limited size may however be less accurate in respect of the advice to be given to individuals accessing the database. The accuracy depends upon the optimal selection of the entries to be included in the database.  
         SUMMARY OF THE INVENTION  
         [0006]    It is an aim of the present invention to optimize the information represented by the entries in a database.  
           [0007]    According to the present invention, there is provided a method of operating a data processing apparatus to optimize the amount of information represented by entries in a database, the method comprising;  
           [0008]    forming prior distributions over the parameters of entries in the database;  
           [0009]    subjecting a potential entry for the database to a process of evaluation, the evaluation including;  
           [0010]    (a) sampling parameters from the prior distributions;  
           [0011]    (b) calculating a utility function from the samples and from the potential entry; and  
           [0012]    (c) calculating a utility value from the utility function;  
           [0013]    repeating the process of evaluation for each of a plurality of further potential entries; and  
           [0014]    selecting from the potential entries the one having the highest calculated utility value.  
           [0015]    Further according to the present invention, there is provided a data processing apparatus to optimize the amount of information represented by entries in a database, the apparatus comprising;  
           [0016]    means forming prior distributions over the parameters of entries in the database;  
           [0017]    evaluating means for subjecting a potential entry for the database to a process of evaluation, the evaluation including;  
           [0018]    (a) sampling parameters from the prior distributions;  
           [0019]    (b) calculating a utility function from the samples and from the potential entry; and  
           [0020]    (c) calculating a utility value from the utility function;  
           [0021]    means to cause the evaluating means to repeat the process of evaluation for each of a plurality of further potential entries; and  
           [0022]    selecting means to select from the potential entries the one having the highest calculated utility value. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0023]    The invention will now be described, by way of example, with reference to the accompanying drawings in which;  
         [0024]    [0024]FIG. 1 is a diagram of a database of variables relating to a set of customers of a bank,  
         [0025]    [0025]FIG. 2 shows data processing apparatus, according to the present invention, for predicting future values of the variables stored in the database of FIG. 1, and  
         [0026]    [0026]FIGS. 3 and 4 show operating steps in a method according to the present invention that employs the apparatus of FIG. 2. 
     
    
     DETAILED DESCRIPTION  
       [0027]    In FIG. 1, a database  10  concerns a set of customers of an enterprise such as a bank The database  10  is shown in two dimensions arranged along a vertical axis  11  and a horizontal axis  12 . The vertical axis  11  is subdivided to include fields for each of five customers A to E although it will be understood that in practice the database would have fields along the vertical axis for a multiplicity of customers far in excess of five. Each customer has attributes that include personal information such as age, type of job and salary and financial attributes such as the number and types of bank accounts, including investment accounts, deposit and current accounts and holdings of credit cards and finally the financial attributes relating to the type of pension arrangements, e.g. an occupational pension or a self-financed pension. The data in the database includes data values relating to quantifiable items and the data values relating to each customer may be thought of as a vector x containing the customer attributes. The database also includes a field including values relating to the advice given to each customer by one or more experts concerning the selection of a financial product. The financial product may be, for example, an insurance product, a further pension plan or an investment plan. The advice for each customer is represented as a string of numerical values identifying the details of the product advice.  
         [0028]    Whilst only five fields of attributes for each customer are shown in FIG. 1, it will be understood that, in practice, provision would be made for a number of attributes for each customer far in excess of five. Furthermore, whilst only one field is shown for the advice given to each customer, in practice, provision would be made for a number of fields so as to cover the range of products on which expert advice is obtainable.  
         [0029]    If sufficient storage space is available, the database may optionally include a time axis providing extra fields in which the customer attributes and the advice given are recorded as a succession of values at regular time intervals.  
         [0030]    In order to set up an effective automated advisor service to be accessed remotely, for example over the internet, it is necessary first to compile a large database of either real or imaginary people that includes their personal attributes in the manner discussed already with respect to FIG. 1. One or more experts must then go through the database and make particular recommendations in respect of the advice to enter in the advice field for each person in the database. Advice for any future individual accessing the database will then be available by reference to the advice already stored. Clearly, the individuals in the database should be chosen so as to allow for the best predictions to be made for any future user. The decisions on which individuals to include in the database may be tackled using Bayesian inference.  
