Semantic deduplication of event logs

Operations include: determining that the first event record does not meet similarity criteria in relation to any of a plurality of representative records in an event log; adding a new representative record, to the plurality of representative records, that is based on the first event record; determining that the second event record meets the similarity criteria in relation to a first representative record of the plurality of representative records; incrementing the numerical value, associated with the first representative record in the event log, that indicates the number of event records that are represented by the first representative record; generating log data that (a) comprises the incremented numerical value associated with the first representative record and (b) does not include the second event record; and updating the event log based on the log data to generate an updated event log that does not include the second event record.

TECHNICAL FIELD

The present disclosure relates to a semantic deduplication process.

BACKGROUND

One of the common problems of event logging is the excessively large size of logs. As an example, security assurance and compliance audit logs may be excessively large, rendering the logging cost-prohibitive to store or transmit all logged events. Some existing solutions reduce log volumes in real-time without losing the essence of the information. These solutions typically relate to data cleaning or storage optimization categories.

Certain existing methodologies evaluate multiple records sourced from different places into one through merging and completing missing data. This work is generally done offline in a batch process to remove any spelling errors, grammatical mistakes, missing values, changes of location or similar kind of data issues, thereby performing data cleaning.

Another industry practice relates to storage optimization, where the records are stored completely and there are schemes to identify the differences and only keep/store the differences. This process is also mostly done as a batch process and intended to be able to reconstruct the full original data records similar to decompressing the data with zero data loss.

Conventional technology often implements lossless logs, retaining all data and/or compressing in a manner that allows for recovery of all data. Furthermore, existing systems log the same event multiple times with merely different permutations of it. The action taken is often the same for all permutations of the same type of a problem. These types of techniques can be wasteful because they frequently create redundancy in event logging and data processing.

DETAILED DESCRIPTION

1. General Overview

One or more embodiments utilize a representative event record associated with a corresponding numerical value, i.e., count that indicates a number of event records being represented by the representative event record. The system receives an event stream including a set of event records. If a received event record (i.e., target event record) meets a similarity criteria in relation to a particular representative event record of a set of (existing) representative event records, the system increments the numerical value associated with the particular representative event record. The target event record is not itself entered into an event log including the set of representative event records. If the target event record does not meet the similarity criteria in relation to any of the set of (existing) representative event records, the system adds the target event record to the set of representative event records. Each of a set of host devices may generate an event log of representative event records based on respective detected event records, as described above. The host devices may transmit the respective event logs to a central logging service.

One or more embodiments determine that a target event record is similar to an existing representative event record using a two-step analysis. In the first step of the two-step analysis, the system categorizes the target event record, based on a first set of attributes associated with the target event record, into a particular category. In the second step of the two-step analysis, the system computes a semantic match score (e.g., cosine similarity) between the target event record and the representative event record(s) corresponding to the particular category. The semantic match score may be computed based on a second set of attributes associated with the received event record. If semantic match score between the target event record and one of the existing representative event records meets a threshold value, then the target event record is determined to be similar to the representative event record.

One or more embodiments determine that a target event record is not similar to any existing representative event record. The system may determine that the target event record is not similar to any existing representative event record if no representative event records are categorized into a same category as the target event record. The system may determine that the target event record is not similar to any existing representative event record if a semantic match score based on the target event record and existing representative event record(s) do not meet a threshold value.

2. Cloud Computing Technology

Infrastructure as a Service (IaaS) is an application of cloud computing technology. IaaS can be configured to provide virtualized computing resources over a public network (e.g., the Internet). In an IaaS model, a cloud computing provider can host the infrastructure components (e.g., servers, storage devices, network nodes (e.g., hardware), deployment software, platform virtualization (e.g., a hypervisor layer), or the like). In some cases, an IaaS provider may also supply a variety of services to accompany those infrastructure components (example services include billing software, monitoring software, logging software, load balancing software, clustering software, etc.). Thus, as these services may be policy-driven, IaaS users may be able to implement policies to drive load balancing to maintain application availability and performance.

In some cases, a cloud computing model will involve the participation of a cloud provider. The cloud provider may, but need not be, a third-party service that specializes in providing (e.g., offering, renting, selling) IaaS. An entity may also opt to deploy a private cloud, becoming its own provider of infrastructure services.

