Contextual state of changed data structures

A method, system, and/or computer program product associates new contextual state information about a changed data structure with a beneficial target data. Event data, which describes an event that modifies a data structure, is incorporated into the data structure to create a changed data structure state. New contextual state information, which describes the changed data structure state, is generated and transmitted to multiple beneficial target data stores, wherein applying the new contextual state information to a particular beneficial target data affects an activity that utilizes the particular beneficial target data.

BACKGROUND

The present disclosure relates to the field of computers, and specifically to the use of data structures in computers. Still more particularly, the present disclosure relates to contextual states of changes to data structures.

A data structure is a software object, such as a database, a collection of software instructions, a table, etc. Data structures may be updated by event data, which include, but are not limited to, a description of a real-time event, a call from a software routine, an input to a table, etc. Thus, data structures provide a structure for data that describes and/or is modified by events.

SUMMARY

A method, system, and/or computer program product associates new contextual state information about a changed data structure with a beneficial target data. Event data, which describes an event that modifies a data structure, is incorporated into the data structure to create a changed data structure state. New contextual state information, which describes the changed data structure state, is generated and transmitted to multiple beneficial target data stores, wherein applying the new contextual state information to a particular beneficial target data affects an activity that utilizes the particular beneficial target data.

DETAILED DESCRIPTION

With reference now to the figures, and in particular toFIG. 1, there is depicted a block diagram of an exemplary system and network that may be utilized by and in the implementation of the present invention. Note that some or all of the exemplary architecture, including both depicted hardware and software, shown for and within computer102may be utilized by software deploying server150and/or event data generating computer152and/or beneficial target data store server154.

Exemplary computer102includes a processor104that is coupled to a system bus106. Processor104may utilize one or more processors, each of which has one or more processor cores. A video adapter108, which drives/supports a display110, is also coupled to system bus106. System bus106is coupled via a bus bridge112to an input/output (I/O) bus114. An I/O interface116is coupled to I/O bus114. I/O interface116affords communication with various I/O devices, including a keyboard118, a mouse120, a media tray122(which may include storage devices such as CD-ROM drives, multi-media interfaces, etc.), a printer124, and external USB port(s)126. While the format of the ports connected to I/O interface116may be any known to those skilled in the art of computer architecture, in one embodiment some or all of these ports are universal serial bus (USB) ports.

As depicted, computer102is able to communicate with a software deploying server150, using a network interface130. Network interface130is a hardware network interface, such as a network interface card (NIC), etc. Network128may be an external network such as the Internet, or an internal network such as an Ethernet or a virtual private network (VPN).

Application programs144in computer102's system memory (as well as software deploying server150's system memory) also include a contextual state information generation and association logic (CSIGAL)148. CSIGAL148includes code for implementing the processes described below, including those described inFIGS. 2-5. In one embodiment, computer102is able to download CSIGAL148from software deploying server150, including in an on-demand basis, wherein the code in CSIGAL148is not downloaded until needed for execution. Note further that, in one embodiment of the present invention, software deploying server150performs all of the functions associated with the present invention (including execution of CSIGAL148), thus freeing computer102from having to use its own internal computing resources to execute CSIGAL148.

With reference now toFIG. 2, a block diagram200of events that lead to an association of new contextual state information and/or new contextual state metadata with multiple beneficial target data stores, in accordance with one embodiment of the present invention, is presented.

Event data202(e.g., from an event data generating computer152shown inFIG. 1) is first incorporated into a data structure204. As described in further detail herein, event data202describes an event that modifies data structure204. This forms a result206of a changed data structure state208. That is, the changed data structure state208describes a new condition of the data structure204, rather than the data structure204itself. In one embodiment, the event data202is generated by a computer (e.g., event data generating computer152) that monitors a particular set of initial activities (e.g., bank deposits being made, weather events occurring, etc.).

The changed data structure state208enables the generation210of new contextual state information212and/or new contextual state metadata214. The new contextual state information212describes the changed data structure state208, and the new contextual state metadata214describes the new contextual state information212.