         [0031]    Bayesian inference is characterized by the expression of uncertainty about all random quantities. In a Bayesian decision problem, the objective is to choose the “best” decision from a predefined set of possible decisions. The measure of which decision is the best is given by a utility function that attaches real-numbered values to all possible outcomes compared to the true state of the world. In the Bayesian approach, all knowledge about the true state of the world is held in the posterior distribution.  
         [0032]    The utility function provides a measure of the amount of information provided by a particular person, whether real or imaginary, in the database. Using a Bayesian approach, the expected utility is always taken with respect to the state of the world. A model is required that provides accurate predictions for all potential users and so a further expectation is taken with respect to the beliefs about the potential customers. The beliefs about the attributes for a potential user are held in the distribution, p(x), where x is a vector containing the user attributes. For given attributes x, then p(y|x,y*,x*) is the distribution representing the updated beliefs about the right advice to give a user exhibiting these attributes after observing x* and y*. Here x* and y* respectively represent the attributes of the people in the database 10 and the corresponding expert advice given to them. The two distributions p(x), and p(y|x,y*,x*) jointly hold the current beliefs about the state of the world. The beliefs about the advice an expert or group of experts would give to a person with attributes x* is expressed in the marginal distribution p(y*|x*).  
         [0033]    The expected utility has to be evaluated with respect to the beliefs about the state of the world and the next observation. Mathematically, this expectation can be expressed as follows;  
         [0034]    U(x*)=∫U(y,x,y*,x*)p(y|x, y*,x*)dy p(x)dx p(y*|x*)dy*  
         [0035]    where U(x *) is the expected utility value that should be maximized with respect to x* and U(y,x,y*,x*) is the utility function. A large number of observations will need to be made and they may be made at the same time, sequentially or in batches.  
         [0036]    The utility function may take a number of different forms since the invention is not limited to a particular form of utility function. The utility function must address the problem of inference and one example of a utility function that does this is the logarithmic divergency measure:  
         U        (     y   ,   x   ,     y   *     ,     x   *       )       =     log          p   (     y             x   ,     y   *     ,     x   *       )           p   (     y           x   )                                   
 
         [0037]    This logarithmic divergency measure can only be used in a fully Bayesian framework. The invention may be applied in a non-Bayesian context in which case a utility function would have to be elicited from expert knowledge and may be of a simple mathematical form e.g. a quadratic loss utility. The quadratic loss utility takes the form:  
         [0038]    U(y,x,y*,x*)=−(y−E(y|y*,x*)) 2    
         [0039]    As for the distributions p(y|x) and p(x), there are many possible ways to model these. One approach for p(y|x) is to use a non parametric model such as a Gaussian process prior or a basis function model. Modeling p(x) would require elicitation from experts and a mixture of normal distributions would be a particularly suitable form for p(x).  
         [0040]    Referring now to FIG. 2, a data processing apparatus is shown that provides a means for receiving the data in the database of FIG. 1. The data is entered by means of an I/O device  20  through an I/O port  21  for entry to a data bus  22 . A group of central processing units (CPU)  23  are controlled by an operating system  24  to control the entry of data onto the data bus. A random access memory  25  is connected to the I/O bus  22  to receive and store data supplied by the bus  22 .  
         [0041]    In response to the entry, by means of the data I/O device  20 , of the data representing the information in the different fields of the database  10  of FIG. 1, the parallel operating CPU&#39;s are programmed to enter the data into the memory  25 . The data in the memory  25  is a set of data values representing each of the variables for each of the customers included in the database and the advice entered for each customer. The data are available to be processed in a series of processing operations as will be described.  
         [0042]    [0042]FIG. 3 provides a graphical overview of how the invention is implemented. From the start  30 , a model for the distribution of the attributes of the population in the database is chosen in step  31 . In step  32  a model is chosen for the distribution p(y|x) and the prior distributions are chosen in step  33 . A utility function is chosen in step  34 . The choices represented by the steps  31  to  34  are entered into the data processing apparatus of FIG. 2 for subsequent use in operating on the data in the database stored in the memory  25 . Continuing with the process in FIG. 3, in step  35  a choice is made of an optimal example of an individual for inclusion in the database. In addition, the utility value of the chosen individual is evaluated. A test is made in step  36  to determine whether the evaluation in step  35  provides a positive change compared to previous evaluations. If the test in step  36  is positive, the current evaluation is stored and the process loops back to repeat steps  35  and  36 . When the process has evaluated all the optimal examples it will have maximized the expected utility. At this point the test in step  36  will be negative and the process is directed to the finish  37 .  
