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A service program for execution of a service is divided into a server control module for execution of the service in a server, a display control module for providing a display control in a client, an initial setting module defining a method of acquiring a service module prepared based on the type of

A service program for execution of a service is divided into a server control module for execution of the service in a server, a display control module for providing a display control in a client, an initial setting module defining a method of acquiring a service module prepared based on the type of client and required for the client, and a common module operable in the operating environment of the server and the client both. When a shortage of resources is detected in the client during execution of this service program then a portion of the common modules is executed in the server, and when a shortage of resources is detected in the server then a portion of the common modules is executed in some other server.

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A service program for execution of a service is divided into a server control module for execution of the service in a server, a display control module for providing a display control in a client, an initial setting module defining a method of acquiring a service module prepared based on the type of

A service program for execution of a service is divided into a server control module for execution of the service in a server, a display control module for providing a display control in a client, an initial setting module defining a method of acquiring a service module prepared based on the type of client and required for the client, and a common module operable in the operating environment of the server and the client both. When a shortage of resources is detected in the client during execution of this service program then a portion of the common modules is executed in the server, and when a shortage of resources is detected in the server then a portion of the common modules is executed in some other server. ified ranges of the multiple dimensions by integrating a functional representation based on the probablity distribution of the adjusted cluster model over the ranges. 2. The method of claim 1 wherein the functional representation excludes certain data records by identifying outlying data records as not falling within a data cluster. 3. The method of claim 2 wherein the step of determining a sum or a count total adds contributions from the outlying data records to the results of the integrating step. 4. The method of claim 1 wherein the step of providing the functional representation is performed by a clustering step that results in a cluster model having a cluster number K and wherein said cluster number K is adjusted when modifying the cluster model. 5. The method of claim 1 wherein the cluster model is adjusted by comparing the functional representation of the cluster model with data read from the database and improving the functional representation based on the cluster model by increasing the number of clusters based on said comparison. 6. Apparatus for querying a database containing data records comprising: a) means for providing a functional representation of data clustering for multiple data records stored on a database; b) means for adjusting a fit between the functional representation and the data in the database to provide a more accurate functional representation of the data clustering by segmenting clusters into cluster segments and comparing an expected data density in the cluster segments based on the initial cluster model with an observed data density of data gathered from the database; c) means for selecting ranges over dimensions of the data for determining a sum or a count of data records from the database falling within the ranges; and d) means for determining the sum or the count of data records from the database that fall within the selected ranges by integrating the functional representation from each cluster of a multiple number of clusters over the ranges. 7. The apparatus of claim 6 wherein the means for determining calculates both the sum and the count and further includes means for determining a ratio to find the average value of data records over the ranges. 8. The apparatus of claim 6 additionally comprising a user input for controlling accuracy parameters for making the comparison between the cluster model and sampled data. 9. Data mining apparatus for querying a database containing data records comprising: a ) a memory device for storing a database comprising multiple data records organized into data fields having a dimension n for storing data record items; b) a computer having one or more processing units for executing a stored computer program, said computer including a rapid access memory store; and c) an interface for coupling the memory device for storing the database to the computer to allow records to be retrieved from the database; wherein d) said computer executing a stored program having software components including i) a component for providing a first data clustering model for multiple number of data records stored on a database, sampling data from the database, and producing an updated cluster model based on a comparison between the first data clustering model and the sampled data; ii) a component for selecting ranges over dimensions of the data for determining a sum or a count of data records from the database falling within the ranges; and iii) a component for integrating a functional representation based on the updated cluster model from each cluster over the ranges to determine the sum or the count of data records from the database that fall within the selected ranges; wherein an amount of the computer's rapid access memory store is allocated for storing outlying data records determined not to fit within any of the K clusters which are counted separately when determining the sum or the count. 10. The apparatus of claim 9 wherein execution of the stored program provides said functional representation by clustering data into a number of clusters K, and wherein said clustering step is performed by allocating an amount of the computer's rapid access memory store for storing a cluster model. 11. A method of querying data records containing continuous attributes that are stored in the database; said method comprising the steps of: a) defining a data cube over which to query the database by selecting ranges over one or more of the continuous attributes of the data records in a database; b) providing a functional representation of data clustering from data records stored in the database; said functional representation identifying a distribution probability of said data records by the steps of: i) performing an intial clustering of data to provide an initial cluster model; ii) determining regions of an attribute data space where the initial cluster model does not accurately characterize data in the database; and iii) reclustering the data to produce a second cluster model which more accurately characterizes the data of the regions of the attribute data space found to not accurately charactaerize data in the database; and c) determining a count of data records from the database that fall within the data 16 cube by integrating the functional representation of the second cluster model over the selected rangs. 12. The method of claim 11 additionally comprising the step of summing attribute contributions by integrating the value of the functional representation over the data cube. 13. The method of claim 11 wherein the clustering step produces a cluster model comprising a covariance matrix for each of K clusters within the clustering model. 14. The method of claim 11 wherein the step of reclustering the data increases a cluster number K to provide a better cluster model that more accurately represents the data in the database. 15. The method of claim 14 wherein one or more regions of the attribute data space not accurately characterizing the data are chosen as cluster means in the second cluster model. 16. The method of claim 15 wherein the functional representation comprises a mixture model probability density function of the form: having mixture weights W1which represent a fraction of the database represented by a data cluster and wherein the number k is the number of such database clusters. 17. A computer-readable medium having computer executable instructions for performing steps comprising: a) clustering data from data records having multiple dimensions that are stored on a database to provide an initial cluster model having an initial probability distribution describing data records in the database; b) comparing the initial probability distribution with a representative sample of records in the database to determine a sufficiency of said initial probability distribution; c) modifying the cluster model to provide an adjusted cluster model that better characterizes the data in the database, said modifying step performed by finding a region within an attribute space of the data records of high discrepancy between the initial cluster model and a data sample gathered from the database and increasing the cluster number of the cluster model and reclustering at least a portion of the data in the database to produce the adjusted cluster model to reduce discrepancies between the initial probability distribution and data sample from the database; and d) determining a sum or a count of data records from the database falling within specified ranges of the multiple dimensions by integrating a functional representation based on the probability distribution of the adjusted cluster model over the ranges. 18. The computer readable medium of claim 17 wherein the determining step limits some but not all of the attributes to specific ranges and wherein the integrating step for continuous attributes not limited to a range is performed over an entire range of said attributes. 19.