The new contextual state information212and/or new contextual state metadata214is then transmitted (via associations216) to multiple beneficial target data stores218a-218n(where “n” is an integer), where applying the new contextual state information212and/or new contextual state metadata214to a particular beneficial target data (from218a-218n) affects an activity that utilizes that particular beneficial target data. The generation210of the new contextual state information212and/or new contextual state metadata214, as well as the associations216, may be performed/determined by reference to a rule set and/or a table of similar data structure. In one embodiment of the present invention, the multiple beneficial target data stores218a-218nare stored in and/or are accessible from a beneficial target data store server, such as beneficial target data store server154depicted inFIG. 1.

Thus, with reference now toFIG. 3, an exemplary table300(which in one embodiment is a lookup table) that associates event data with contextual state information, and contextual state information with beneficial target data stores, is presented in accordance with one embodiment of the present invention. In one embodiment, some or all of the associations depicted in table300are made by compliance with one or more rules, as described in exemplary detail below. As depicted by table300, the present invention may be incorporated into activities that are similar or unique for different industries/fields. The following embodiments are described with reference to bothFIG. 3andFIG. 2.

For example, consider an embodiment of the present invention that is utilized in the field of banking. An exemplary event may be that a customer makes a deposit of $1,000 into his bank account. This results in event data202of “$1,000”, which is incorporated into the data structure204that describes her bank account. A result206of this deposit is that her bank account now has $100,000 as a current balance (as described by changed data structure state208). This newly changed data structure state208enables the generation210(e.g., using software that uses information from the changed data structure state208) of new contextual state information212, which describes her bank account as now being at a next tier (e.g., at the “Platinum” level of accounts, as defined by that bank's rules/criteria). This newly generated new contextual state information212(e.g., “this account just reached the ‘Platinum’ level”) is sent to multiple beneficial target data stores218a-218n, which may be internal (i.e., are used by the bank with which this customer has her account), external (i.e., are used by other banks), or disparate (i.e., are used by non-banking enterprises). In one embodiment, the beneficial target data found in the beneficial target data stores218a-218naffect an activity that utilizes the beneficial target data.

For example, consider a bank's training schedule. This training schedule utilizes a particular data and/or programming software to implement, monitor, and/or upgrade the bank's training schedule and its activities. The new contextual state information212describing the customer's account reaching “Platinum” status is received by the beneficial target data that is used to update/manage the bank's training schedule, either by implementation of a rule or by insertion of data/instructions into software (which is the beneficial target data).

For example, assume that software is monitoring a particular beneficial target data, which is related to the bank's training schedule, for incoming new contextual state information212. Assume further that receipt of the new contextual state information212(by the particular beneficial target data) triggers a rule, which states “If an account reaches ‘Platinum’ status, then send an on-line training video, to the banker who is assigned to that account, regarding how to service ‘Platinum’ accounts.” Thus, when the new contextual state information212is inserted into this beneficial target data, the monitoring software implements the associated rule and sends the training video to the responsible banker.

In another embodiment, however, the new contextual state information212, and/or the new contextual state metadata214(e.g., a phrase/term such as “New Platinum Account”, which describes the new contextual state information) is inserted directly into monitoring software, which has been programmed to respond to such data insertion by automatically sending the training video to the responsible banker. That is, in one embodiment the phrase/term “New Platinum Account” is actually a coded flag that, if present in a particular set of software (i.e., a particular beneficial target data), causes this particular set of software to automatically send the training video to the responsible banker.

Continuing with the banking embodiment/example described inFIG. 3, assume that the beneficial target data is related to a bank bonus allocation process (i.e., “activity”). Assume further, for exemplary purposes, that the new contextual state information212and/or new contextual state metadata214that describes the changed data structure state208is (or conveys the concept of) a “New Platinum Account”. A transmission of this new contextual state information212and/or new contextual state metadata214to a beneficial target data in beneficial target data store218a(which handles beneficial target data related to the bank bonus allocation process) causes a rule to be invoked and/or software to execute a process. That is, new contextual state information212and/or new contextual state metadata214indicative of the “New Platinum Account” may 1) causes monitoring software to invoke a rule, which results in the responsible banker receiving a bonus payment, and/or 2) result in software using the input “New Platinum Account” (e.g., a coded flag) to send the responsible banker her bonus payment.

