ARTIFICIAL INTELLIGENCE DRIVEN LOG EVENT ASSOCIATION

A computer implemented method processes log events. A computer system determines dependence and independence of training log events. The computer system clusters the training log events with determined dependence and independence to form clustered training log events. The computer system generates an event association model using the clustered training log events. The computer system clusters the log events using the event association model.

BACKGROUND

The disclosure relates generally to an improved computer system and more specifically to a computer implemented method, apparatus, system, and computer program product that clusters log events in logs files.

2. Description of the Related Art

Log files are generated during the operation of software and hardware. These log files are basic resources that provide information about the operation of software and hardware. Log files can be generated in response to defects related to the user experience. For example, a log event can be generated when an error occurs in the operation of a user interface. Log events can include information about an unexpected behavior that occurs when a functionality of a software component is used. For example, a log event can be generated when a user clicks a send email button in an email application and the email application does not send the email but generates an error.

Log events also can be generated for system level events. For example, a log event can be generated when an unexpected behavior occurs in response to multiple users accessing a shared resource, such as a table in a database or a shared document for an online word processor. As another example, log events can be generated for performance issues in which a noticeable performance slow down occurs. For example, a log event can be generated when the time to spell check a word document is greater than a threshold.

A log file contains lines of information in which each line represents information for a log event. These log events are examined to understand the activity of a system and diagnose problems. For example, the analysis of log events is performed to identify issues for a system such as an operating system, an application, a database, a network, or other system.

SUMMARY

According to one illustrative embodiment, a computer implemented method processes log events. A computer system determines a dependence and an independence of training log events such that the training log events have a determined dependance and independence. The computer system clusters the training log events with the determined dependence and independence to form clustered training log events. The computer system generates an event association model using the clustered training log events. The computer system clusters the log events using the event association model. According to other illustrative embodiments, a computer system and a computer program product for processing log events are provided.

DETAILED DESCRIPTION

With reference now to the figures, in particular with reference toFIG.1, a block diagram of a computing environment is depicted in accordance with an illustrative embodiment. Computing environment100contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as improved log event processor190. In the illustrative example, improved log event processor190operates to process log files containing log events to associate log events with each other. This association of log events can be based on the dependency or independence of log events as well as similarity of log events. In the illustrative examples, improved log event processor190can employ event association models associate log events with each other in clusters. In addition to improved log event processor190, computing environment100includes, for example, computer101, wide area network (WAN)102, end user device (EUD)103, remote server104, public cloud105, and private cloud106. In this embodiment, computer101includes processor set110(including processing circuitry120and cache121), communication fabric111, volatile memory112, persistent storage113(including operating system122and improved log event processor190, as identified above), peripheral device set114(including user interface (UI) device set123, storage124, and Internet of Things (IoT) sensor set125), and network module115. Remote server104includes remote database130. Public cloud105includes gateway140, cloud orchestration module141, host physical machine set142, virtual machine set143, and container set144.

The illustrative embodiments recognize and take into account a number of different considerations as described herein. For example, the illustrative embodiments recognize and take into account that current techniques for analyzing log events are difficult to automate. For example, when rules are used to associate one log event with another log event, these rules only provide event correlation when a mapping is provided from the one log event to another log event. These rules, however, cannot determine whether one log event is dependent or independent with respect to another log event. As result, an incorrect conclusion and correlation can occur using rule-based systems to process log events.

The illustrative embodiments recognize and take account that a solution that can cluster events based on assessment of dependency or independence can provide a higher degree of precision in associating log events with each other. Thus, the illustrative embodiments provide a computer implemented method, apparatus, system, and computer program product for processing log events. In one illustrative example, a computer system determines a dependence and an independence of training log events such that the training log events have a determined dependance and independence. The computer system clusters the training log events with the determined dependence and independence to form clustered training log events. The computer system generates an event association model using the clustered training log events with the determined dependence and independence. The computer system clusters the log events using the event association model that clusters the log events based on the dependence and the independence of the log events.

