Computing system and method for controlling the execution of a decision process to maintain the data access efficiency upon receipt of an availability information inquiry

Computer-implemented reservation method and system. The method utilized for controlling the execution of a decision process by a computer backend machine of a computer network upon receipt of a computation inquiry includes associating to the computer backend machine a configuration file containing at least a decision rule that drives the decision process and that is computed at least from a current value of a statistical indicator and a target value of the statistical indicator; periodically obtaining an updated value of the statistical indicator; upon detection that the updated value is differing from the target value, dynamically updating the configuration file which further comprises re-computing the decision rule using the updated value as new current value, and storing in real-time the re-computed decision rule in the configuration file.

TECHNICAL FIELD

The present invention relates generally to data and information processing for computer-implemented database systems and, more particularly, relates to a computer-implemented reservation system and method utilized for providing availability information in a travel reservation and booking system, such as travel seats, in connection with service resources. More precisely, the invention addresses the real-time decision for driving task executions by a source of data; for instance, choosing one among a plurality of data sources storing availability information data relevant to the availability requests and using inventory links to their optimal capacity, and thus providing a high availability service while using data sources of a lower reliability. In addition, the invention can also reduce failures in retrieving data from a data source and dynamically monitor and maintain the high efficiency of data access to an availability cache source when a data sync process is being executed.

BACKGROUND

A computerized travel system is organized around a Global Distribution System GDS being accessed by travel vendors such as travel agencies, online travel vendors and travel companies. The GDS system may be proprietary computer systems allowing real-time access to airline fares, schedules, and seating availability and other data.

The GDS system implements an access to various data sources in order to provide availability information. To retrieve availability information, the inventory source can be accessed by polling while other data sources are accessible in parallel in order to reduce the polling bandwidth, save costs (inventory database's accesses are more expensive) and cut off response time as often as possible.

A challenge is to quickly react to a rapid variation such as a growth of computation inquiries or a crisis (like a polling outage) and meanwhile to maintain the data access efficiency of data sources. It leads to technical constraints involving routing decisions between various sources where data relevant to reply to an availability request are potentially stored. System must then decide in which case it is more appropriate to use an AVS (availability status source) or a cache data source.

SUMMARY

In one example of embodiment, a method is disclosed for controlling the execution of a decision process by a computer backend machine of a computer network upon receipt of a computation inquiry, comprising:associating to the computer backend machine a configuration file containing at least a decision rule that drives the decision process and that is computed at least from a current value of a statistical indicator and a target value of the statistical indicator;periodically obtaining an updated value of the statistical indicator;upon detection that the updated value is differing from the target value, dynamically updating the configuration file which further comprises:re-computing the decision rule using the updated value as new current value;storing in real-time the re-computed decision rule in the configuration file.

In another example of embodiment, a computerized system comprises at least one computer backend machine conured to execute a decision process upon receipt of a computation inquiry and comprising a configuration file containing at least a decision rule that drives the decision process and that is computed at least from a current value of a statistical indicator and a target value of the statistical indicator, the computer backend machine comprising a processor where operation of the processor in accordance with a computer program stored in a computer-readable medium causes the computerized system to:periodically obtaining an updated value of the statistical indicator;upon detection that the updated value is differing from the target value, dynamically updating the configuration file which further comprises:re-computing the decision rule using the updated value as new current value;storing in real-time the re-computed decision rule in the configuration file.

Potential advantages of the present invention are:Maintaining a high quality of service by choosing an appropriate target data source from several data sources including relatively low-reliable ones;Optimally using the data sources and their storage capacity;Following and maintaining the contractual agreements, such as Service Level Agreements (SLA).

The exemplary embodiments also encompass a computer-readable medium that contains software program instructions, where execution of the software program instructions by at least one data processor results in performance of operations that comprise execution of the method of the present invention.

DETAILED DESCRIPTION

Although the following description is given in the context of an application to the airline industry, it does not represent a limiting example since the present invention is applicable to all sorts of travel and tourism products such as rooms, car rental, railways tickets or the like.