In one embodiment, the industry/field in which the initial event occurred is different (i.e., disparate, dissimilar, etc.) from an industry/field that is able to benefit from the resulting new contextual state information212and/or new contextual state metadata214. For example, consider an embodiment in which a particular beneficial target data affects the activity of forecasting the national economy. That is, forecasting activities rely, in whole or in part, on the data found in that particular beneficial target data. When new contextual state information212and/or new contextual state metadata214is incorporated into this particular beneficial target data (in order to invoke a rule and/or to cause a certain software application output, as described above), then the activity of forecasting the nation's economy is affected. Thus, new contextual state information212and/or new contextual state metadata214related to “New Platinum Account” (and ultimately, the account deposit that initiated the event and event data202) will automatically affect this economic forecast, either directly (as an input to a forecasting software program) or indirectly (by invoking a rule).

Consider now an embodiment of the present invention in which an event from the field of meteorology (i.e., weather forecasting) affects an activity that utilizes a particular beneficial target data, as also described inFIG. 3. Assume that the event that prompted the generation of event data202is a weather event, such as a hurricane. This results (206) in a changed data structure state208to data structure204, which is a record of rainfall amounts. The changed data structure state208enables the generation (210) of new contextual state information212and/or new contextual state metadata214, which describes the updated record of rainfall amounts, such as “Flooding”. That is, “Flooding” is a contextual state and/or metadata that describe the updated record of rainfall amounts. In a similar process as that described above (i.e., where a rule is invoked and/or a software program receives an input to generate an output), new contextual state information212and/or new contextual state metadata214, which describes the weather event and/or updated record of rainfall amounts, changes a weather forecast data. This weather forecast data is a beneficial target data that is used when making forecasts. As described herein, the beneficial target data is either a set of reference data or an executable software program, either of which affect the activity of predicting the weather. Similarly, the new contextual state information212and/or new contextual state metadata214that describes the weather event may be utilized by activities other than weather prediction, such as managing a construction schedule for a new building, predicting agricultural crop output, etc.

Consider now an embodiment of the present invention in which an event from the field of medicine affects an activity that utilizes a particular beneficial target data, as also described inFIG. 3. Assume that the event that prompted the generation of event data202is a particular set of medical test results being generated for one or more patients. Examples of such medical test results are levels of metabolites in bodily fluids, presence/absence of pathogens in a patient, identification of abnormalities to structures/organs (or normalcy thereof) in patients, etc. The event ultimately results in a changed data structure state208(e.g., state of medical records), which enables the generation of new contextual state information212and/or new contextual state metadata214that describes the updated medical records, which include data about medical test results. In a similar process as that described above (i.e., where a rule is invoked and/or a software program receives an input to generate an output), new contextual state information212and/or new contextual state metadata214, which relates to the medical test results, changes the beneficial target diagnostic data that is used in the activity of diagnosing diseases of one or more patients. As described herein, the beneficial target diagnostic data is either a set of reference data or an executable software program, either of which affect the activity of making medical diagnoses. Similarly, the new contextual state information212and/or new contextual state metadata214that relates to the medical test results may be utilized by beneficial target data used in establishing a plan of medical treatment for one or more patients. Similarly, new contextual state information212and/or new contextual state metadata214that relates to the medical test results may be utilized to update activities in a medical study related to the medical test results.

Note that the new contextual state information212and/or new contextual state metadata214that relate to the medical test results inFIG. 3may be utilized in a non-medical context. For example, new contextual state information212and/or new contextual state metadata214that relates to the medical test results may be utilized when setting financial reserves with an insurance company. That is, new contextual state information212and/or new contextual state metadata214that relates to the medical test results may invoke a rule in software, which monitors the beneficial target data (used when setting financial reserves), to increase financial reserves for a particular patient/set of patients having a certain disease. Similarly, new contextual state information212and/or new contextual state metadata214that relates to the medical test results may be utilized (e.g., as a coded flag) in software that automatically sets financial reserves for a particular claim file, set of related claims for a similar disease, etc.