         [0043]    To maximize the expected utility as just explained with reference to FIG. 3, an appropriate optimizing algorithm is employed as will be further explained with reference to FIG. 4. The optimizing algorithm may employ simulated annealing or conjugate gradient methods. In choosing the examples for inclusion in the database, a sequential approach is used where the expected utility for the next example is maximized assuming the previous examples are fixed, i.e. choose x j * assuming that x 1 *, . . ., x j−1 * have already been chosen. The expected utility is maximized across all the possible values of the user attributes. At each iteration of the optimizing algorithm, the expected utility must be calculated. In some forms of prior distribution, some or all the integration can be performed analytically while in other situations none of the integrals can be calculated analytically and the integral must then be approximated numerically. A generic numerical strategy will now be described although it will be understood that the invention is not limited to this generic numerical strategy.  
         [0044]    The following equation is useful in explaining the numerical strategy:  
           P ( y|x,y*,x *)= p ( y|θ,x,y*,x *) p (θ y*,x *) dθ   (1)  
         [0045]    where the integration is over the appropriate range of values for θ. Substituting equation (1) into the expected utility and rearranging;  
               U        (     x   *     )       =     ∫       U        (     y   ,   x   ,     y   *     ,     x   *       )            p   (     y             x   ,     y   *     ,     x   *       )               yp        (   x   )                   xp   (       y   *               x   *     )               y   *                                 =     ∫       U        (     y   ,   x   ,     y   *     ,     x   *       )            p   (     y             θ   ,   x   ,     y   *     ,     x   *       )          p   (     θ               x   *     ,     y   *       )             θ               yp        (   x   )                   xp   (       y   *               x   *     )               y   *                                     =     ∫       U        (     y   ,   x   ,     y   *     ,     x   *       )            p   (     y             θ   ,   x   ,     y   *     ,     x   *       )          p   (     θ   ,       y   *               x   *     )             θ               yp        (   x   )                 x               y   *                                 =     ∫       U        (     y   ,   x   ,     y   *     ,     x   *       )            p   (     y             θ   ,   x   ,     y   *     ,     x   *       )          p        (   x   )            p   (       y   *               θ   ,     x   *       )          p        (   θ   )               θ             y             x               y   *                                               
 
         [0046]    The numerical technique used to calculate the expected utility consists of Monte Carlo sampling where for some probability distribution p(x)and function g(x);  
           ∫     i   =   1     N            g        (   x   )            p        (   x   )                          x         ≈       1   /   N          ∑     g        (     x   i     )                                 
 
         [0047]    for suitably large value of N and draws x 1 , . . . x N  from the distribution p(x).  
         [0048]    [0048]FIG. 4 is illustrative of the steps of operation employed in the apparatus of FIG. 2 to implement this numerical technique. From the start  41 , j is set to zero and a choice is made in step  42  of a new value of x j *. A value of θ i  is sampled from the distribution p(θ) in step  43  and a sample value y j * is taken from the distribution p(y ji *|θ i ,x j *) in step  44 . In step  45 , samples y 1i * . . . y (j−1)i  are taken from the distribution p(y ki *|θ i ,x k *). A value of x i  is sampled from the distribution p(x i ) in step  46  and a value of y i  is sampled from the distribution p(y i |θ i ,x i ) in step  47 . The sampled values and the current value of x* are used to calculate the expected utility value in the step  48 . A test is conducted in step  49  to determine Monte Carlo convergence and if no convergence has occurred the process loops round to repeat the sampling steps  43  to  47  and the calculation step  48 . Once convergence has occurred, the process moves on to step  50  to determine whether the algorithm has converged If not, the process loops round to a step  51  where the value of j is incremented according to the relation j=j+1 and the process is re-entered at the step  42  to choose another value for x*.  
         [0049]    What has been described is a method and apparatus for the optimization of the amount of information represented by the entries in the database. This has the benefit that a reduced size of database can be produced for a given level of accuracy.