Before the introduction of the present invention, certain terms used in the following description are defined as follows:Availability: This is the number of seats available for sale. It is used to accept or deny further bookings.Sources of availability information: this comprises all kinds of data repositories where information connected to travel product availability are stored.Inventory source: a source of availability information which has the best accuracy since it reflects the actual status of the bookings made for travel products the inventory is comprised of. In typical cases, the inventory is based at the travel carrier side.Base availability source: a source of availability information which derives from the inventory source. It is often used at a GDS system side to prevent systematic access to the inventory source and save bandwidth consumption. Such data source type, also called AVS is periodically updated from the inventory source and is a simplified version of the inventory source. In term of data quality, the data sources rated according to a high, fair, or low confidence level are respectively listed as follows: the inventory source, the availability cache source and the AVS.Availability cache source: a source of availability information usually of lower confidence than the inventory source but with lower usage cost. Such caches may be fed with results obtained from past availability requests. A first user request and the corresponding reply can thus be re-used in response to another request. In some cases the cache is first built from responses to pro-active requests, i.e.: requests automatically created at server side.Polling: a query sent to the inventory source for replying to a user request.

FIGS. 1 and 2give an example of a first embodiment of the invention. In the embodiment, a polling error rate of an inventory source is monitored and utilized to drive a decision process on how to use the inventory source. Further embodiments of the invention are depicted inFIG. 3 through 6. The embodiments can be combined with each other.

FIG. 1is an example of an architecture block diagram of an availability inquiry system10according to the present invention. In the present embodiment, after receiving a computation inquiry, which is here an availability request r1, the availability inquiry system10can dynamically choose one from at least two data sources and retrieve the corresponding data from the chosen data source by applying a data source adaptation method to compute a decision rule. The data source adaptation method requires a polling error rate of an inventory source as an input data. The polling error rate represents a ratio between a number of access failures and a number of access attempts to a given data source. It is used as a statistical indicator.

The availability inquiry system10comprises a computer backend facility11, a decision controller12, and two data sources, an inventory source13aand an availability cache source13b, respectively, such data sources are purely indicative and the invention applies to any data sources. In the present embodiment, the given data source is the inventory source13a. All data sources are utilized for providing a storage space to (at least temporarily) store data relating to the seat availability. The computer backend facility11can be implemented by a computer cluster consisting of a set of loosely connected computers, or only by a single computing device. In the present embodiment, the computer backend facility11is coupled to the decision controller12and the data sources13aand13b, respectively.

The computer backend facility11is utilized for executing a decision process upon receipt of an availability request r1. It comprises at least one backend computing unit21and an observer22. The observer22is utilized for periodically generating and sending a latest polling error rate m1to the decision controller12. The polling error rate m1is a ratio between a number of failures in retrieving data from the inventory source13aand a total number of access attempts to the said inventory source13a. It thus reflects the access efficiency performed for the inventory source13a.

The backend computing unit21comprises a strategy coordinator31and a memory unit32. The memory unit32is coupled to the strategy coordinator31. The memory unit32is utilized for storing at least a configuration file c1containing at least a decision rule. The strategy coordinator31is utilized for executing the decision rule.

The decision controller12comprises a controller module26and a controller memory unit27. The controller module26, coupled to the memory unit27, is utilized for generating an order value o2which is between 0% and 100%. An order value, for example the order value o2, is defined as an access attempt rate to the inventory source13a. The memory unit27stores at least a configuration file c2, an order history file OH1and an observation history file OBH1. The configuration file c2contains a polling error threshold rate g, that is the highest acceptable inventory access failure rate, predefined by the system administrator for example; in the present embodiment, the polling error threshold rate g is set to be 20%. In addition, the decision controller12gathers the updated values of the polling error rate m1at regular intervals preferably of a length under 5 minutes.

It should be noted that in other embodiments, the configuration files c1and c2, the order history file OH1and the observation history file OBH1can be stored in a same memory unit, or in different memory units other than the memory unit32or the memory unit27. Also the decision controller12and the computer backend facility11may share some hardware and/or software resources.