In some examples, IaaS deployment is the process of implementing a new application, or a new version of an application, onto a prepared application server or other similar device. IaaS deployment may also include the process of preparing the server (e.g., installing libraries, daemons, etc.). The deployment process is often managed by the cloud provider, below the hypervisor layer (e.g., the servers, storage, network hardware, and virtualization). Thus, the customer may be responsible for handling (OS), middleware, and/or application deployment (e.g., on self-service virtual machines (e.g., that can be spun up on demand) or the like.

In some cases, there are challenges for IaaS provisioning. There is an initial challenge of provisioning the initial set of infrastructure. There is an additional challenge of evolving the existing infrastructure (e.g., adding new services, changing services, removing services, etc.) after the initial provisioning is completed. In some cases, these challenges may be addressed by enabling the configuration of the infrastructure to be defined declaratively. In other words, the infrastructure (e.g., what components are needed and how they interact) can be defined by one or more configuration files. Thus, the overall topology of the infrastructure (e.g., what resources depend on which, and how they each work together) can be described declaratively. In some instances, once the topology is defined, a workflow can be generated that creates and/or manages the different components described in the configuration files.

In some instances, continuous deployment techniques may be employed to enable deployment of infrastructure code across various virtual computing environments. Additionally, the described techniques can enable infrastructure management within these environments. In some examples, service teams can write code that is desired to be deployed to one or more, but often many, different production environments (e.g., across various different geographic locations, sometimes spanning the entire world). In some embodiments, infrastructure and resources may be provisioned (manually, and/or using a provisioning tool) prior to deployment of code to be executed on the infrastructure. However, in some examples, the infrastructure on which the code will be deployed must first be set up. In some instances, the provisioning can be done manually, a provisioning tool may be utilized to provision the resources, and/or deployment tools may be utilized to deploy the code once the infrastructure is provisioned.

The VCN106can include a local peering gateway (LPG)110that can be communicatively coupled to a secure shell (SSH) VCN112via an LPG110contained in the SSH VCN112. The SSH VCN112can include an SSH subnet114, and the SSH VCN112can be communicatively coupled to a control plane VCN116via the LPG110contained in the control plane VCN116. Also, the SSH VCN112can be communicatively coupled to a data plane VCN118via an LPG110. The control plane VCN116and the data plane VCN118can be contained in a service tenancy119that can be owned and/or operated by the IaaS provider.

The control plane VCN116can include a control plane demilitarized zone (DMZ) tier120that acts as a perimeter network (e.g., portions of a corporate network between the corporate intranet and external networks). The DMZ-based servers may have restricted responsibilities and help keep breaches contained. Additionally, the DMZ tier120can include one or more load balancer (LB) subnet(s)122, a control plane app tier124that can include app subnet(s)126, a control plane data tier128that can include database (DB) subnet(s)130(e.g., frontend DB subnet(s) and/or backend DB subnet(s)). The LB subnet(s)122contained in the control plane DMZ tier120can be communicatively coupled to the app subnet(s)126contained in the control plane app tier124and an Internet gateway134that can be contained in the control plane VCN116, and the app subnet(s)126can be communicatively coupled to the DB subnet(s)130contained in the control plane data tier128and a service gateway136and a network address translation (NAT) gateway138. The control plane VCN116can include the service gateway136and the NAT gateway138.

The control plane VCN116can include a data plane mirror app tier140that can include app subnet(s)126. The app subnet(s)126contained in the data plane mirror app tier140can include a virtual network interface controller (VNIC)142that can execute a compute instance144. The compute instance144can communicatively couple the app subnet(s)126of the data plane mirror app tier140to app subnet(s)126that can be contained in a data plane app tier146.

The data plane VCN118can include the data plane app tier146, a data plane DMZ tier148, and a data plane data tier150. The data plane DMZ tier148can include LB subnet(s)122that can be communicatively coupled to the app subnet(s)126of the data plane app tier146and the Internet gateway134of the data plane VCN118. The app subnet(s)126can be communicatively coupled to the service gateway136of the data plane VCN118and the NAT gateway138of the data plane VCN118. The data plane data tier150can also include the DB subnet(s)130that can be communicatively coupled to the app subnet(s)126of the data plane app tier146.

