Abstract:
A method, system, and computer program product for allocating data mining processing tasks that does not use complex internal schemes, yet results in better performance than is possible with general-purpose operating system based schemes. The present invention uses a data mining agent that operates autonomously, proactively, reactively, deliberatively, and cooperatively to allocate and reallocate data mining processing tasks among computer systems, and/or among processors. The data mining agent reacts to its own environment, determines if a data mining activity can be completed as expected, solicits bids from other data mining agents, determines if anther data mining system could complete the data mining activity and migrates that data mining activity to the selected data mining system.

Description:
FIELD OF THE INVENTION 
     The present invention relates to allocating data mining processing tasks using data mining agents that provide efficient hardware utilization of the data mining system. 
     BACKGROUND OF THE INVENTION 
     Data mining is a technique by which hidden patterns may be found in a group of data. True data mining doesn&#39;t just change the presentation of data, but actually discovers previously unknown relationships among the data. Data mining is typically implemented as software in or in association with database systems. Data mining includes several major steps. First, data mining models are generated based on one or more data analysis algorithms. Initially, the models are “untrained”, but are “trained” by processing training data and generating information that defines the model. The generated information is then deployed for use in data mining, for example, by providing predictions of future behavior based on specific past behavior. 
     Data mining typically involves the processing of large amounts of data, which consumes significant hardware resources. As a result, it is desirable to configure the data mining software system for efficient utilization of the hardware resources. This may present a problem. For example, if a data mining software system is configured to use all of the processors of a given hardware system, the data mining software system must either perform complex internal allocation of tasks to multiple threads/processes, or the data mining software system must allow the operating system to perform the allocation. If internal allocation is used, significant complexity is added to the data mining software system. This can cause difficulties in generating, debugging, and maintaining the data mining software system. If the operating system is used to perform allocation, the operating system will typically use a general-purpose allocation scheme. This general purpose allocation scheme cannot produce optimal usage of resources since data mining demands and behavior are significantly different than those that the typical general purpose allocation scheme has been designed to handle. 
     An additional problem may arise if, once a data mining processing task has started execution, the hardware system servicing the task becomes overloaded due to other tasks being executed. This may cause degradation in the performance of the data mining processing task, or, in some cases, cause the data mining processing task to become unexecutable. For example, if a data mining processing task requires a certain minimum number of processors to execute and the number of available processors is always fewer than that minimum, due to other tasks, the data mining processing task will never execute. This is unacceptable from a performance standpoint, since the typical data mining system expects a data mining processing task to run to completion in its current environment, without interruption. 
     A need arises for a technique by which data mining processing tasks may be allocated without complex internal schemes, yet resulting in better performance than is possible with general-purpose operating system based schemes. 
     SUMMARY OF THE INVENTION 
     The present invention is a method, system, and computer program product for allocating data mining processing tasks that does not use complex internal schemes, yet results in better performance than is possible with general-purpose operating system based schemes. The present invention uses a data mining agent that operates autonomously, proactively, reactively, deliberatively and cooperatively to allocate and reallocate data mining processing tasks among computer systems, and/or among processors. 
     In one embodiment, the present invention is a method of data mining performed in a data mining agent executing in a computer system, the method comprising the steps of examining a request queue comprising at least one request for data mining processing, determining if the at least one request for data mining processing can be processed, accepting the at least one request for data mining processing if it is determined that the at least one request for data mining processing can be processed, and processing the accepted request for data mining processing in the computer system. 
     In one aspect of this embodiment of the present invention, the determining step comprises the steps of determining if an algorithm required to process the at least one request for data mining processing is supported by the computer system, if the algorithm required to process the at least one request for data mining processing is supported, determining whether the computer system is available for additional processing, if the computer system is not available for additional processing, determining whether the computer system will become available for additional processing before other computer systems that might process the at least one request, if the computer system is available for additional processing, or if the computer system will become available for additional processing before other computer systems that might process the at least one request, determining whether the computer system will be able to complete requested processing in an allotted time, and if the computer system will be able to complete the requested processing in the allotted time, determining that the computer system can process the at least one request for data mining processing. The at least one request for data mining processing comprises data defining at least one algorithm that must be performed in order to perform the requested data mining processing. There is data defining algorithms that are supported by the computer system. The step of determining if an algorithm required to process the at least one request for data mining processing is supported comprises comparing the data defining at least one algorithm that must be performed in order to perform the requested data mining processing with data defining algorithms that are supported by the computer system. The data defining at least one algorithm that must be performed in order to perform the requested data mining processing and the data defining algorithms that are supported by the computer system are in extensible markup language format. 
     In one aspect of this embodiment of the present invention, the step of determining whether the computer system is available for additional processing comprises the step of determining whether available idle time of the computer system is greater than a predefined or a dynamically calculated threshold. 
     In one aspect of this embodiment of the present invention, the computer system comprises a plurality of processors and the step of determining whether the computer system is available for additional processing comprises the step of determining whether any of the plurality of processors is available for additional processing. The step of determining whether any of the plurality of processors is available for additional processing comprises the step of determining, for each of the plurality of processors, whether available idle time of the processor is greater than a predefined or a dynamically calculated threshold. 
     In one aspect of this embodiment of the present invention, the step of determining whether the computer system is available for additional processing comprises the step of determining availability of the computer system for additional processing relative to at least one other computer system. 