The computer backend facility11executes a decision process in order to reach (but not exceed) the order value o2, and switches from the inventory source13a(the main data source) to the availability cache source13bin order to keep the polling error rate of the inventory source13anot greater than the polling error threshold rate g.

The decision process requires data computed by the decision controller12. The controller module26determines the order value o2according to information provided by the observer22, the configuration c2and the observation history file OBH1stored in the memory unit27. The detailed description about the data source adaptation method will be included in the following paragraphs.

The decision controller12is utilized for computing the updated order value o2related to the inventory source13a. It periodically obtains the updated polling error rate m1provided by the observer22, and the previous order value o1stored in the observation history file OBH1. It then detects a difference between the polling error rate m1and the polling error threshold rate g provided by the configuration file c2. The decision controller12generates the order value o2by applying a first response function which will be presented in the following paragraphs.

There are three cases of the value comparison between the polling error rate m1and the polling error threshold rate g:The polling error rate m1is equal to the polling error threshold rate g: The computer backend facility11remains to access to the inventory source13aas before and doesn't need to change the order value that will be applied to decision rules.The polling error rate m1is smaller than the polling error threshold rate g: It means that the number of data retrieving requests received by the inventory source13ais less than its maximum capacity of access. The computer backend facility11can more access to the inventory source13a. The updated order value o2is greater than the previous order value o1.The polling error rate m1is bigger than the polling error threshold rate g: It means that the number of data retrieving requests received by the inventory source13ais already greater than its maximum capacity of access. The updated order value o2is less than the previous order value o1.

As mentioned above, the controller module26generates the order value o2by applying the first response function which calculates on the previous order value o1, the polling error threshold rate g, the history of order values (recorded in OH1) and the history of observations (recorded in OBH1). The first response function can be presented as, but not limited to, the following example formula: o2=o1×e(g-m1).

The order value o2is then regulated if one of the following conditions occurs:If the updated order value o2is greater than 100%, the updated order value o2is set to be 100%.If the updated order value o2is smaller than a predefined minimum order value, the updated order value o2is set to be the minimum order value stored in the configuration file c2.

The controller module26stores the order value o2to the order history file OH1, which will become the new value of the order value o1utilized in the next computation.

After receiving the updated order value o2, the backend computing unit21re-computes the decision rule, including a data source selection rule for determining the data source among the inventory source13aand the availability cache source13bfor retrieving data to be used for the availability request r1. In all embodiments, the data source selection rule may be also a function of at least one characteristic of an availability request, such as the originator of the availability request or a parameter of the availability request, such as a market (it can be a journey origin and/or a destination, or the airline code of the provider in the travel industry.)

The detailed description about updating the data source selection rule given the updated order value o2is provided as follows:If the updated order value o2is greater than the previous order value o1, the data source selection rule is updated with a higher level of access, based on the rules stored in the configuration file c1. Said stored rules will take the updated order value o2and some characteristics of the request r1(eg: airline, product) which will be sent to the chosen data source to use.If the updated order value o2is smaller than the previous order value o1, it means the inventory source13apreviously got too many data retrieving requests, relative to its maximum capacity of access. The computer backend facility11needs to lower down the order value from the value o1to the value o2to reduce the polling error rate of the inventory13aand the data source selection rule is updated with a lower level of access. If the updated order value o2lowers and is close to 0, the computer backend facility11will be guided by the data source selection rule and thus switch to retrieve data from the availability cache source13bto process the largest part of the requests.

After obtaining necessary data retrieved from the chosen data source, the computer backend facility11generates an availability reply p1corresponding to the availability request r1.

The above re-computed decision rule is stored in real-time in the configuration file c1. The updating of the configuration file c1can be executed independently from applying the configuration file c1for any availability requests received by the computer backend facility11. Furthermore, it should be noted that in other embodiments, in addition to the inventory source13aand the availability cache source13b, the availability inquiry system10may comprise more than two data sources, such as an AVS source. In this case, if the availability cache source is selected but it is empty or it does not answer in time, the AVS source will be selected. In addition, there can be a plurality of polling sources selected among a dynamic availability polling source, a direct access polling source and an availability calculator source.