The Internet gateway134of the control plane VCN116and of the data plane VCN118can be communicatively coupled to a metadata management service152that can be communicatively coupled to public Internet154. Public Internet154can be communicatively coupled to the NAT gateway138of the control plane VCN116and of the data plane VCN118. The service gateway136of the control plane VCN116and of the data plane VCN118can be communicatively couple to cloud services156.

In some examples, the service gateway136of the control plane VCN116or of the data plane VCN118can make application programming interface (API) calls to cloud services156without going through public Internet154. The API calls to cloud services156from the service gateway136can be one-way: the service gateway136can make API calls to cloud services156, and cloud services156can send requested data to the service gateway136. But, cloud services156may not initiate API calls to the service gateway136.

In some examples, the secure host tenancy104can be directly connected to the service tenancy119, which may be otherwise isolated. The secure host subnet108can communicate with the SSH subnet114through an LPG110that may enable two-way communication over an otherwise isolated system. Connecting the secure host subnet108to the SSH subnet114may give the secure host subnet108access to other entities within the service tenancy119.

The control plane VCN116may allow users of the service tenancy119to set up or otherwise provision desired resources. Desired resources provisioned in the control plane VCN116may be deployed or otherwise used in the data plane VCN118. In some examples, the control plane VCN116can be isolated from the data plane VCN118, and the data plane mirror app tier140of the control plane VCN116can communicate with the data plane app tier146of the data plane VCN118via VNICs142that can be contained in the data plane mirror app tier140and the data plane app tier146.

In some examples, users of the system, or customers, can make requests, for example create, read, update, or delete (CRUD) operations, through public Internet154that can communicate the requests to the metadata management service152. The metadata management service152can communicate the request to the control plane VCN116through the Internet gateway134. The request can be received by the LB subnet(s)122contained in the control plane DMZ tier120. The LB subnet(s)122may determine that the request is valid, and in response to this determination, the LB subnet(s)122can transmit the request to app subnet(s)126contained in the control plane app tier124. If the request is validated and requires a call to public Internet154, the call to public Internet154may be transmitted to the NAT gateway138that can make the call to public Internet154. Metadata that may be desired to be stored by the request can be stored in the DB subnet(s)130.

In some examples, the data plane mirror app tier140can facilitate direct communication between the control plane VCN116and the data plane VCN118. For example, changes, updates, or other suitable modifications to configuration may be desired to be applied to the resources contained in the data plane VCN118. Via a VNIC142, the control plane VCN116can directly communicate with, and can thereby execute the changes, updates, or other suitable modifications to configuration to, resources contained in the data plane VCN118.

In some embodiments, the control plane VCN116and the data plane VCN118can be contained in the service tenancy119. In this case, the user, or the customer, of the system may not own or operate either the control plane VCN116or the data plane VCN118. Instead, the IaaS provider may own or operate the control plane VCN116and the data plane VCN118, both of which may be contained in the service tenancy119. This embodiment can enable isolation of networks that may prevent users or customers from interacting with other users', or other customers', resources. Also, this embodiment may allow users or customers of the system to store databases privately without needing to rely on public Internet154, which may not have a desired level of threat prevention, for storage.

In other embodiments, the LB subnet(s)122contained in the control plane VCN116can be configured to receive a signal from the service gateway136. In this embodiment, the control plane VCN116and the data plane VCN118may be configured to be called by a customer of the IaaS provider without calling public Internet154. Customers of the Iaas provider may desire this embodiment since database(s) that the customers use may be controlled by the IaaS provider and may be stored on the service tenancy119, which may be isolated from public Internet154.

FIG.2is a block diagram200illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators202(e.g., service operators102ofFIG.1) can be communicatively coupled to a secure host tenancy204(e.g., the secure host tenancy104ofFIG.1) that can include a virtual cloud network (VCN)206(e.g., the VCN106ofFIG.1) and a secure host subnet208(e.g., the secure host subnet108ofFIG.1). The VCN206can include a local peering gateway (LPG)210(e.g., the LPG110ofFIG.1) that can be communicatively coupled to a secure shell (SSH) VCN212(e.g., the SSH VCN112ofFIG.1) via an LPG110contained in the SSH VCN212. The SSH VCN212can include an SSH subnet214(e.g., the SSH subnet114ofFIG.1), and the SSH VCN212can be communicatively coupled to a control plane VCN216(e.g., the control plane VCN116ofFIG.1) via an LPG210contained in the control plane VCN216. The control plane VCN216can be contained in a service tenancy219(e.g., the service tenancy119ofFIG.1), and the data plane VCN218(e.g., the data plane VCN118ofFIG.1) can be contained in a customer tenancy221that may be owned or operated by users, or customers, of the system.