     In one aspect of the present invention, the step of determining whether the computer system will become available for additional processing before other computer systems that might process the at least one request comprises the steps of estimating a time to availability of the computer system, exchanging an estimate of a time to availability of the at least one other computer system, and comparing the time to availability of the computer system with the time to availability of the at least one other computer system. The step of determining whether the computer system will be able to complete requested processing in an allotted time comprises the steps of estimating a time to completion for the requested processing on the computer system, comparing the time to completion for the requested processing on the computer system with time allocation information included in the request for data mining processing. 
     In one embodiment, the present invention is a method of data mining performed in a data mining agent executing in a computer system, the method comprising the steps of determining that the computer system is overloaded, querying at least one other computer system to determine whether the at least one other computer system can complete a data mining processing task being performed on the computer system faster than the computer system, determining whether the at least one other computer system can complete the data mining processing task being performed on the computer system faster than the computer system, and if the at least one other computer system can complete the data mining processing task faster than the computer system, migrating the processing from the computer system to the at least one other computer system. 
     In one aspect of this embodiment of the present invention, the migrating step comprises the steps of reserving the at least one other computer system for migration, interrupting and checkpointing the data mining processing task on the computer system, and enqueueing a request to the at least one other computer system for continued processing of the data mining processing task. 
     In one aspect of this embodiment of the present invention, the step of determining that the computer system is overloaded comprises the step of determining that the computer system is overloaded if a utilization of a processor of the computer system is greater than a predefined threshold for a predefined time. 
     In one aspect of this embodiment of the present invention, the querying step comprises the step of generating an estimate of a time to complete the data mining processing task. The generating step comprises the steps of estimating an amount of processing that must be performed to complete the data mining processing task, estimating a processor utilization that will be available to process the data mining processing task, and estimating a time to complete the data mining processing task based on the estimate of the amount of processing that must be performed, the estimate of available processor utilization, and a speed of the processor. The querying step further comprises the step of requesting information from the at least one other computer system, the information including a speed of the at least one other computer system and an estimate of processor utilization of the at least one other computer system. 
     In one aspect of this embodiment of the present invention, the step of determining whether the at least one other computer system can complete a data mining processing task being performed on the computer system faster than the computer system comprises the step of estimating a time to complete the data mining processing task for the at least one other computer system based on the estimate of the amount of processing that must be performed to complete the data mining processing task, the speed of the at least one other computer system and the estimate of processor utilization of the at least one other computer system. The step of determining whether the at least one other computer system can complete a data mining processing task being performed on the computer system faster than the computer system further comprises the steps of adding an estimate of a time to migrate the data mining processing task to the at least one other computer system and the estimate of the time to complete the data mining processing task for the at least one other computer system, comparing the estimate of the time to complete the data mining processing task for the computer system with the estimate of the time to complete the data mining processing task for the at least one other computer system, and determining whether the at least one other computer system can complete the data mining processing task being performed on the computer system faster than the computer system. 
     In one aspect of this embodiment of the present invention, the querying step further comprises the step of transmitting to the at least one other computer system the estimate of the amount of processing that must be performed to complete the data mining processing task, and receiving from the at least one other computer system an estimate of a time to complete the data mining processing task for the at least one other computer system. 
     In one aspect of this embodiment of the present invention, the step of determining whether the at least one other computer system can complete a data mining processing task being performed on the computer system faster than the computer system further comprises the steps of adding an estimate of a time to migrate the data mining processing task to the at least one other computer system and the estimate of the time to complete the data mining processing task for the at least one other computer system, comparing the estimate of the time to complete the data mining processing task for the computer system with the estimate of the time to complete the data mining processing task for the at least one other computer system, and determining whether the at least one other computer system can complete the data mining processing task being performed on the computer system faster than the computer system. 
     In one embodiment, the present invention is a method of data mining performed in a data mining agent executing in a computer system, the method comprising the steps of determining that a processing load in the computer system is high relative to at least one other computer system, determining a remaining cost of completing processing of a data mining processing task being processed by the computer system, determining whether the at least one other computer system can complete processing of the data mining processing task at a lower cost than the computer system, and if the at least one other computer system can complete processing of the data mining processing task at a lower cost than the computer system, migrating processing of the data mining processing task to the at least one computer system. 
     In one aspect of this embodiment of the present invention, the step of determining that a processing load in the computer system is high relative to at least one other computer system comprises the steps of determining a processor utilization of the computer system, determining a processor utilization of the at least one other computer system, and determining that the processor utilization of the computer system is greater than a predefined amount higher than the processor utilization of the at least one other computer system. 
     In one aspect of this embodiment of the present invention, the remaining cost of completing processing of a data mining processing task may be determined based on a time to complete processing of the data mining processing task. The remaining cost of completing processing of a data mining processing task may be determined based on a time to complete processing of the data mining processing task and on additional factors, including actual costs of use of the computer system. The step of determining a remaining cost of completing processing of a data mining processing task being processed by the computer system may comprise the steps of estimating an amount of processing that must be performed to complete the data mining processing task, estimating a processor utilization that will be available to process the data mining processing task, and estimating a time to complete the data mining processing task based on the estimate of the amount of processing that must be performed, the estimate of available processor utilization, and a speed of the processor. The method may further comprise the step of estimating additional factors, including actual costs of use of the computer system. 