Turning now toFIG. 2,FIG. 2is a linear graph presenting the values of polling error rates maand mb, a polling error threshold rate g, and order values oaand ob. The polling error rate maand the order value oaare recorded before the execution of the data source adaptation method. The polling error rate mband the order value obare recorded during the execution of the data source adaptation method according to the present invention.

During the observation period, by executing the data source adaptation method, the availability inquiry system10efficiently reduces the polling error rate of the inventory source13afrom the value mato the value mb, which steadily approximates to the polling error threshold rate g. In addition, the order value performed for the inventory source13ais reduced from the value oato the value obbecause the availability inquiry system10adjusts in real-time the number of access attempts to the inventory source13aby dynamically computing the order value of the inventory source13awhen receiving new observations.

FIG. 3shows another embodiment where look-to-book considerations are used to drive the decision process.FIG. 3is an example of an architecture block diagram of a reservation system40according to the present invention. The structure of the reservation system40and its functions are similar to those of the above-mentioned availability inquiry system10of the above-mentioned embodiment. However, the decision process executed by the reservation system40requires a look-to-book number as an input data to be used as a statistical indicator. While retrieving data from an inventory source43a, the look-to-book number indicates the average number of availability requests received by the reservation system40involving an access to a given data source for making a conversion into a booking (eg, a seat reservation). An example of 700 “looks” for 1 “book” is used below.

In the present embodiment, after receiving an availability request r2, the reservation system40can dynamically choose one from at least two data sources and retrieve the corresponding data from the chosen data source by applying a data source adaptation method to compute a decision process.

The reservation system40comprises a computer backend facility41, a decision controller42, and two data sources, an inventory source43aand an availability cache source43b, respectively, such data sources are purely indicative and the invention applies to any data source. In the present embodiment, the given data source is the inventory source43a. The computer backend facility41is coupled to the decision controller42and the data sources43aand43b, respectively.

The computer backend facility41is utilized for executing a decision process upon receipt of an availability request r2. It comprises at least one backend computing unit and an observer52. The observer52is utilized for periodically generating and sending a latest look-to-book number lb1to the decision controller42. The smaller the look-to-book number lb1is, the more orders can be processed while retrieving the corresponding data from the inventory source43a, keeping a given inventory source usage efficiency. A small look-to-book number reflects a good efficiency of the inventory source since its access rate is low compared to the conversion of availability requests into bookings. (A look-to-book conversion rate takes into account only the looks to the inventory source43aagainst the bookings made by the reservation system40.)

The backend computing unit51comprises a strategy coordinator61and a memory unit62. The memory unit62is coupled to the strategy coordinator61. The memory unit62is utilized for storing at least a configuration file c3containing at least a decision rule. The strategy coordinator61is utilized for executing the decision rule.

The decision controller42comprises a controller module56and a controller memory unit57. The controller module56, coupled to the memory unit57, is utilized for generating an order value o4, which is between 0% and 100%. An order value, for example the order value o4, is defined as an access attempt rate to the inventory source43a. The decision controller42generates the order value o4by applying a second response function which will be presented in the following paragraphs.

The memory unit57stores at least a configuration file c4, an order history file OH2and an observation history file OBH2. The configuration file c4contains a goal look-to-book number lbg set by the system administrator to be 700 for example, and a look-to-book conversion rate is thus 700:1 in the present embodiment. In addition, the decision controller42gathers the updated values of the look-to-book number lb1at regular intervals of a length under 5 minutes, and computes a global look-to-book conversion rate which is computed as an integration since a given point in time, for example, the start of a day.

It should be noted that in other embodiments, the configuration files c3and c4, the order history file OH2and the observation history file OBH2can be stored in a same memory unit, or in different memory units other than the memory unit62or the memory unit57.

The computer backend facility41executes a decision process in order to decide the order value o4, and switch from the inventory source43a(the main data source) to the availability cache source43bif the incoming availability requests in order to keep the look-to-book number corresponding to the inventory source43anot greater than the goal look-to-book number lbg.