With reference now toFIG.2, a block diagram of an event processing environment is depicted in accordance with an illustrative embodiment. In this illustrative example, event processing environment200includes components that can be implemented in hardware such as the hardware shown for computing environment100inFIG.1. In this illustrative example, event processing environment200includes log processing system202that can process log events204in as set of log files205. In this illustrative example, log processing system202comprises computer system206and event processor208. As depicted, event processor208is located in computer system206. In this example, event processor208is an example of improved log event processor190inFIG.1.

In the illustrative examples, the hardware can take a form selected from at least one of a circuit system, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device, or some other suitable type of hardware configured to perform a number of operations.

As used herein, “a number of” when used with reference to items, means one or more items. For example, “a number of operations” is one or more operations.

With a programmable logic device, the device can be configured to perform the number of operations. The device can be reconfigured at a later time or can be permanently configured to perform the number of operations. Programmable logic devices include, for example, a programmable logic array, a programmable array logic, a field programmable logic array, a field programmable gate array, and other suitable hardware devices. Additionally, the processes can be implemented in organic components integrated with inorganic components and can be comprised entirely of organic components excluding a human being. For example, the processes can be implemented as circuits in organic semiconductors.

As depicted, computer system206includes a number of processor units210that is capable of executing program instructions211implementing processes in the illustrative examples. As used herein a processor unit in the number of processor units210is a hardware device and is comprised of hardware circuits such as those on an integrated circuit that respond and process instructions and program instructions that operate a computer.

When the number of processor units210execute program instructions211for a process, the number of processor units210is one or more processor units that can be on the same computer or on different computers. In other words, the process can be distributed between processor units on the same or different computers in a computer system. Further, the number of processor units210can be of the same type or different type of processor units. For example, the number of processor units210can be selected from at least one of a single core processor, a dual-core processor, a multi-processor core, a general-purpose central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), or some other type of processor unit. A processor unit can be implemented using processor set110inFIG.1.

As depicted, event processor208can process log events204using event association model212. In this illustrative example, event association model212can be machine learning model214that has been trained to cluster log events204taking into account least one of dependence215or independence216of log events204. In this example, the clustering performed by event association model212can also be based on similarity218of log events to each other.

A machine learning model is a type of artificial intelligence model that can learn without being explicitly programmed. A machine learning model can learn based on training data being input into the machine learning model. The machine learning model can learn using various types of machine learning algorithms. The machine learning algorithms include at least one of a supervised learning, and unsupervised learning, a feature learning, a sparse dictionary learning, an anomaly detection, a reinforcement learning, a recommendation learning, or other types of learning algorithms.

Examples of machine learning models include an artificial neural network, a convolutional neural network, a decision tree, a support vector machine, a regression machine learning model, a classification machine learning model, a random forest learning model, a Bayesian network, a genetic algorithm, and other types of models. These machine learning models can be trained using data and process additional data to provide a desired output.

In this illustrative example, event processor208determines dependence215and independence216of training log events220. In this example, the processing results in training log events220having determined dependence and independence222. Event processor208clusters training log events220with determined dependence and independence222to form a set of clusters224of clustered training log events226. In this illustrative example, event processor208generates the event association model212using clustered training log events226with determined dependence and independence222.

As used herein, a “set of” when used with reference items means one or more items. For example, a set of clusters224is one or more of clusters224.

Additionally, event processor208can determine similarity227between training log events220. Similarity227of training log events220can also be used in generating event association model212. When similarity227is used, similarity227can determined before determining dependence215and independence216of training log events220.

In this illustrative example, event processor208can process log events204using the event association model212after training of this model using training log events220. In the illustrative example, the event association model212can cluster log events204into clusters240. In the illustrative example, the clustering of log events204into clusters240can be performed irrespective of the order in which log events are present within log files205.

These clusters can be analyzed to identify issues and solutions to issues identified from analyzing log events204placed into clusters240. With a higher degree of accuracy in clustering log events204into clusters240based on dependence215and independence216, more accurate analysis can be performed with the increase precision in event association. This increase in precision can result in increased accuracy in identifying issues and actions to resolve issues in different systems in this illustrative example.