     In one aspect of this embodiment of the present invention, the step of determining whether the at least one other computer system can complete processing of the data mining processing task at a lower cost than the computer system comprises the step of soliciting a bid for completing processing of the data mining processing task from the at least one other computer system. 
     In one aspect of this embodiment of the present invention, the soliciting step comprises the steps of transmitting a request for a bid to the at least one other computer system, the request for the bid including information relating to the amount of processing that must be performed to complete the data mining processing task, and receiving a bid from the at least one other computer system, the bid including an estimate of a cost of completing processing of the data mining processing task on the at least one other computer system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The details of the present invention, both as to its structure and operation, can best be understood by referring to the accompanying drawings, in which like reference numbers and designations refer to like elements. 
         FIG. 1  is an exemplary block diagram of a data mining system, in which the present invention may be implemented. 
         FIG. 2  is an exemplary block diagram of a database/data mining system shown in FIG.  1 . 
         FIG. 3  is an exemplary data flow diagram of a data mining process, which may be implemented in the system shown in FIG.  1 . 
         FIG. 4   a  is an exemplary block diagram of one embodiment of a data mining system shown in FIG.  1 . 
         FIG. 4   b  is an exemplary block diagram of one embodiment of a data mining system shown in FIG.  1 . 
         FIG. 5  is an exemplary data flow diagram of processing performed by a data mining agent, according to the present invention. 
         FIG. 6  is an exemplary data flow diagram of data mining agents shown in  FIG. 5  selecting tasks to process. 
         FIG. 7  is an exemplary flow diagram of a data mining processing task request selection process, according to the present invention. 
         FIG. 8  is an exemplary flow diagram of a process performed by a step of the data mining processing task request selection process shown in FIG.  7 . 
         FIG. 9  is an exemplary flow diagram of one embodiment of a data mining processing task migration process, according to the present invention. 
         FIG. 10  is an exemplary flow diagram of one embodiment of a data mining processing task migration process, according to the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     An exemplary data mining system  100 , in which the present invention may be implemented, is shown in FIG.  1 . System  100  includes a data mining system  102  that is connected to a variety of sources of data. For example, system  102  may be connected to a plurality of internal or proprietary data sources, such as systems  104 A- 104 N. Systems  104 A- 104 N may be any type of data source, warehouse, or repository, including those that are not publicly accessible. Examples of such systems include inventory control systems, accounting systems, scheduling systems, etc. System  102  may also be connected to a plurality of proprietary data sources that are accessible in some way over the Internet  108 . Such systems include systems  106 A- 106 N, shown in FIG.  1 . Systems  106 A- 106 N may be publicly accessible over the Internet  108 , they may be privately accessible using a secure connection technology, or they may be both publicly and privately accessible. System  102  may also be connected to other systems over the Internet  108 . For example, system  110  may be privately accessible to system  102  over the Internet  108  using a secure connection, while system  112  may be publicly accessible over the Internet  108 . 
     The common thread to the systems connected to system  102  is that the connected systems all are potential sources of data for system  102 . The data involved may be of any type, from any original source, and in any format. System  102  has the capability to utilize and all such data that is available to it. 
     An exemplary embodiment of data mining system  102  is shown in FIG.  2 . Data mining system  102  utilizes data, such as externally stored data  204  and internally stored data  206 , which is obtained from data sources such as the proprietary and public data sources shown in FIG.  1 . Data mining system  102  also includes data mining engine  208 . Externally stored data  204  is typically stored in a database management system and is accessed by data mining system  102 . The database management system typically includes software that receives and processes queries of the database, such as those received from data mining system  102 , obtains data satisfying the queries, and generates and transmits responses to the queries, such as to data mining system  102 . Internally stored data  206  contemplates an embodiment in which data mining engine  208  is combined with, or implemented on, a database management system. In either case, data  204  or  206  includes data, typically arranged as a plurality of data tables, such as relational data tables, as well as indexes and other structures that facilitate access to the data. Data mining engine  208  performs data mining processes, such as processing data to generate data mining models and responding to requests for data mining results from one or more users, such as user  212 . 
     An exemplary data flow diagram of a data mining process, which may be performed by data mining engine  208 , including building and scoring of models and generation of predictions/recommendations, is shown in FIG.  3 . The training/model building step  302  involves generating the models that are used to perform data mining recommendation/prediction, clustering, association rule generation, etc. The inputs to training/model building step  302  include training parameters  304 , training data  306 , and untrained models  308 . For some types of models, such as neural network or self-organizing map models, untrained models  308  may include initialized or untrained representations of the models in addition to algorithms that process the training data  306  in order to actually build the models. Such a representation includes a structural representation of the model that either does not actually contain data that makes up the model, or contains only default data or parameters. The actual data or parameters are generated and entered into the representation during training/model building step  302  by the model building algorithms. For other types of models, such as tree models or association rule models, untrained models  308  do not include untrained representations of the models, but only include the algorithms that process the training data  306  in order to actually build the models. Training parameters  304  are parameters that are input to the data-mining model building algorithms to control how the algorithms build the models. Training data  306  is data that is input to the algorithms and which is used to actually build the models. Model building can also partition “build data” into training, evaluation, and test datasets. The evaluation dataset can be used by the model building algorithm to avoid overtraining, while the test dataset can be used to provide error estimates of the model. 