Consider now an embodiment of the present invention in which an event from the field of manufacturing affects an activity that utilizes a particular beneficial target data, as also described inFIG. 3. Assume that the event that prompted the generation of event data202is an increase or decrease in manufacturing orders (i.e., new orders coming in or old orders being cancelled). As a result (206) of these changes in manufacturing orders, a changed data structure state208of a data structure204(e.g., order backlog records) results, which enables the generation (210) of new contextual state information212and/or new contextual state metadata214that describes the “Updated Backlog Data”. Examples of such new contextual state information212and/or new contextual state metadata214include, but are not limited to, descriptive terms such as “Six-month Backlog”, “Inadequate Backlog” (as defined by rules/guidelines that have been set by the manufacturer), etc. In a similar process as that described above (i.e., where a rule is invoked and/or a software program receives an input to generate an output), new contextual state information212and/or new contextual state metadata214that describes the “Updated Backlog Data” changes the beneficial target data that is used in ordering raw materials used in manufacturing. As described herein, the beneficial target data is either a set of reference data or an executable software program, either of which affect the activity of ordering raw materials. Similarly, the new contextual state information212and/or new contextual state metadata214that describes the “Updated Backlog Data” may be utilized by beneficial target data used in establishing and/or executing a plan for hiring additional workers.

Note that the new contextual state information212and/or new contextual state metadata214that describes the “Updated Backlog Data” may be utilized in a non-manufacturing context. For example, new contextual state information212and/or new contextual state metadata214that describes the “Updated Backlog Data” may be utilized when forecasting a politician's electability. That is, many election contests are based on economic conditions at the time of the election. Thus, new contextual state information212and/or new contextual state metadata214that describes the “Updated Backlog Data” may invoke a rule in software that monitors the beneficial target data (used when predicting economic conditions, and thus political viability) to adjust a prediction of voter polls for a particular politician. Similarly, new contextual state information212and/or new contextual state metadata214that describes the “Updated Backlog Data” may be utilized (e.g., as a coded flag) in software that automatically creates political polling projections.

Consider now an embodiment of the present invention in which an event from the field of research affects an activity that utilizes a particular beneficial target data, as also described inFIG. 3. Research is defined as the field of systematically increasing knowledge about a particular subject, through the study of known information in order to extrapolate new knowledge, and/or by observation of previously unobserved/unstudied objects and/or processes. Assume that the event that prompted the generation of event data202is a new research discovery (e.g., the discovery of a new type of material). Due to this discovery, a changed data structure state208(e.g., an updated list of known materials) results, which enables the generation of new contextual state information212and/or new contextual state metadata214that describes the newly discovered material. An example of such new contextual state information212and/or new contextual state metadata214is “New Material”. In a similar process as that described above (i.e., where a rule is invoked and/or a software program receives an input to generate an output), new contextual state information212and/or new contextual state metadata214that describes the newly discovered material changes the beneficial target data that is used in funding research projects. As described herein, the beneficial target data is either a set of reference data or an executable software program, either of which affect the activity of funding research.

Note that the new contextual state information212and/or new contextual state metadata214that describes the newly discovered material may be utilized in a non-research context. For example, new contextual state information212and/or new contextual state metadata214that describes the newly discovered material may be utilized when forecasting the future of the environment (pollution, climate, etc.). That is, environmental changes may be caused by a research breakthrough in which a new material is able to reduce pollution, lower greenhouse effects, etc. Thus, new contextual state information212and/or new contextual state metadata214that describes the newly discovered material may invoke a rule in software that monitors the beneficial target data (used when predicting environmental changes) to adjust a prediction of changes to the environment. Similarly, new contextual state information212and/or new contextual state metadata214that describes the newly discovered material may be utilized (e.g., as a coded flag) in software that automatically creates environmental predictions.