The decision process requires data computed by the decision controller42. The controller module56determines the order value o4according to information provided by the observer52, the configuration c4and the observation history file OBH2stored in the memory unit57. The detailed description about the data source adaptation method will be included in the following paragraphs.

The decision controller42is utilized for computing the updated order value o4related to the inventory source43a. It periodically obtains the updated look-to-book number lb1provided by the observer52, and the previous order value o3stored in the order history file OH2. It then detects a difference between the look-to-book number lb1and the goal look-to-book number lbg provided by the configuration file c4.

There are three cases of the value comparison between the look-to-book number lb1and the goal look-to-book number lbg:The look-to-book number lb1is equal to the goal look-to-book number lbg: The computer backend facility41remains to access as before to the inventory source43aand doesn't need to change the order value that will be applied to decision rules. However, if more orders are to be made, the computer backend facility41switches to access to the availability cache source43bfor retrieving data.The look-to-book number lb1is smaller than the goal look-to-book number lbg:The computer backend facility41can thus continue accessing to the inventory source43afor retrieving data. The updated order value o4is greater than the previous order value o3.The look-to-book number lb1is bigger than the goal look-to-book number lbg:It means that currently for making a conversion into a booking, the inventory source43aalready receives too many data retrieving requests. The computer backend facility41switches thus more frequently to access to the availability cache source43bfor retrieving data. The updated order value o4is less than the previous order value o3.

As mentioned above, The controller module56generates the updated order value o4by applying the second response function which calculates on the previous order value o3, the goal look-to-book number lbg, the history of order values (recorded in OH2) and the history of observations (recorded in OBH2). The second response function can be presented as, but not limited to, the following example formula: o4=o3×e(lbr1-lgbr), wherein the variable lgbr is a ratio calculated based on the goal look-to-book number lbg while the variable lbr1is a ratio calculated based on the look-to-book number lb1.

Like the order value o2of the availability inquiry system10, the order value o4is then regulated if one of the following conditions occurs:If the updated order value o4is greater than 100%, the updated order value o4is set to be 100%.If the updated order value o4is smaller than a predefined minimum order value, the updated order value o4is set to be the minimum order value stored in the configuration file c1.

The controller module56stores the order value o4to the order history file OH2, which will become the new value of the order value o3utilized in the next computation.

After receiving the updated order value o4, the backend computing unit21re-computes the decision rule, including a data source selection rule for determining the data source among the inventory source43aand the availability cache source43bfor retrieving data to be used for the availability request r2. The data source selection rule may also be a function of at least one characteristic of an availability request.

After obtaining necessary data retrieved from the chosen data source, the computer backend facility41generates an availability reply p2corresponding to the availability request r2.

The above re-computed decision rule is stored in real-time in the configuration file c3. The updating of the configuration file c3can be executed independently from applying the configuration file c3for any availability requests received by the computer backend facility41. Furthermore, it should be noted that in other embodiments, in addition to the inventory source43aand the availability cache source43b, the reservation system40may comprise more than two data sources, such as an availability status source. In addition, there can be a plurality of polling sources selected among a dynamic availability polling source, a direct access polling source and an availability calculator source.

FIG. 4is a linear graph presenting the values of look-to-book numbers lbaand lbb, a goal look-to-book number lbg, and order values oband od. The look-to-book number lbaand the order value ocare recorded without the execution of the data source adaptation method. The look-to-book number lbband the order value odare recorded with the execution of the data source adaptation method according to the present invention.

By executing the data source selection rule update process according to the present invention, the reservation system40makes the look-to-book number corresponding to the inventory source43aapproximate to the goal look-to-book number lbg at the end of each aggregation period, such as a daily integration), in order to adjust in real-time the number of access attempts to retrieving data from the inventory source43a.

FIG. 5is an example of an architecture block diagram of an availability inquiry system70according to the present invention. In the present embodiment, after receiving an availability request r3, the availability inquiry system70retrieves availability data from at least two data sources to generate an availability reply p3corresponding to the availability request r3. It further executes a data sync method to keep the data consistency among the data sources, one of which is here an availability cache source73b. The decision process requires a number of writes w1of the availability cache source73bas an input data, which is used as a statistical indicator.