     Training/model building step  302  invokes the data mining model building algorithms included in untrained models  308 , initializes the algorithms using the training parameters  304 , processes training data  306  using the algorithms to build the model, and generates trained model  310 . Trained model  310  may include rules that implement the conditions and decisions that make up the operational model, for those types of models that use rules. As part of the process of building trained model  310 , trained model  310  is evaluated and, for example, in the case of decision tree models, those rules that decrease or do not contribute to the quality, i.e. prediction accuracy, of the model are eliminated from the model. The remaining rules of trained model  310  are encoded in an appropriate format and are deployed for use in making predictions or recommendations. For those types of models that do not use rules, such as neural networks, the trained model  310  includes an appropriate representation of the model encoded in an appropriate format and deployed for use in making predictions or recommendations. 
     Scoring step  312  involves using the deployed trained model  310  to make predictions or recommendations based on new data that is received. Trained model  310 , prediction parameters  314 , and prediction data  316  are input to scoring step  312 . Trained models  310  include one or more sets of deployed rules that were generated by model building step  302 . Prediction parameters  314  are parameters that are input to the scoring step  318  to control the scoring of trained model  310  against prediction data  316  and are input to the selection and prediction/recommendation step  320  to control the selection of the scored rules and the generation of predictions and recommendations. 
     Prediction data  316  is processed according to deployed rules or other representation of the model included in trained model  310 , as controlled by prediction parameters  314 . In the case of a rule based model, scores are generated for prediction data  316  based upon each rule in the set of deployed rules included in trained model  310 . Typically, a trained model  310  can be defined in terms of a function of input variables producing a prediction/recommendation based on the input variables. The function is evaluated using the input prediction data  316  and scores are generated. The scores indicate how closely the function defined by the model matches the prediction data, how much confidence may be placed in the prediction, how likely the output prediction/recommendation from the model is to be true, and other statistical indicators. Scored data  318  is output from scoring step  312  and includes predictions or recommendations for each scored record in prediction data  316 , along with corresponding probabilities for each scored record. 
     Scored data  318  is input to selection and prediction/recommendation generation step, which evaluates the probabilities associated with each record of scored data  318  and generates predictions/recommendations based on the scored data. Records may be selected based on prediction parameters  314  provided by the user, for example, to filter records that do not meet some probability threshold. The generated predictions/recommendations are output  322  from step  320  for use in any post data mining processing. 
     An exemplary block diagram of one embodiment of a database/data mining system  102 , shown in  FIG. 1 , is shown in  FIG. 4   a . Database/data mining system  102  is typically a programmed general-purpose computer system, such as a personal computer, workstation, server system, and minicomputer or mainframe computer. Database/data mining system  102  includes one or more processors (CPUs)  402 A- 402 N, input/output circuitry  404 , network adapter  406 , and memory  408 . CPUs  402 A- 402 N executes program instructions in order to carry out the functions of the present invention. Typically, CPUs  402 A- 402 N are one or more microprocessors, such as an INTEL PENTIUM® processor.  FIG. 4  illustrates an embodiment in which data mining system  102  is implemented as a single multi-processor computer system, in which multiple processors  402 A- 402 N share system resources, such as memory  408 , input/output circuitry  404 , and network adapter  406 . However, the present invention also contemplates embodiments in which data mining system  102  is implemented as a plurality of networked computer systems, which may be single-processor computer systems, multi-processor computer systems, or a mix thereof. 
     Input/output circuitry  404  provides the capability to input data to, or output data from, database/data mining system  102 . For example, input/output circuitry may include input devices, such as keyboards, mice, touchpads, trackballs, scanners, etc., output devices, such as video adapters, monitors, printers, etc., and input/output devices, such as, modems, etc. Network adapter  406  interfaces database/data mining system  102  with network  410 . Network  410  may be any standard local area network (LAN) or wide area network (WAN), such as Ethernet, Token Ring, the Internet, or a private or proprietary LAN/WAN. 
     Memory  408  stores program instructions that are executed by, and data that are used and processed by, CPU  402  to perform the functions of the database/data mining system  102 . Memory  408  may include electronic memory devices, such as random-access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc., and electromechanical memory, such as magnetic disk drives, tape drives, optical disk drives, etc., which may use an integrated drive electronics (IDE) interface, or a variation or enhancement thereof, such as enhanced IDE (EIDE) or ultra direct memory access (UDMA), or a small computer system interface (SCSI) based interface, or a variation or enhancement thereof, such as fast-SCSI, wide-SCSI, fast and wide-SCSI, etc, or a fiber channel-arbitrated loop (FC-AL) interface. 
     Memory  408  includes data  206 , database management processing routines  412 , data mining processing routines  414 A- 414 Z, data mining agents  416 A  416 Z, and operating system  418 . Data  206  includes data, typically arranged as a plurality of data tables, such as relational database tables, as well as indexes and other structures that facilitate access to the data. Database management processing routines  412  are software routines that provide database management functionality, such as database query processing. 