The control plane VCN216can include a control plane DMZ tier220(e.g., the control plane DMZ tier120ofFIG.1) that can include LB subnet(s)222(e.g., LB subnet(s)122ofFIG.1), a control plane app tier224(e.g., the control plane app tier124ofFIG.1) that can include app subnet(s)226(e.g., app subnet(s)126ofFIG.1), a control plane data tier228(e.g., the control plane data tier128ofFIG.1) that can include database (DB) subnet(s)230(e.g., similar to DB subnet(s)130ofFIG.1). The LB subnet(s)222contained in the control plane DMZ tier220can be communicatively coupled to the app subnet(s)226contained in the control plane app tier224and an Internet gateway234(e.g., the Internet gateway134ofFIG.1) that can be contained in the control plane VCN216, and the app subnet(s)226can be communicatively coupled to the DB subnet(s)230contained in the control plane data tier228and a service gateway236(e.g., the service gateway136ofFIG.1) and a network address translation (NAT) gateway238(e.g., the NAT gateway138ofFIG.1). The control plane VCN216can include the service gateway236and the NAT gateway238.

The control plane VCN216can include a data plane mirror app tier240(e.g., the data plane mirror app tier140ofFIG.1) that can include app subnet(s)226. The app subnet(s)226contained in the data plane mirror app tier240can include a virtual network interface controller (VNIC)242(e.g., the VNIC of142) that can execute a compute instance244(e.g., similar to the compute instance144ofFIG.1). The compute instance244can facilitate communication between the app subnet(s)226of the data plane mirror app tier240and the app subnet(s)226that can be contained in a data plane app tier246(e.g., the data plane app tier146ofFIG.1) via the VNIC242contained in the data plane mirror app tier240and the VNIC242contained in the data plane app tier246.

The Internet gateway234contained in the control plane VCN216can be communicatively coupled to a metadata management service252(e.g., the metadata management service152ofFIG.1) that can be communicatively coupled to public Internet254(e.g., public Internet154ofFIG.1). Public Internet254can be communicatively coupled to the NAT gateway238contained in the control plane VCN216. The service gateway236contained in the control plane VCN216can be communicatively couple to cloud services256(e.g., cloud services156ofFIG.1).

In some examples, the data plane VCN218can be contained in the customer tenancy221. In this case, the IaaS provider may provide the control plane VCN216for each customer, and the IaaS provider may, for each customer, set up a unique compute instance244that is contained in the service tenancy219. Each compute instance244may allow communication between the control plane VCN216, contained in the service tenancy219, and the data plane VCN218that is contained in the customer tenancy221. The compute instance244may allow resources, that are provisioned in the control plane VCN216that is contained in the service tenancy219, to be deployed or otherwise used in the data plane VCN218that is contained in the customer tenancy221.

In other examples, the customer of the IaaS provider may have databases that live in the customer tenancy221. In this example, the control plane VCN216can include the data plane mirror app tier240that can include app subnet(s)226. The data plane mirror app tier240can reside in the data plane VCN218, but the data plane mirror app tier240may not live in the data plane VCN218. That is, the data plane mirror app tier240may have access to the customer tenancy221, but the data plane mirror app tier240may not exist in the data plane VCN218or be owned or operated by the customer of the IaaS provider. The data plane mirror app tier240may be configured to make calls to the data plane VCN218but may not be configured to make calls to any entity contained in the control plane VCN216. The customer may desire to deploy or otherwise use resources in the data plane VCN218that are provisioned in the control plane VCN216, and the data plane mirror app tier240can facilitate the desired deployment, or other usage of resources, of the customer.

In some embodiments, the customer of the IaaS provider can apply filters to the data plane VCN218. In this embodiment, the customer can determine what the data plane VCN218can access, and the customer may restrict access to public Internet254from the data plane VCN218. The IaaS provider may not be able to apply filters or otherwise control access of the data plane VCN218to any outside networks or databases. Applying filters and controls by the customer onto the data plane VCN218, contained in the customer tenancy221, can help isolate the data plane VCN218from other customers and from public Internet254.