The availability inquiry system70comprises a computer backend facility71, a decision controller72, and two data sources, an inventory source73aand a cache database73b, respectively, such data sources are purely indicative and the invention applies to any data sources. The computer backend facility71is coupled to the decision controller72and the data sources73aand73b, respectively.

The computer backend facility71is utilized for executing a data sync method to update data stored in the availability cache source73b. It comprises at least one backend computing unit and an observer82. The observer82is utilized for periodically sending a latest number of writes w1, performed for the availability cache source73b, to the decision controller72. The detailed description about the data sync method will be included in the following paragraphs.

The backend computing unit81comprises an availability engine91and a memory unit92. The memory unit92is coupled to the availability engine91. The memory unit92is utilized for storing at least a configuration file c5containing at least a decision rule. The availability engine91is utilized for executing the decision rule with applying an order sync rate o6, which represents a percentage of updated availability data to be replicated from the inventory source73ato the availability cache source73b.

The decision controller72comprises a controller module86and a controller memory unit87. The controller module86, coupled to the memory unit87, is utilized for generating the order sync rate o6. The memory unit87stores at least a configuration file c6, an order history file OH3and an observation history file OBH3. The configuration file c6contains a goal number of writes wg set by the system administrator, for instance, a maximum capacity of access to the availability cache source73b; in the present embodiment, the goal number of writes wg is set to be 500000. In addition, the decision controller72gathers the updated values of the number of writes w1at regular intervals preferably of a length under 5 minutes.

It should be noted that in other embodiments, the configuration files c5and c6, the order sync history file OSH and the observation history file OBH3can be stored in a same memory unit, or in different memory units other than the memory unit92or the memory unit87. Also the decision controller72and the computer backend facility71may share some hardware and/or software resources.

The computer backend facility71executes a decision process in order to reach (but not exceed) the order sync rate o6. The decision process requires data computed by the decision controller72. The decision controller72is utilized for computing the order sync rate o6. It periodically obtains the updated number of writes w1provided by the observer82, and the previous order sync rate o5stored in the order sync history file OSH. It then detects a difference between the number of writes w1and the goal number of writes wg provided by the configuration file c6.

The controller module86generates the updated order sync rate o6based on a third response function which calculates on the goal number of writes wg and the number of writes w1, and stores the order sync rate o6to the order sync history file OSH. The third response function is presented as, but not limited to, the following formula: o6=wg/w1. The controller module86also provides the order sync rate o6to the computer backend facility71to ensure that the availability cache source73bwill not exceed its maximum capacity of access.

There are three cases of the value comparison between the number of writes w1and the goal number of writes wg:The number of writes w1is equal to the goal number of writes wg: The computer backend facility71remains the same sync rate o6to replicate updated availability data from the inventory source73ato the availability cache source73b. The number of writes w2is set to be the value of the goal number of writes wg.The number of writes w1is bigger than the goal number of writes wg: The number of writes performed for the availability cache source73bmay be greater than its maximum capacity of access. It means that the computer backend facility71need to reduce data replication writes from the inventory source73ato the availability cache source73b. The number of writes w2is set to be the value of the goal number of writes wg.The number of writes w1is smaller than the goal number of writes wg: It means that the computer backend facility71can perform more data replication writes from the inventory source73ato the availability cache source73b. The number of writes w2is set to be the value of the number of writes w1.

After receiving the updated order sync rate o6, the computer backend facility71recomputes the decision rule, including a cache write throttling rule.

Turning now toFIG. 6,FIG. 6is a linear graph presenting the values of numbers of writes waand wb, and a maximum number of writes wg. The number of writes wais recorded without the execution of the data sync method. The number of writes wbis recorded with the execution of the data sync method according to the present invention. In addition, during the observation period, by executing the data sync method, the availability inquiry system70efficiently reduces the write overload of the availability cache source73b.