     Data mining processing routines  414 A- 414 Z are software routines that implement the data mining processing performed by the present invention. Data mining processing routines  414 A- 414 Z interact with and are used by data mining agents  418 A- 418 Z. Data mining agents  418 A- 418 Z are software components that perform data mining processing, but which have been enhanced to be capable of flexible, autonomous action in the environment. That is, each data mining agent can operate autonomously, proactively, reactively, deliberatively and cooperatively. Autonomous operation means that the data mining agent has control over its own behavior and internal states. Proactive operation means that the data mining agent can act in anticipation of future goals or tasks. Reactive operation means that the data mining agent can respond in a timely fashion to changes in its environment, including changes in available processing tasks, etc. Deliberative operation means that the data mining agent can reflect on or process received information before acting on that information. Cooperative operation means that the data mining agent can communicate with other data mining agents to coordinate their actions. Operating system  418  provides overall system functionality. 
     An exemplary block diagram of another embodiment of a data mining system  102  is shown in  FIG. 4   b . This embodiment includes a plurality of computer systems, such as computer systems  420 A-X, which communicate with each other over network  410 . Each computer system  420 A- 420 X includes components similar to those shown in  FIG. 4   a , but not all of these components are shown in  FIG. 4   b . Some of the computer systems, such as computer systems  420 A and  420 X include one or more active, running data mining agents. For example, computer system  420 A includes active, running data mining agent  422 , while computer system  420 X includes a plurality of active, running data mining agents  424 A- 424 Z. Computer system  420 N includes machine agent  426 . Machine agent  426  is a software component that provides monitoring and coordination capabilities to computer system  420 N even in the absence of any active, running data mining agents. 
     Machine agent  426  is a process that runs in the background and performs a specified operation at predefined times or in response to certain events. In particular, machine agent  426  receives and responds to coordination requests from data mining agents, which allows coordination of the local computer system upon which the machine agent resides (computer system  420 N in this case) with other computer systems. Machine agent  426  monitors the configuration, utilization, processing load, and other parameters of the local computer system and can respond to requests requiring such information. Machine agent  426  can also launch data mining agents, such as data mining agents  428 A- 428 Z, if necessary to respond to requests for migration of data mining processing tasks to the local computer system. 
     An exemplary data flow diagram of processing performed by a data mining agent  500  is shown in FIG.  5 . Data mining agent  500  includes real time processing  502 , tuning and/or adaptation processing  504 , and user/system goal assessment  506 . Data mining agent  500  accepts input data  508  and performs real time processing  502  on the data to generate output data  510 . Input data  508  typically includes data such as data mining model training data, data mining model training parameters, data mining prediction data, and data mining prediction parameters, which is obtained from data sources such as proprietary and public data sources, users of the data mining system, and predefined parameters. Input data  508  may also include system observation data, such as machine CPU usage/load data. Real time processing  502  typically includes processing such as data mining model building, data mining model scoring, and data mining prediction/recommendation generation. Output data  510  typically includes data such as trained data mining models, scored data mining models, and data mining predictions and recommendations. Input data  508  is received from, and output data  510  is transmitted to, environment  512 . Environment  512  includes users of data mining processing services, sources of data mining data, other data mining systems with other data mining agents, etc. 
     User/system goal assessment processing  506  involves monitoring input data  508  to determine goals that users of data mining processing are attempting to achieve and how well those goals are being achieved by, in particular, other data mining systems with other data mining agents that are included in environment  512 . In addition, User/system goal assessment processing  506  monitor how well data mining agent  500  is achieving the goal of the data mining processing being performed by data mining agent  500 . By monitoring these factors, user/system goal assessment processing  506  allows data mining agent  500  to recognize goals that are not being achieved, whether by other data mining systems with other data mining agents or by data mining agent  500  itself. Tuning and/or adaptation processing  504  provides data mining agent  500  with the capability to respond when it determines that goals are not being achieved by other data mining agents or by data mining agent  500  itself. If the goals are not being achieved by other data mining systems, tuning and/or adaptation processing  504  can coordinate with the other data mining systems to migrate processing of data mining processing tasks from those systems to data mining agent  500  for processing. Likewise, if the goals are not being achieved by data mining agent  500 , tuning and/or adaptation processing  504  can coordinate with other data mining systems to migrate processing of data mining processing tasks from data mining agent  500  to the other data mining systems. 
     A data flow diagram of data mining agents selecting tasks to process is shown in FIG.  6 . As shown in  FIG. 6 , there are a plurality of data mining agents, such as data mining agents  602 A- 602 N. These data mining agents are software components that are present on one or more computer systems, such as servers. Data mining agents  602 A-N are typically distributed among the computer systems. One form of communication among data mining agents  602 A- 602 N is provided by mining object repository (MOR)  604 , which serves as a central repository for data mining objects that is accessible by all data mining agents. In particular, MOR  604  includes one or more request queues, such as request queue  606 A- 606 X. Each request queue contains requests for data mining processing received directly or indirectly from data mining users. Request queues  606 A- 606 X may be organized in any way desired. For example, request queues  606 A- 606 X may be organized according to data mining users, types of data mining processing requested, priority levels of the requests, etc. The received requests for data mining processing are typically queued in a first-in-first-out (FIFO) arrangement. However, any request queue organization and any queueing arrangement is contemplated by the present invention. In addition, the MOR  604  is a logical entity and may itself be distributed to provide reliability and fault tolerance. Again, the present invention contemplates any arrangement or distribution of the MOR. 