In some embodiments, cloud services256can be called by the service gateway236to access services that may not exist on public Internet254, on the control plane VCN216, or on the data plane VCN218. The connection between cloud services256and the control plane VCN216or the data plane VCN218may not be live or continuous. Cloud services256may exist on a different network owned or operated by the IaaS provider. Cloud services256may be configured to receive calls from the service gateway236and may be configured to not receive calls from public Internet254. Some cloud services256may be isolated from other cloud services256, and the control plane VCN216may be isolated from cloud services256that may not be in the same region as the control plane VCN216. For example, the control plane VCN216may be located in “Region 1,” and cloud service “Deployment 1,” may be located in Region 1 and in “Region 2.” If a call to Deployment 1 is made by the service gateway236contained in the control plane VCN216located in Region 1, the call may be transmitted to Deployment 1 in Region 1. In this example, the control plane VCN216, or Deployment 1 in Region 1, may not be communicatively coupled to, or otherwise in communication with, Deployment 1 in Region 2.

FIG.3is a block diagram300illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators302(e.g., service operators102ofFIG.1) can be communicatively coupled to a secure host tenancy304(e.g., the secure host tenancy104ofFIG.1) that can include a virtual cloud network (VCN)306(e.g., the VCN106ofFIG.1) and a secure host subnet308(e.g., the secure host subnet108ofFIG.1). The VCN306can include an LPG310(e.g., the LPG110ofFIG.1) that can be communicatively coupled to an SSH VCN312(e.g., the SSH VCN112ofFIG.1) via an LPG310contained in the SSH VCN312. The SSH VCN312can include an SSH subnet314(e.g., the SSH subnet114ofFIG.1), and the SSH VCN312can be communicatively coupled to a control plane VCN316(e.g., the control plane VCN116ofFIG.1) via an LPG310contained in the control plane VCN316and to a data plane VCN318(e.g., the data plane118ofFIG.1) via an LPG310contained in the data plane VCN318. The control plane VCN316and the data plane VCN318can be contained in a service tenancy319(e.g., the service tenancy119ofFIG.1).

The control plane VCN316can include a control plane DMZ tier320(e.g., the control plane DMZ tier120ofFIG.1) that can include load balancer (LB) subnet(s)322(e.g., LB subnet(s)122ofFIG.1), a control plane app tier324(e.g., the control plane app tier124ofFIG.1) that can include app subnet(s)326(e.g., similar to app subnet(s)126ofFIG.1), a control plane data tier328(e.g., the control plane data tier128ofFIG.1) that can include DB subnet(s)330. The LB subnet(s)322contained in the control plane DMZ tier320can be communicatively coupled to the app subnet(s)326contained in the control plane app tier324and to an Internet gateway334(e.g., the Internet gateway134ofFIG.1) that can be contained in the control plane VCN316, and the app subnet(s)326can be communicatively coupled to the DB subnet(s)330contained in the control plane data tier328and to a service gateway336(e.g., the service gateway ofFIG.1) and a network address translation (NAT) gateway338(e.g., the NAT gateway138ofFIG.1). The control plane VCN316can include the service gateway336and the NAT gateway338.

The data plane VCN318can include a data plane app tier346(e.g., the data plane app tier146ofFIG.1), a data plane DMZ tier348(e.g., the data plane DMZ tier148ofFIG.1), and a data plane data tier350(e.g., the data plane data tier150ofFIG.1). The data plane DMZ tier348can include LB subnet(s)322that can be communicatively coupled to trusted app subnet(s)360and untrusted app subnet(s)362of the data plane app tier346and the Internet gateway334contained in the data plane VCN318. The trusted app subnet(s)360can be communicatively coupled to the service gateway336contained in the data plane VCN318, the NAT gateway338contained in the data plane VCN318, and DB subnet(s)330contained in the data plane data tier350. The untrusted app subnet(s)362can be communicatively coupled to the service gateway336contained in the data plane VCN318and DB subnet(s)330contained in the data plane data tier350. The data plane data tier350can include DB subnet(s)330that can be communicatively coupled to the service gateway336contained in the data plane VCN318.