Exemplary embodiments of the invention are summarized hereafter; they can each be used independently or in combination with at least another exemplary embodiment of the invention:The invention comprises using the configuration file for any computation inquiry received by the computer backend machine independently from the updating of the configuration file.The at least one decision rule comprises data source selection rule providing a logic conured to determine a data source among plural data sources accessible for retrieving data to be used for the computation inquiry.The data source selection rule is a function of at least one characteristic of the computation inquiry.The computation inquiry is a flight availability computation inquiry, and where the data source selection rule is an availability data source selection rule, and where the plural data sources comprise at least two of: a polling source, an availability status source (AVS), an availability cache source.The plural data sources comprise at least one polling source selected among a dynamic availability polling source, a direct access polling source and an availability calculator source, and comprising using, as statistical indicator for determining the availability data source selection rule, a ratio between a number of failures in retrieving data from the polling source and a total number of access attempts to said polling source.The plural data sources comprise at least one polling source selected among a dynamic polling source, a direct access polling source and an availability calculator source, the method comprising using, as statistical indicator for determining the availability data source selection rule, a look-to-book ratio of an originator of the computation inquiry.The at least one decision rule comprises a cache writing decision rule providing a logic of whether to write in an availability cache source a replication of data obtained from a remote data source such as an inventory source also called polling source.The cache writing decision rule is a function of at least one characteristic of the computation inquiry.The invention comprises using a number of cache writings as statistical indicator for determining the cache writing decision rule.The computation inquiry requires the execution of a data read or write instruction in a data source and where the decision process is for deciding on whether a given data source is to be accessed by the computer backend machine for the execution of the data read or write instruction.The invention comprises: 1) defining an order level of read or write instructions to be executed by the given data source; this order level may be a proportion of instructions to be sent by the given data source; and 2) computing the decision rule so as to reach or stay below the order level.The invention comprises, upon the updated value of the statistical indicator differing from the target value of the statistical indicator, computing an updated value of the order level based on the product of a current value of the order level with an exponential function of a difference between the target value of the statistical indicator and the updated value of the statistical indicator, and re-computing the decision rule so as to reach the updated value of the order level.The invention comprises periodically gathering the updated values of the statistical indicator at regular intervals of a length under 5 minutes.A computer program product stored in non-transitory computer-readable medium and comprising instructions adapted to perform the method of the inventionThe invention comprises plural data sources accessible for retrieving data to be used for the computation inquiry and a network for bilateral communication between the computer backend machine and the plural data source.

The foregoing description has provided by way of exemplary and non-limiting examples a full and informative description of various method, apparatus and computer program software for implementing the exemplary embodiments of this invention. However, various modifications and adaptations may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the accompanying drawings and the appended claims. As but some examples, the use of other similar or equivalent processes or algorithms and data representations may be attempted by those skilled in the art. Further, the various names used for the different elements, functions and algorithms (e.g., etc.) are merely descriptive and are not intended to be read in a limiting sense, as these various elements, functions and algorithms can be referred to by any suitable names. All such and similar modifications of the teachings of this invention will still fall within the scope of the embodiments of this invention.

Furthermore, while described above primarily in the context of travel solutions provided by airlines (air carriers), those skilled in the art should appreciate that the embodiments of this invention are not limited for use only with airlines, but could be adapted as well for use with other types of travel modalities and travel providers including, as non-limiting examples, providers of travel by ship, train, motorcar, bus and travel products such as hotels.

Furthermore, some of the features of the exemplary embodiments of the present invention may be used to advantage without the corresponding use of other features. As such, the foregoing description should be considered as merely illustrative of the principles, teachings and embodiments of this invention, and not in limitation thereof.

The program code embodying the software program instructions of various exemplary embodiments described herein is capable of being distributed as a program product in a variety of different forms. In particular, the program code may be distributed using a computer readable media, which may include computer readable storage media and communication media. Computer readable storage media, which is inherently non-transitory, may include volatile and non-volatile, and removable and non-removable tangible media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computer readable storage media may further include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid state memory technology, portable compact disc read-only memory (CD-ROM), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and which can be read by a computer. Communication media may embody computer readable instructions, data structures or other program modules. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above may also be included within the scope of computer readable media.