     Each data mining agent, such as data mining agent  602 A, includes a plurality of processes/threads, such as peek at queue process  608 A and operation thread  610 A. The peek at queue processes  608 A- 608 N of data mining agents  602 A- 602 N communicate with request queues  606 A- 606 X and examine the queued requests for data mining processing contained therein. The peek at queue processes  608 A- 608 N select requests for data mining processing that are to be processed by each associated data mining agent as shown in FIG.  7 . 
     A data flow diagram of a data mining processing task request selection process  700  of a data mining agent is shown in FIG.  7 .  FIG. 7  is best viewed in conjunction with FIG.  6 . Process  700  begins with requests for data mining processing being submitted to request queues  606 A- 606 X, as described above. In step  704 , a peek at queue process, such as peek at queue process  608 A of data mining agents  602 A, examines the queued requests for data mining processing contained therein. Typically, peek at queue processes  608 A is proactive, that is, the process actively examines request queues  606 A- 606 X looking for suitable requests to handle. In step  706 , peek at queue process  608 A determines if its associated data mining agent, data mining agent  602 A, is capable of processing each particular request. In step  708 , if peek at queue process  608 A determines that its associated data mining agent, data mining agent  602 A, is capable of processing a particular request, then peek at queue process  608 A accepts the request for processing and dequeues that request from the request queue in which it is contained. Steps  706  and  708  are performed repeatedly, with peek at queue process  608 A examining any accepted requests until it determines that data mining agent  602 A cannot handle any more requests. In step  710 , data mining agent  602 A processes the accepted requests. 
     A flow diagram of a process performed by step  706 , shown in  FIG. 7 , in which peek at queue process  608 A determines if its associated data mining agent, data mining agent  602 A, is capable of processing each particular request, is shown in FIG.  8 . The process of step  706  begins with step  706 - 1 , in which it is determined whether the data mining agent supports the algorithm or algorithms that are required to process the particular request for data mining process being examined. For example, there may be data defined in, or associated with, the data mining agent, which defines the algorithms that are supported by the data mining agent. Likewise, the request for data mining processing may include data that defines, explicitly or implicitly, one or more algorithms that must be performed in order to perform the requested processing. An example may include XML data stored in the data mining agent that defines the algorithms supported by the data mining agent and XML data in the request for data mining processing that defines the algorithms that are required to process the request. In this case, a simple comparison of the XML definitions should suffice to determine whether the data mining agent supports the algorithm or algorithms that are required to process the particular request for data mining process being examined. If the request for data mining processing includes data that implicitly defines the algorithms that must be performed in order to perform the requested processing, a more complex process must be performed in order to determine whether the data mining agent supports the algorithm or algorithms that are required to process the particular request for data mining process being examined. 
     If, in step  706 - 1 , it is determined that the data mining agent does not support the algorithm or algorithms that are required to process the particular request for data mining process being examined, then the process of step  706  continues with step  706 - 2 , in which it is determined that the local computer system cannot process the particular request being examined. If, in step  706 - 1 , it is determined that the data mining agent does support the algorithm or algorithms that are required to process the particular request for data mining process being examined, then the process of step  706  continues with step  706 - 3 , in which it is determined whether the computer system upon which the associated data mining agent resides is currently busy and thus unavailable to accept additional processing. The definition of busy may be adjusted as desired. For example, a computer system may be defined as busy if it is performing any processing at all. On the other hand, a computer system may be defined as busy only if the available idle time of the computer system is less than some predefined or some dynamically calculated threshold. Likewise, in an embodiment in which one or more computer systems have more than one processor, the busy condition of each processor may be used instead. 
     An enhancement to step  706 - 3  is to determine the busy condition of the local computer system relative to other computer systems that may be utilized, rather than absolutely. For example, it may be determined whether the local computer system is more or less busy than other computer systems that might process the request. If other computer systems are more busy, then it may be determined, in step  706 - 3 , that the local computer system is relatively not busy. Conversely, if other computer systems are less busy, then it may be determined, in step  706 - 3 , that the local computer system is relatively busy. The relative busy conditions of the involved computer systems may be determined based on a variety of factors. For example, the processing load on each computer system may be considered, along with the processing speed of each computer system. The involved computer systems may exchange messages indicating these and other parameters, which may be compared by the data mining agents on each computer system. For example, each involved computer system may transmit a message in XML format, which may then be compared by the data mining agents on each computer system to determine the relative busy conditions of the involved computer system. The determinations may be made based on different algorithms, parameters, or thresholds by the various data mining agents. Thus, different data mining agents may generate different determinations of relative busy conditions. 
     However the determination of the busy condition of the local computer system is made, if, in step  706 - 3 , it is determined that the local computer system is busy, then the process of step  706  continues with step  706 - 4 , in which it is determined whether the local computer system is the first computer system that will become available for additional processing. The data mining agent first estimates the time to availability of the computer system upon which it resides. This estimate is performed based on factors such as estimated completion times of the processing jobs currently running on the computer system upon which the data mining agent resides. Each processing algorithm, such as data mining algorithms and others, provides estimates of completion times and also provides regular updates to those estimates. After the data mining agent has produced an estimate of the availability of the computer system upon which it resides, the data mining agent then exchanges estimates with other data mining agents and determines its availability relative to other data mining agents. If, in step  706 - 4 , it is determined that the data mining agent is not the first, or is not among the first number, of data mining agents that will become available, then the process of step  700  continues with step  706 - 2 , in which it is determined that the local computer system cannot process the particular request being examined. 