The untrusted app subnet(s)362can include one or more primary VNICs364(1)-(N) that can be communicatively coupled to tenant virtual machines (VMs)366(1)-(N). Each tenant VM366(1)-(N) can be communicatively coupled to a respective app subnet367(1)-(N) that can be contained in respective container egress VCNs368(1)-(N) that can be contained in respective customer tenancies370(1)-(N). Respective secondary VNICs372(1)-(N) can facilitate communication between the untrusted app subnet(s)362contained in the data plane VCN318and the app subnet contained in the container egress VCNs368(1)-(N). Each container egress VCNs368(1)-(N) can include a NAT gateway338that can be communicatively coupled to public Internet354(e.g., public Internet154ofFIG.1).

The Internet gateway334contained in the control plane VCN316and contained in the data plane VCN318can be communicatively coupled to a metadata management service352(e.g., the metadata management system152ofFIG.1) that can be communicatively coupled to public Internet354. Public Internet354can be communicatively coupled to the NAT gateway338contained in the control plane VCN316and contained in the data plane VCN318. The service gateway336contained in the control plane VCN316and contained in the data plane VCN318can be communicatively couple to cloud services356.

In some embodiments, the data plane VCN318can be integrated with customer tenancies370. This integration can be useful or desirable for customers of the IaaS provider in some cases such as a case that may desire support when executing code. The customer may provide code to run that may be destructive, may communicate with other customer resources, or may otherwise cause undesirable effects. In response to this, the IaaS provider may determine whether to run code given to the IaaS provider by the customer.

In some examples, the customer of the IaaS provider may grant temporary network access to the IaaS provider and request a function to be attached to the data plane app tier346. Code to run the function may be executed in the VMs366(1)-(N), and the code may not be configured to run anywhere else on the data plane VCN318. Each VM366(1)-(N) may be connected to one customer tenancy370. Respective containers371(1)-(N) contained in the VMs366(1)-(N) may be configured to run the code. In this case, there can be a dual isolation (e.g., the containers371(1)-(N) running code, where the containers371(1)-(N) may be contained in at least the VM366(1)-(N) that are contained in the untrusted app subnet(s)362), which may help prevent incorrect or otherwise undesirable code from damaging the network of the IaaS provider or from damaging a network of a different customer. The containers371(1)-(N) may be communicatively coupled to the customer tenancy370and may be configured to transmit or receive data from the customer tenancy370. The containers371(1)-(N) may not be configured to transmit or receive data from any other entity in the data plane VCN318. Upon completion of running the code, the IaaS provider may kill or otherwise dispose of the containers371(1)-(N).

In some embodiments, the trusted app subnet(s)360may run code that may be owned or operated by the IaaS provider. In this embodiment, the trusted app subnet(s)360may be communicatively coupled to the DB subnet(s)330and be configured to execute CRUD operations in the DB subnet(s)330. The untrusted app subnet(s)362may be communicatively coupled to the DB subnet(s)330, but in this embodiment, the untrusted app subnet(s) may be configured to execute read operations in the DB subnet(s)330. The containers371(1)-(N) that can be contained in the VM366(1)-(N) of each customer and that may run code from the customer may not be communicatively coupled with the DB subnet(s)330.

In other embodiments, the control plane VCN316and the data plane VCN318may not be directly communicatively coupled. In this embodiment, there may be no direct communication between the control plane VCN316and the data plane VCN318. However, communication can occur indirectly through at least one method. An LPG310may be established by the IaaS provider that can facilitate communication between the control plane VCN316and the data plane VCN318. In another example, the control plane VCN316or the data plane VCN318can make a call to cloud services356via the service gateway336. For example, a call to cloud services356from the control plane VCN316can include a request for a service that can communicate with the data plane VCN318.

FIG.4is a block diagram400illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators402(e.g., service operators102ofFIG.1) can be communicatively coupled to a secure host tenancy404(e.g., the secure host tenancy104ofFIG.1) that can include a virtual cloud network (VCN)406(e.g., the VCN106ofFIG.1) and a secure host subnet408(e.g., the secure host subnet108ofFIG.1). The VCN406can include an LPG410(e.g., the LPG110ofFIG.1) that can be communicatively coupled to an SSH VCN412(e.g., the SSH VCN112ofFIG.1) via an LPG410contained in the SSH VCN412. The SSH VCN412can include an SSH subnet414(e.g., the SSH subnet114ofFIG.1), and the SSH VCN412can be communicatively coupled to a control plane VCN416(e.g., the control plane VCN116ofFIG.1) via an LPG410contained in the control plane VCN416and to a data plane VCN418(e.g., the data plane118ofFIG.1) via an LPG410contained in the data plane VCN418. The control plane VCN416and the data plane VCN418can be contained in a service tenancy419(e.g., the service tenancy119ofFIG.1).