Consider now an embodiment of the present invention in which an event from the field of agriculture affects an activity that utilizes a particular beneficial target data, as also described inFIG. 3. Assume that the event that prompted the generation of event data202is a poor crop harvest (i.e., the harvest of a particular crop such as corn is well below average). As a result of this poor crop harvest, a changed data structure state208(e.g., an updated corn tonnage list of how much corn is in silos) results, which enables the generation of new contextual state information212and/or new contextual state metadata214that describes the poor crop harvest. Examples of such new contextual state information212and/or new contextual state metadata214include, but are not limited to, “Reduced Corn Harvest”, “5,000 tons”, etc. In a similar process as that described above (i.e., where a rule is invoked and/or a software program receives an input to generate an output), new contextual state information212and/or new contextual state metadata214that describes the poor crop harvest changes the beneficial target data that is used in adjusting prices on the commodity futures market. As described herein, the beneficial target data is either a set of reference data or an executable software program, either of which affect the pricing of commodity futures (i.e., speculations on the future prices of a particular commodity).

Note that the new contextual state information212and/or new contextual state metadata214that describes the poor crop harvest may be utilized in a non-agricultural context, such as grocery retailing, food services such as restaurant management, etc. For example, new contextual state information212and/or new contextual state metadata214that describes the updated corn tonnage may be utilized when setting a price for prepared food (e.g., packaged food sold in a grocery store, freshly-prepared food served in a restaurant, etc.). Thus, new contextual state information212and/or new contextual state metadata214that describes the updated corn tonnage may invoke a rule in software that monitors the beneficial target data (used to set prepared food prices) to adjust these prepared food prices. Similarly, new contextual state information212and/or new contextual state metadata214that describes the updated corn tonnage may be utilized (e.g., as a coded flag) in software that automatically sets the current price of prepared food.

Similarly, new contextual state information212and/or new contextual state metadata214that describes the updated corn tonnage may be utilized when making predictions about public health. For example, lower corn output may result in lower high-fructose corn syrup production, thus driving up the cost of candy, and thus driving down levels of obesity, diabetes, etc. due to decreased demand. Alternatively, lower corn output may drive up the cost of all foods, including feedstock such as livestock feed, thus resulting in higher food prices and higher malnutrition rates (e.g., due to increased beef prices caused by the more expensive feedstock). Thus, new contextual state information212and/or new contextual state metadata214that describes the updated corn tonnage may invoke a rule in software that monitors the beneficial target data (used to predict public health conditions) to adjust these predictions. Similarly, new contextual state information212and/or new contextual state metadata214that describes the updated corn tonnage may be utilized (e.g., as a coded flag) in software that automatically generates public health forecasts.

As depicted inFIG. 2, new contextual state information212and/or new contextual state metadata214is associated with multiple beneficial target data found in the beneficial target data stores218a-218n. In one embodiment, determining which beneficial target data is to be associated with the new contextual state information212and/or new contextual state metadata214is set by probabilistic impact data. Probabilistic impact data is defined as data that describes a predicted impact level that new contextual state information212and/or new contextual state metadata214will have on beneficial target data.

For example, assume that new contextual state information212and/or new contextual state metadata214describes a banking account's tier level (e.g., “New Platinum Account”). Information that describes this “New Platinum Account” will have a 100% impact on beneficial target data that is used to identify all “Platinum Accounts” in that bank. That is, “New Platinum Account” has a 100% probability of enabling/affecting activities related to identifying “Platinum Accounts” in that bank. However, this same new contextual state information212and/or new contextual state metadata214that describes this “New Platinum Account” will have (for example) only a 60% impact on beneficial target data that is used to identify the overall financial health of this bank. That is, “New Platinum Account” will have only a 60% likelihood of enabling/affecting the process of identifying the overall financial health of this bank.

Furthermore, the new contextual state information212and/or new contextual state metadata214that describes a “New Platinum Account” may have a negative impact on beneficial target data. For example, information that describes this “New Platinum Account” will have a 100% negative impact on preventing beneficial target data that is used to prove that no new account levels have been reached, since the description of a “New Platinum Account” makes the proof of no new account levels impossible. That is, if a particular beneficial target data receives “New Platinum Account”, then there is a 100% certainty that proving that there are no new account levels cannot occur.

Furthermore, new contextual state information212and/or new contextual state metadata214that describes the “New Platinum Account” may have 0% impact on data used to identify “Closed Accounts” in that bank, since “New Platinum Accounts” have nothing to do with “Closed Accounts”.