     If, in step  706 - 4 , it is determined that the data mining agent is the first, or is among the first number, of data mining agents that will become available, then the process of step  700  continues with step  706 - 5 . Likewise, if in step  706 - 3 , it is determined that the local computer system is not busy, then the process of step  706  continues with step  706 - 5 . In step  706 - 5 , it is determined whether the local computer system will be able to complete the requested processing in the allotted time. The request for data mining processing that is being examined may include time allocation information indicating a time that the processing must be completed or a total amount of processing time to be allocated to the task. The data mining agent generates an estimate of the time to completion of the task if the processing were performed on the computer system upon which the data mining agent resides. This estimate is then compared with the time allocation information included in the request for data mining processing. If it is determined that the local computer system will be able to complete the requested processing in the allotted time, then process  700  continues with step  706 - 6 , in which it is determined that the local computer system can process the particular request being examined. If it is determined that the local computer system will not be able to complete the requested processing in the allotted time, then process  700  continues with step  706 - 2 , in which it is determined that the local computer system cannot process the particular request being examined. 
     If no data mining agent accepts a request for data mining processing within a defined time limit, a timeout response may be transmitted to the entity that issued the request, the requester. The time limit may be defined in the processing request itself, or it may be defined by a default value for the system MOR, or the particular request queue in which the processing request is queued. The timeout response allows the requestor to perform alternate or error processing in the event the processing request is not accepted for processing. 
     An important feature of the present invention is the mobility of data mining processing from data mining agent to other agents and from one computer system to another. In particular, one or more data mining processing tasks that are being processed may be migrated to other computer systems under certain circumstances. For example, a computer system upon which a data mining agent resides may become overloaded, which would result in some or all of the tasks being processed by that computer system to be completed late or not completed at all. In this situation, the data mining agent, which is monitoring its environment will detect the overload condition and may transfer the data mining processing task that it is processing to another computer system. 
     A flow diagram of one embodiment of a data mining processing task migration process  900  is shown in FIG.  9 . The process begins with step  902 , in which a local data mining agent determines that the local computer system, upon which the local data mining agent resides, and which is processing the current task of the local data mining agent, is overloaded. The local data mining agent may determine overloading in a number of ways, but typically, processor (CPU) utilization is the preferred measure. For example, a threshold CPU utilization may be set, such as if the CPU utilization is greater than a predefined percentage for a predefined number of seconds, then an overload condition exists. 
     In step  904 , the local data mining agent queries other computer systems to determine if any other computer systems can complete the current task of the local data mining agent more quickly than the local computer system. To do this, the local data mining agent generates an estimate of the time the task would take to complete if the processing were performed on the local computer system. This estimate involves estimating the amount of processing that must be performed to complete the data mining processing task and an estimate of the CPU utilization available to process the data mining processing task. The time to complete processing of the data mining processing task may then be estimated based on the estimate of the amount of processing that must be performed, the estimate of available CPU utilization, and the speed of the CPU. The data mining agent also transmits queries to other computer systems. Typically, the queries request from other data mining agents information such as the speeds of the computer systems upon which the other data mining agents reside and estimates of CPU utilization that the computer systems upon which the other data mining agents reside could provide to process the data mining processing task. In some cases, there may not be any data mining agents running on a computer system that receives a query, even though the computer system is available for performing data mining processing. In this situation, other software on the computer system can respond to the query. 
     In step  906 , the local data mining agent determines whether another computer system could complete the data mining processing task faster than the local computer system. To do this, the local data mining agent computes estimates of times to complete the data mining processing task based on the amount of processing that must be performed to complete the data mining processing task, the speed of the other computer systems, and estimates of CPU utilization of the other computer systems. 
     Alternatively, the queries transmitted to the other data mining agents may include information relating to the amount of processing that must be performed to complete the data mining processing task. The other data mining agents would then compute estimates of times to complete the data mining processing task based on the amount of processing that must be performed to complete the data mining processing task, the speed of the other computer systems, and estimates of CPU utilization of the other computer systems. The responses to the queries would include these completion time estimates. 
     In either case, the local data mining agent then adds estimates of the time it would take to migrate the data mining processing task to another computer system to the estimated completion times for the other computer systems. The local data mining agent then compares the estimated completion time for the local computer system with the estimated completion times for the other computer systems to determine whether another computer system could complete the data mining processing task faster than the local computer system. If, in step  906 , the local data mining agent determines that the computer system upon which it resides could complete the data mining processing task faster than any other computer system, then process  900  ends and the data mining processing task is not migrated. 
     If in step  906 , the local data mining agent determines that another computer system could complete the data mining processing task faster than the local computer system, then process  900  continues with step  908 , in which the local data mining agent selects the computer system with the fastest completion time and reserves that computer system for migration of the data mining processing task. If there are one or more data mining agents running on the selected computer system, one of those data mining agents may receive and accept the reservation. Alternatively, other software on the selected computer system may receive and accept the reservation, whether data mining agents are running on the selected computer system or not. If there are no data mining agents running on the selected computer system, then the software that receives and accepts the reservation is responsible for launching a data mining agent to handle the data mining processing. 