The control plane VCN416can include a control plane DMZ tier420(e.g., the control plane DMZ tier120ofFIG.1) that can include LB subnet(s)422(e.g., LB subnet(s)122ofFIG.1), a control plane app tier424(e.g., the control plane app tier124ofFIG.1) that can include app subnet(s)426(e.g., app subnet(s)126ofFIG.1), a control plane data tier428(e.g., the control plane data tier128ofFIG.1) that can include DB subnet(s)430(e.g., DB subnet(s)330ofFIG.3). The LB subnet(s)422contained in the control plane DMZ tier420can be communicatively coupled to the app subnet(s)426contained in the control plane app tier424and to an Internet gateway434(e.g., the Internet gateway134ofFIG.1) that can be contained in the control plane VCN416, and the app subnet(s)426can be communicatively coupled to the DB subnet(s)430contained in the control plane data tier428and to a service gateway436(e.g., the service gateway ofFIG.1) and a network address translation (NAT) gateway438(e.g., the NAT gateway138ofFIG.1). The control plane VCN416can include the service gateway436and the NAT gateway438.

The data plane VCN418can include a data plane app tier446(e.g., the data plane app tier146ofFIG.1), a data plane DMZ tier448(e.g., the data plane DMZ tier148ofFIG.1), and a data plane data tier450(e.g., the data plane data tier150ofFIG.1). The data plane DMZ tier448can include LB subnet(s)422that can be communicatively coupled to trusted app subnet(s)460(e.g., trusted app subnet(s)360ofFIG.3) and untrusted app subnet(s)462(e.g., untrusted app subnet(s)362ofFIG.3) of the data plane app tier446and the Internet gateway434contained in the data plane VCN418. The trusted app subnet(s)460can be communicatively coupled to the service gateway436contained in the data plane VCN418, the NAT gateway438contained in the data plane VCN418, and DB subnet(s)430contained in the data plane data tier450. The untrusted app subnet(s)462can be communicatively coupled to the service gateway436contained in the data plane VCN418and DB subnet(s)430contained in the data plane data tier450. The data plane data tier450can include DB subnet(s)430that can be communicatively coupled to the service gateway436contained in the data plane VCN418.

The untrusted app subnet(s)462can include primary VNICs464(1)-(N) that can be communicatively coupled to tenant virtual machines (VMs)466(1)-(N) residing within the untrusted app subnet(s)462. Each tenant VM466(1)-(N) can run code in a respective container467(1)-(N), and be communicatively coupled to an app subnet426that can be contained in a data plane app tier446that can be contained in a container egress VCN468. Respective secondary VNICs472(1)-(N) can facilitate communication between the untrusted app subnet(s)462contained in the data plane VCN418and the app subnet contained in the container egress VCN468. The container egress VCN can include a NAT gateway438that can be communicatively coupled to public Internet454(e.g., public Internet154ofFIG.1).

The Internet gateway434contained in the control plane VCN416and contained in the data plane VCN418can be communicatively coupled to a metadata management service452(e.g., the metadata management system152ofFIG.1) that can be communicatively coupled to public Internet454. Public Internet454can be communicatively coupled to the NAT gateway438contained in the control plane VCN416and contained in the data plane VCN418. The service gateway436contained in the control plane VCN416and contained in the data plane VCN418can be communicatively couple to cloud services456.

In some examples, the pattern illustrated by the architecture of block diagram400ofFIG.4may be considered an exception to the pattern illustrated by the architecture of block diagram300ofFIG.3and may be desirable for a customer of the IaaS provider if the IaaS provider cannot directly communicate with the customer (e.g., a disconnected region). The respective containers467(1)-(N) that are contained in the VMs466(1)-(N) for each customer can be accessed in real-time by the customer. The containers467(1)-(N) may be configured to make calls to respective secondary VNICs472(1)-(N) contained in app subnet(s)426of the data plane app tier446that can be contained in the container egress VCN468. The secondary VNICS472(1)-(N) can transmit the calls to the NAT gateway438that may transmit the calls to public Internet454. In this example, the containers467(1)-(N) that can be accessed in real-time by the customer can be isolated from the control plane VCN416and can be isolated from other entities contained in the data plane VCN418. The containers467(1)-(N) may also be isolated from resources from other customers.