This probabilistic impact data (between +1.0 (i.e., +100%) and −1.0 (i.e., −100%)) may be set by an explicit or implicit rule set, a table, by Bayesian analytics, and/or by statistical formulations/techniques. For example, a rule may state that if the new contextual state information212and/or new contextual state metadata214contain a term such as “account”, then there is a 50% chance that it will have a 90% positive impact on activities that use a beneficial target data that has the term “amount”.

In one embodiment, a Bayesian analysis is used to establish a probabilistic impact datum. This Bayesian analysis utilizes the formula:

P⁡(A❘B)=P⁡(B❘A)×P⁡(A)P⁡(B)
where:
P(A|B) is the probability that the particular new contextual state information212and/or new contextual state metadata214(e.g., which contains the term “account”), has a 90% positive impact on activities that use a particular beneficial target data (A) given that (I) the particular beneficial target data contains the term “amount” (B);
P(B|A) is the probability that the particular beneficial target data contains the term “amount” (B) given that (I) the particular new contextual state information212and/or new contextual state metadata214actually have a 90% positive impact on activities that use a particular beneficial target data (A);
P(A) is the probability that the particular new contextual state information212and/or new contextual state metadata214has a 90% positive impact on activities that use a particular beneficial target data regardless of any other information; and
P(B) is the probability that the particular beneficial target data contains the term “amount” regardless of any other information.

P⁡(A❘B)=(.70)⁢(.80)(.90)=.62
then P(A|B)=0.62, and thus, the probabilistic impact datum is 62%.

In one embodiment of the present invention, the probabilistic impact data and/or new contextual state information212and/or new contextual state metadata214have a provenance. The term “provenance” is defined as a recorded trail of the life of an object, from its original creation to its current position/usage, and may include all intervening locations/users that have used and/or modified the object.

Thus, in one embodiment of the present invention, the provenance is established for probabilistic impact data. For example, assume that a particular new probabilistic impact data was named NPID′, had a probability value of 50%, and was created by program P on date D. Assume further that NPID′ was later adjusted by program PP on date DD, to become NPID″. A record of where, when, and by what/whom the various versions of NPID were created/modified is stored in a probabilistic provenance audit trail, which may be a single path, a tree network, a graph structure, etc.

Furthermore, in one embodiment of the present invention, the provenance is established for new contextual state information (e.g., element212shown inFIG. 2). For example, assume that a particular new contextual state information was initially named NCSI′, and was created by program P on date D. Assume further that NCSI′ was later modified by program PP on date DD, to become NCSI″. Assume further that NCSI′ was 1) sent to beneficial target data store218aon date DY, where records (e.g., data that maps particular activities to particular data stores) mapped to a particular event E indicate that 2) NCSI′ had no impact/effect on activity A. However, when NCSI″ was 1) sent to beneficial target data store218aon date DZ, mapped records indicate that 2) NCSI″ caused activity A to be altered. All of this data is included in a state provenance audit trail for NCSI′/NCSI″. Note that this state provenance audit trail is not just a set of time stamps and modification flags, but rather includes information about how new contextual state information 1) is modified, 2) is used, and 3) affects various activities that utilize the beneficial target data described herein. A record of where, when, how, and for what/whom the various versions of NCSI affected certain activities is stored in a probabilistic provenance audit trail, which may be a single path, a tree network, a graph structure, etc.

With reference now toFIG. 4, a high-level flow chart of one or more steps performed by one or more processors to associate new contextual state information about a changed data structure with a beneficial target data is presented. After initiator block402, which may be initiated by a particular event, event data that describes that particular event is received (block404). This event data describes an event that modifies a data structure, such as described in exemplary detail above inFIGS. 2-3. The event data is then incorporated into the data structure to create a changed data structure state (block406), as described by elements204-208inFIG. 2.

As described in block408, new contextual state information about the changed data structure state is then generated. This new contextual state information describes the changed data structure state (e.g., provides a new description of a bank account as being a “Platinum Account”). As described in block410, the new contextual state information is then transmitted (e.g., by one or more processors via an electronic communication pathway) to multiple beneficial target data stores. As described herein, applying the new contextual state information to a particular beneficial target data affects an activity that utilizes the particular beneficial target data. The process ends at terminator block412.