     In step  910 , the local data mining agent interrupts the processing of the data mining processing task that is being performed on the local computer system. The data mining processing task is checkpointed, that is, all input data, processing state information, and output data that is required to resume processing of the data mining processing task is saved. In step  912 , the local data mining agent enqueues a “continueBuild” request in a request queue that serves the selected computer system, to which the data mining processing task is migrating. The continueBuild request typically references the checkpointed data that is needed to resume processing of the data mining processing task. When a data mining agent on the computer system to which the data mining processing task is migrating dequeues the continueBuild request, the reference to the checkpointed information is used to actually transfer the checkpointed information to the computer system to which the data mining processing task is migrating. Alternatively, the checkpointed information may be included with the continueBuild request. 
     A flow diagram of one embodiment of a data mining processing task migration process  1000  is shown in FIG.  10 . In this embodiment, the data mining agents communicate with each other on a regular basis, so that computer system utilization can be easily coordinated among the data mining agents. Process  1000  begins with step  1002 , in which a local data mining agent determines that the local computer system, upon which the local data mining agent resides, and which is processing the current task of the local data mining agent, has a high load relative to other computer systems. The local data mining agent may determine load in a number of ways, but typically, processor (CPU) utilization is the preferred measure. Data mining agents communicate loading information with each other on a regular basis. In particular, it may determined that the processing load of the local computer system is high relative to the processing loads of other computer systems by determining a processor utilization of the local computer system, determining processor utilizations of the other computer systems, and determining that the processor utilization of the local computer system is greater than a predefined amount higher than the processor utilization of the other computer systems. 
     In step  1004 , the local data mining agent determines the remaining cost of completing processing of the data mining processing task on the local computer system. The cost of completing processing may be based solely on the time it would take to complete processing, or it may be based on additional factors, such as actual costs that must be paid for use of computing equipment, etc. In order to determine the time it would take to complete processing, the local data mining agent generates an estimate of the time the task would take to complete if the processing were performed on the local computer system. This estimate involves estimating the amount of processing that must be performed to complete the data mining processing task and an estimate of the CPU utilization that will be used to process the data mining processing task. In addition, the local data mining agent may estimate other factors, such as actual costs that must be paid for use of computing equipment, etc. 
     In step  1006 , the local data mining agent solicits bids for completing processing of the data mining processing task from other computer systems. Typically, the requests for bids transmitted to the other data mining agents include information relating to the amount of processing that must be performed to complete the data mining processing task. The other data mining agents would then submit bids to the local data mining agent. The bids would include estimates of the costs of completing the data mining processing task on each of the other computer systems. In order to generate a bid, a data mining agent would compute estimates of costs to complete the data mining processing task that are based on the amount of time that is needed to complete the migrated task and may also be based on other factors, such as the cost of processing on the computer system. The time to complete the migrated task includes both the time needed to complete the processing and the time needed to migrate the task from one computer system to another. The time needed to complete the processing is based on the amount of processing that must be performed to complete the data mining processing task, the speed of the other computer systems, and estimates of CPU utilization of the other computer systems. 
     In some cases, there may not be any data mining agents running on a computer system that receives a request for a bid, even though the computer system is available for performing data mining processing. In this situation, other software on the computer system can generate and transmit the bid. 
     In step  1008 , the local data mining agent determines whether another computer system has a bid that is lower than the cost to complete the data mining processing task on the local computer system. To do this, the local data mining agent compares the determination of the cost of completing processing of the data mining processing task on the local computer system with the bids received from the other computer systems. If any of the received bids are significantly lower than the cost of completing processing of the data mining processing task on the local computer system, the local data mining agent migrates the remaining processing of the data mining processing task to the lowest bidder among the other computer systems. In order to carry out the migration, the local data mining agent interrupts the processing of the data mining processing task that is being performed on the local computer system. The data mining processing task is checkpointed, that is, all input data, processing state information, and output data that is required to resume processing of the data mining processing task is saved. The data mining agent enqueues a “continueBuild” request in a request queue that serves the computer system to which the data mining processing task is migrating. The continueBuild request typically references the checkpointed data that is needed to resume processing of the data mining processing task. When a data mining agent on the computer system to which the data mining processing task is migrating dequeues the continueBuild request, the reference to the checkpointed information is used to actually transfer the checkpointed information to the computer system to which the data mining processing task is migrating. Alternatively, the checkpointed information may be included with the continueBuild request. 
     It is important to note that while the present invention has been described in the context of a fully functioning data processing system, those of ordinary skill in the art will appreciate that the processes of the present invention are capable of being distributed in the form of a computer readable medium of instructions and a variety of forms and that the present invention applies equally regardless of the particular type of signal bearing media actually used to carry out the distribution. Examples of computer readable media include recordable-type media such as floppy disc, a hard disk drive, RAM, and CD-ROM&#39;s, as well as transmission-type media, such as digital and analog communications links. 
     Although specific embodiments of the present invention have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the invention is not to be limited by the specific illustrated embodiments, but only by the scope of the appended claims.