With reference now toFIG.3, a block diagram illustrating generating an event association model is depicted in accordance with an illustrative embodiment. In this illustrative example, event processor208generates training dataset300for training event association model212which is implemented using machine learning model214.

In this illustrative example, event processor208can generate event association model212that includes dependence determination process371and clustering process373as functions. Dependence determination process371determines dependence215of log events204, and clustering process373clusters log events204with dependence215determined for log events204. In other words, the clustering can cluster log events based on dependence215determined for log events204. In clustering log events204, the determination of dependence215is used to cluster log events204in this example.

In this depicted example, dependence determination process371can be implemented using Fisher's exact test. In other examples, Barnard's test or other suitable test can be used for dependence determination process371. In this example, independence216can be determined based on the dependence215using dependence determination process371. For example, if the level of dependence decreases for a log event, then the level of independence increases for the log event.

Clustering process373can be performed using a gaussian mixture model (GMM) that can determine the number clusters based on analysis of training log events220. With a GMM, the number of clusters224to be formed are not input or defined. Instead, the gaussian mixture model can determine the number clusters based on analysis of training log events220.

In other illustrative examples, other types of clustering models or processes can be used in addition to or in place of a GMM. For example, a K-means clustering, a neural model such as principal component analysis, a mean shift model, an agglomerative hierarchical algorithm, or other suitable clustering process or model for clustering log events having dependence and independence can be used.

Event association model212with this functionality can be trained using machine learning algorithms to form machine learning model214. This training of event association model212using machine learning algorithms can be performed using training dataset300.

In generating training dataset300, event processor208can analyze log file302of training log events220. Training log events220are historical log events that are selected for generating training dataset300.

In this illustrative example, in analyzing training log events220, event processor208determines entities305in text308in training log events220. As depicted, log file302comprises lines306in which each line corresponds to a training log event in training log events220. In other words, each line is a log event in these examples.

In this illustrative example, text308in lines306for training log events220can be analyzed to determine entities305. A line in lines306also includes other information such as a timestamp, a subcomponent identifier in addition to text308.

In this illustrative example, event processor208can use various processes to identify entities305in text308. For example, event processor208can use natural language processing algorithms, entity analysis, or other techniques to identify entities of interest303. Text308can be processed to remove stock words. In this depicted example, the removal of stock words leaves entities305in text308that can be analyzed to determine whether entities305are entities of interest303. Entities of interest303can be identified by at least one of a user input or a selection by machine learning model.

In the depicted example, entities of interest303comprise at least one of a word, words, a phrase, phrases, or other combinations of text that are of interest for clustering log events204. For example, entities of interest303can be “memory overflow”, “database rollback failure”, “massive critical failure”, “handshake error”, “failure”, or other text. For example, relevancy317can be indicated using entity relevance312. Entity relevance312can be a value, for example, from 0 to 1. When entity relevance312is 0, the entity has no relevance to the entity of interest. When entity relevance312is 1, the entity can be an exact match to the entity of interest.

In this example, entities305determined from processing text308can be analyzed with reference to entities of interest303. This analysis can be used to determine relevancy317of entities305to entities of interest303.

In addition, training log events220are analyzed by event processor208for at least one of dependence215or independence216. This analysis performed by event processor208can be used to determine values for dependence215, independence216, or both. For example, a value closer to 1 indicates independence of a training log event and value closer to 0 indicates dependence of a training log event on another training log events. In another illustrative example, dependence can be indicated by a value closer to 1 while independence is indicated by a value closer to 0.

In this illustrative example, event processor208can determine dependence215and independence216using a number of different types of tests or processes. For example, event processor208can use at least one of Barnard's test, Fishers exact test, or other suitable test. With this analysis, training log events220have determined dependence and independence222.