As described above, the event data (e.g., element202inFIG. 2) is generated in response to an actual event (e.g., the deposit of money into a bank account). However, in one embodiment of the present invention, the event data is generated in response to a non-event. For example, assume that a bank customer routinely deposits $100 into her account every Friday, and has done so for the past 100 weeks without fail. If this bank customer fails to make her usual $100 Friday deposit for a week or two, this will be deemed (e.g., by a rule found in a monitoring software program) to be a non-event, which affects the normal status of the data structure202, which ultimately affects the generation of the new contextual state information212and/or the new contextual state metadata214shown inFIG. 2.

In one embodiment of the present invention, and as discussed above with reference to element214inFIG. 2, contextual state metadata that describes the new contextual state information is generated and then transmitted (e.g., by one or more processors via the electronic communication pathway) to the multiple beneficial target data stores.

In one embodiment of the present invention, a determination (i.e., creation) of a probabilistic impact data associated with the new contextual state information and a specific activity that utilizes a specific beneficial target data is made. This probabilistic impact data describes a probability of the new contextual state information affecting the specific activity that utilizes the specific beneficial target data. In one embodiment, in response to the probabilistic impact data having a value that is absolutely less than a predetermined value, the transmission of the new contextual state information to the specific beneficial target data is blocked. In one embodiment of the present invention, a determination of a probability provenance of the probabilistic impact data is made, where the probability provenance describes a source and subsequent modification of the probabilistic impact data.

In one embodiment of the present invention, the new contextual state information is associated with the multiple beneficial target data stores according to a rule set for associating the contextual information with one or more beneficial target data. In another embodiment, the new contextual state information is associated with the multiple beneficial target data stores according to a lookup table for associating the contextual information with one or more beneficial target data.

In one embodiment of the present invention, each beneficial target data store contains beneficial target data associated with disparate industries (e.g., as depicted inFIG. 3).

In one embodiment of the present invention, the event data describes a non-event, where the non-event describes a non-occurrence of an expected event.

In one embodiment of the present invention, the event data is a first input to a first software application program, the new contextual state information is a second input to a second software application program, and a particular beneficial target data is the second software application program. For example, inFIG. 2the event data202may be a data input (e.g., “$100”) to a first software application program (i.e., where data structure204inFIG. 2is actually an application program). This results in the generation of the new contextual state information212and/or new contextual state metadata214, either or both of which may be a data input (e.g., “Value X”) into a second software application (where a beneficial target data, such as that found in beneficial target data store218ainFIG. 2, is actually a software application).

In one embodiment of the present invention, one or more processors sort the multiple beneficial target data stores into different commercial activity fields, such as those depicted inFIG. 3.

In one embodiment of the present invention, a graph data structure is used to identify relationships among new contextual state information/metadata and beneficial target data. A graph data structure consists of multiple nodes, each of which contain data, and whose relationships are described by edges (e.g., pointers) between the nodes. For example, in the graph data structure500depicted inFIG. 5, new contextual state nodes502x-502zcontain data found in new contextual state information212and/or the new contextual state metadata214(depicted inFIG. 2). Beneficial target data nodes504a-504ccontain beneficial target data found in beneficial target data stores218a-218cshown inFIG. 2. Thus, edge506rdescribes the relationship, caused by implementation of a rule, entries in a lookup table, etc., between a particular new contextual state information212and/or the new contextual state metadata214(contained within a new contextual state node502x) and beneficial target data504a. Similarly, edge506sdescribes the relationship, caused by implementation of a rule, entries in a lookup table, etc., between a different particular new contextual state information212and/or the new contextual state metadata214(contained within a new contextual state node502y) and beneficial target data504b, while edge506tdescribes the relationship, caused by implementation of a rule, entries in a lookup table, etc., between new contextual state information in new contextual state node502yand beneficial target data504c.

Similarly, edge506udescribes the relationship between new contextual state node502zand new contextual state node502y. For example, the new contextual state information212and/or the new contextual state metadata214found in these respective new contextual state nodes502y/502zmay be related according to a lookup table, rule, etc., that identifies them as both pertaining to the same field, such agriculture, manufacturing, etc.