Although the illustrative example uses values between 0 and 1 to indicate dependence215, independence216, and relevancy317, other scales or indicators can be used. For example, a scale from 1 to 5 or 0 to 100 can be used. In other examples, characters, words, or alpha numeric indicators can be used.

In this depicted example, event processor208clusters training log events220with determined dependence and independence222to form clustered training log events226in clusters224. This clustering can also be performed taking into account relevancy317of entities305for training log events220to entities of interest303. As depicted, the clustering of training log events220can be performed by event processor208using various clustering techniques. In this example, event processor208can use a gaussian mixture model (GMM).

In this illustrative example, training dataset300comprises clusters224of clustered training log events226. Dependence215and independence216can be used as labels310for clusters224of clustered training log events226. Additionally, relevancy317can also be used as labels310.

In one illustrative example, labels can take the form of values for dependence215and independence216in which the values are from 0 to 1. Further, relevancy317between entities305in training log events220can be indicated for entity relevance312for labels310. Entity relevance312comprises values for relevancy of text308to entities of interest303in clustered training log events226that can be used as labels310for clusters224of clustered training log events226.

In this illustrative example, labels310can be applied to a cluster in clusters224. Additionally, labels310can also be applied to individual log events in clustered training log events226.

Event processor208can train event association model212by training machine learning model214using training dataset300with a machine learning algorithm. As a result, event processor208trains event association model212to cluster at least one of dependent log events316or independent log events318. In this example, event processor208can train event association model212to cluster dependent log events316, independent log events318, or both dependent log events316and independent log events318. In other words, a single event association model can cluster one type of event or both types of events.

With reference now toFIG.4, a block diagram illustrating clustering using an event association model is depicted in accordance with an illustrative embodiment. As depicted, event processor208can use event association model212to cluster log events204into clusters240. The results of these clusters can be used for analysis or can be displayed to user400in graphical user interface402on display system404.

Further in the illustrative example, event association model212can cluster log events204into clusters240for hierarchical structure405for analysis or display. For example, hierarchical structure405can be displayed in graphical user interface402on display system404.

In this illustrative example, hierarchical structure405can have a hierarchy of dependent log events406in log events204. For example, hierarchy410can be based on dependence values414for log events in dependent log events406that can be placed in levels408in hierarchy410for hierarchical structure405based on dependence values414determined for dependent log events406.

In this example, dependent log events406having higher dependence values are placed in higher levels in levels408as compared to dependent log events406having lower dependence values. For example, a first dependent log event having dependence value of 0.9 is located in a higher level in levels408as compared to a second dependent log event having a dependence value of 0.3.

In one example, three levels can be present in levels408for hierarchy410. The highest level can be for a dependence value of 1 to 0.9, the next level can be for a dependence value that is from 0.5 to less than 0.9, and the lowest level can be for dependence values from 0.0 to less than 0.5. Thus, the location of dependent log events406in levels408can be based on dependence values414.

In another illustrative example, the placement of dependent log events406in hierarchy410can be based on entity relevance312of dependent log events406. In this example, a log event is placed in a higher level in levels408when entity relevance312for the log event is higher as compared to entity relevance312for other log events.

In yet another illustrative example, the placement of dependent log events406in levels408can based on system architecture412. For example, system architecture412can be for a storage management system. The hierarchy within the storage management system can be, for example, the levels of hierarchy from highest to lowest can be as follows: data center, customer, database server, and system activity. In this example, data center is the facility that provides data and is the highest level in levels408. Customers are users that use the data center. Database servers are database servers assigned to customers, and system activity are system activity for a particular database server.

In this example, dependent log events406can be placed within levels408for system architecture based on the type of events. For example, dependent log events406relating to system defects may be placed in the level for data center, dependent log events relating to user experience by customers can be placed in the level for customers. As another example, unexpected behavior of a shared resource such as a database server can be placed in the level for database servers. As another example, dependent log events406relating to performance such as slowdowns in responsiveness by database servers can be in the level for system activity.

The illustration of different types of hierarchies is presented as examples and are not meant to limit the manner in which hierarchical structure405can be structured or organized. For example, hierarchy410can include levels408for system architecture412. Within levels for system architecture412, additional sub levels can be present for at least one of dependence values414or entity relevance312. Further, with graphical user interface402, a user input by user400selecting a dependent event can result in displaying the line for the dependent log event.

In one illustrative example, one or more solutions are present that overcome a problem with clustering log events in which dependencies and independence are factors in clustering events. As result, one or more illustrative examples can cluster events into clusters based on dependence and independence of the log events more accurately as compared to rule-based clustering systems. In the illustrative examples, a training dataset is created that enables an event association model to cluster log events based on dependence or independence of the log events.

Computer system206can be configured to perform at least one of the steps, operations, or actions described in the different illustrative examples using software, hardware, firmware or a combination thereof. As a result, computer system206operates as a special purpose computer system in which event processor208in computer system206enables processing log events in a manner in which the clustering of log events is performed using one or more event association models. These event association models can cluster log events based on dependence and independence. In particular, event processor208transforms computer system206into a special purpose computer system as compared to currently available general computer systems that do not have event processor208.

For example, log events204can be for different types of systems. For example, one log file in log files205can contain log events for the operation of a database. Another log file in log files205can contain log events for failures within a network. In the illustrative examples, different event association models in addition to event association model212can be trained to process different types of log files in log files205to provide clustering of different types of log events with a desired level of accuracy.

As another example, event association model212can be a first event association model that clusters dependent log events316. Event processor208can also train a second event association model to cluster independent log events318. In yet another illustrative example, an independence determination process can be implemented in event association model212in addition to or in place of dependence determination process371.

With reference now toFIG.5, a diagram illustrating a hierarchical data structure for a storage management system is depicted in accordance with an illustrative embodiment. As depicted, hierarchical data structure500shows a hierarchy of levels within storage management system501. Hierarchical data structure500is an example of hierarchical structure405inFIG.4.

In this illustrative example, four levels of hierarchy are present in hierarchical data structure500for storage management system501. In this illustrative example, the levels of hierarchy from highest to lowest within hierarchical data structure500for storage management system501are data center502, customers504, database servers506, and system activity508.

In this example, data center502is a facility that stores, processes, and disseminates data and applications for customers504within storage management system501. A production management suite (PMS) within data center502partitions data center into geographic regions, as depicted by DC01, DC02, and DC03, based on location of customers504, as depicted by C01-008.

Database servers506are assigned to customers504and partitioned to provide database services to customers504. In this example, the partitions are DB01-DB04. System activity508is the system activity for database servers506. In this example, system activity includes CPU %, disk read utilization %, disk write utilization %, disk reads per second, disk writes per second, memory resident size bytes, and memory virtual size bytes.

Entity analysis, clustering analysis, and independence and dependence analysis of event logs can be performed for one or more levels within storage management system501using an event association model. For example, dependence values510and entity relevance512for log events can be determined for a number of levels within hierarchical data structure500for storage management system501by event association model212. In other words, dependence values510and entity relevance512can be determined for event logs within levels selected from at least one of data center502, customers504, database servers506, or system activity508.

In this illustrative example, dependence values510are depicted for log events at the level, customer504and entity relevance512is depicted for log events at a lower level, system activity508, in hierarchical data structure500. Log events can be clustered depending on the relevancy of the event logs to each entity of interest. In other words, in this illustrative example, log events can be clustered based on dependence values510for customers504with entity relevancy512at for system activity508.

In another example, log events can be placed within levels within hierarchical data structure500based on the type of events. For example, log events relating to system defects can be placed in data center502, log events relating to user experience by a customer can be placed in customers504in the levels in hierarchical data structure500. As another example, unexpected behavior of a shared resource for a database server can be placed in the database servers506in hierarchical data structure500. As another example, dependent log events406relating to performance such as slowdowns in responsiveness by database servers506can be placed system activity508in hierarchical data structure500.

With reference toFIG.6, a flowchart of a process for determining a dependence and an independence of training log events is depicted in accordance with an illustrative embodiment. The process inFIG.6can be implemented in hardware, software, or both. When implemented in software, the process can take the form of program instructions that is run by one of more processor units located in one or more hardware devices in one or more computer systems. For example, the process can be implemented in event processor208in computer system206inFIG.2.

The process begins by determining a dependence and an independence of training log events (step600). In step600, the training log events can have a determined dependence and independence. The process clusters the training log events with the determined dependence and independence to form clustered training log events (step602). The process generates an event association model using the clustered training log events (step604). The process clusters log events using the event association model (step606). The process terminates thereafter. In step606, the event association model can cluster the log events based on the dependence and the independence of the log events.

Turning toFIG.7, a flowchart of a process for analyzing a log file of training log events to determine entities is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.7is an example of an additional step that can be used with the steps inFIG.6.

The process analyzes a log file of the training log events to determine entities for the training log events when clustering the training log events (step700). The process terminates thereafter.

Turning next toFIG.8, a flowchart of a process for determining a dependence and an independence of training log events is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.8is an example of one implementation for step600inFIG.6.

The process analyzes the training log events for at least one of the dependence or the independence using at least one of a Fishers exact test or a Barnard's test (step800). The process terminates thereafter.

With reference toFIG.9, a flowchart of a process for clustering training log events with determined dependence and independence is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.9is an example of one implementation for step602inFIG.6.

The process clusters the training log events with the determined dependence and independence to form the clustered training log events using a gaussian mixture model (step900). The process terminates thereafter.

Turning toFIG.10, a flowchart of a process for generating an event association model to cluster log events is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.10is an example of one implementation for step604inFIG.6.

The process trains the event association model to cluster at least one of dependent log events or cluster independent log events (step1000). The process terminates thereafter.

Turning next toFIG.11, a flowchart of a process for generating an event association model to cluster log events is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.11is an example of one implementation for step604inFIG.6.

The process trains the event association model using a training dataset comprising clusters of the clustered training log events with the dependence and the independence of the clustered training log events as labels to the clusters (step1100). The process terminates thereafter.

With reference toFIG.12, a flowchart of a process for generating an event association model to cluster log events is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.12is an example of one implementation for step604inFIG.6.

The process trains the event association model using a training dataset comprising clusters of the clustered training log events with the dependence of the clustered training log events as labels to the clusters and with a relevancy of clustered training log events to entities of interest (step1200). The process terminates thereafter.

Turning next toFIG.13, a flowchart of a process for clustering log events using an event association model is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.13is an example of one implementation for step606inFIG.6.

The process clusters the log events using the event association model that clusters the log events based on dependence and independence into a hierarchical structure of dependence log events and entity relevance for the log events (step1300). The process terminates thereafter.

Turning now toFIG.14, a block diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system1400can be used to implement computers and computing devices in computing environment100inFIG.1. Data processing system1400can be used to implement computer system206inFIG.2. In this illustrative example, data processing system1400includes communications framework1402, which provides communications between processor unit1404, memory1406, persistent storage1408, communications unit1410, input/output (I/O) unit1412, and display1414. In this example, communications framework1402takes the form of a bus system.

Processor unit1404serves to execute instructions for software that can be loaded into memory1406. Processor unit1404includes one or more processors. For example, processor unit1404can be selected from at least one of a multicore processor, a central processing unit (CPU), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a network processor, or some other suitable type of processor. Further, processor unit1404can may be implemented using one or more heterogeneous processor systems in which a main processor is present with secondary processors on a single chip. As another illustrative example, processor unit1404can be a symmetric multi-processor system containing multiple processors of the same type on a single chip.

Memory1406and persistent storage1408are examples of storage devices1416. A storage device is any piece of hardware that is capable of storing information, such as, for example, without limitation, at least one of data, program instructions in functional form, or other suitable information either on a temporary basis, a permanent basis, or both on a temporary basis and a permanent basis. Storage devices1416may also be referred to as computer-readable storage devices in these illustrative examples. Memory1406, in these examples, can be, for example, a random-access memory or any other suitable volatile or non-volatile storage device. Persistent storage1408may take various forms, depending on the particular implementation.

For example, persistent storage1408may contain one or more components or devices. For example, persistent storage1408can be a hard drive, a solid-state drive (SSD), a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination of the above. The media used by persistent storage1408also can be removable. For example, a removable hard drive can be used for persistent storage1408.

Communications unit1410, in these illustrative examples, provides for communications with other data processing systems or devices. In these illustrative examples, communications unit1410is a network interface card.

Input/output unit1412allows for input and output of data with other devices that can be connected to data processing system1400. For example, input/output unit1412may provide a connection for user input through at least one of a keyboard, a mouse, or some other suitable input device. Further, input/output unit1412may send output to a printer. Display1414provides a mechanism to display information to a user.

Instructions for at least one of the operating system, applications, or programs can be located in storage devices1416, which are in communication with processor unit1404through communications framework1402. The processes of the different embodiments can be performed by processor unit1404using computer-implemented instructions, which may be located in a memory, such as memory1406.

These instructions are referred to as program instructions, computer usable program instructions, or computer-readable program instructions that can be read and executed by a processor in processor unit1404. The program instructions in the different embodiments can be embodied on different physical or computer-readable storage media, such as memory1406or persistent storage1408.

Program instructions1418is located in a functional form on computer-readable media1420that is selectively removable and can be loaded onto or transferred to data processing system1400for execution by processor unit1404. Program instructions1418and computer-readable media1420form computer program product1422in these illustrative examples. In the illustrative example, computer-readable media1420is computer-readable storage media1424.

Computer-readable storage media1424is a physical or tangible storage device used to store program instructions1418rather than a medium that propagates or transmits program instructions1418. Computer-readable storage media1424, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Alternatively, program instructions1418can be transferred to data processing system1400using a computer-readable signal media. The computer-readable signal media are signals and can be, for example, a propagated data signal containing program instructions1418. For example, the computer-readable signal media can be at least one of an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals can be transmitted over connections, such as wireless connections, optical fiber cable, coaxial cable, a wire, or any other suitable type of connection.

Further, as used herein, “computer-readable media1420” can be singular or plural. For example, program instructions1418can be located in computer-readable media1420in the form of a single storage device or system. In another example, program instructions1418can be located in computer-readable media1420that is distributed in multiple data processing systems. In other words, some instructions in program instructions1418can be located in one data processing system while other instructions in program instructions1418can be located in one data processing system. For example, a portion of program instructions1418can be located in computer-readable media1420in a server computer while another portion of program instructions1418can be located in computer-readable media1420located in a set of client computers.

The different components illustrated for data processing system1400are not meant to provide architectural limitations to the manner in which different embodiments can be implemented. In some illustrative examples, one or more of the components may be incorporated in or otherwise form a portion of, another component. For example, memory1406, or portions thereof, may be incorporated in processor unit1404in some illustrative examples. The different illustrative embodiments can be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system1400. Other components shown inFIG.14can be varied from the illustrative examples shown. The different embodiments can be implemented using any hardware device or system capable of running program instructions1418.

Thus, illustrative embodiments of the present invention provide a computer implemented method, computer system, and computer program product for processing log events. In one illustrative example, a computer system determines a dependence and an independence of training log events such that the training log events have a determined dependance and independence. The computer system clusters the training log events with the determined dependence and independence to form clustered training log events. The computer system generates an event association model using the clustered training log events with the determined dependence and independence. The computer system clusters the log events using the event association model that clusters the log events based on the dependence and the independence of the log events.

Thus, an event processor in the illustrative examples can process log events with greater accuracy as compared to current techniques that are rule-based. In the illustrative example, the event processor can determine whether an event is dependent on one or more other events or is independent. Further, the event processor can cluster events based on the assessment of dependency or independence made for the log events. The clustering can also be based on the similarity of events to each other.