In other examples, the customer can use the containers467(1)-(N) to call cloud services456. In this example, the customer may run code in the containers467(1)-(N) that requests a service from cloud services456. The containers467(1)-(N) can transmit this request to the secondary VNICs472(1)-(N) that can transmit the request to the NAT gateway that can transmit the request to public Internet454. Public Internet454can transmit the request to LB subnet(s)422contained in the control plane VCN416via the Internet gateway434. In response to determining the request is valid, the LB subnet(s) can transmit the request to app subnet(s)426that can transmit the request to cloud services456via the service gateway436.

3. Computer System

FIG.5illustrates an example computer system500, in which various embodiments may be implemented. The system500may be used to implement any of the computer systems described above. As shown in the figure, computer system500includes a processing unit504that communicates with a number of peripheral subsystems via a bus subsystem502. These peripheral subsystems may include a processing acceleration unit506, an I/O subsystem508, a storage subsystem518and a communications subsystem524. Storage subsystem518includes tangible computer-readable storage media522and a system memory510.

Processing unit504, which can be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system500. One or more processors may be included in processing unit504. These processors may include single core or multicore processors. In certain embodiments, processing unit504may be implemented as one or more independent processing units532and/or534with single or multicore processors included in each processing unit. In other embodiments, processing unit504may also be implemented as a quad-core processing unit formed by integrating two dual-core processors into a single chip.

In various embodiments, processing unit504can execute a variety of programs in response to program code and can maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in processor(s)504and/or in storage subsystem518. Through suitable programming, processor(s)504can provide various functionalities described above. Computer system500may additionally include a processing acceleration unit506, which can include a digital signal processor (DSP), a special-purpose processor, and/or the like.

Computer system500may comprise a storage subsystem518that provides a tangible non-transitory computer-readable storage medium for storing software and data constructs that provide the functionality of the embodiments described in this disclosure. The software can include programs, code modules, instructions, scripts, etc., that when executed by one or more cores or processors of processing unit504provide the functionality described above. Storage subsystem518may also provide a repository for storing data used in accordance with the present disclosure.

As depicted in the example inFIG.5, storage subsystem518can include various components including a system memory510, computer-readable storage media522, and a computer readable storage media reader520. System memory510may store program instructions that are loadable and executable by processing unit504. System memory510may also store data that is used during the execution of the instructions and/or data that is generated during the execution of the program instructions. Various different kinds of programs may be loaded into system memory510including but not limited to client applications, Web browsers, mid-tier applications, relational database management systems (RDBMS), virtual machines, containers, etc.

System memory510may also store an operating system516. Examples of operating system516may include various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems, a variety of commercially-available UNIX® or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as iOS, Windows® Phone, Android® OS, BlackBerry® OS, and Palm® OS operating systems. In certain implementations where computer system500executes one or more virtual machines, the virtual machines along with their guest operating systems (GOSs) may be loaded into system memory510and executed by one or more processors or cores of processing unit504.

System memory510can come in different configurations depending upon the type of computer system500. For example, system memory510may be volatile memory (such as random access memory (RAM)) and/or non-volatile memory (such as read-only memory (ROM), flash memory, etc.) Different types of RAM configurations may be provided including a static random access memory (SRAM), a dynamic random access memory (DRAM), and others. In some implementations, system memory510may include a basic input/output system (BIOS) containing basic routines that help to transfer information between elements within computer system500, such as during start-up.

Computer-readable storage media522may represent remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing, storing, computer-readable information for use by computer system500including instructions executable by processing unit504of computer system500.

Computer-readable storage media522can include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information. This can include tangible computer-readable storage media such as RAM, ROM, electronically erasable programmable ROM (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible computer readable media.

Machine-readable instructions executable by one or more processors or cores of processing unit504may be stored on a non-transitory computer-readable storage medium. A non-transitory computer-readable storage medium can include physically tangible memory or storage devices that include volatile memory storage devices and/or non-volatile storage devices. Examples of non-transitory computer-readable storage medium include magnetic storage media (e.g., disk or tapes), optical storage media (e.g., DVDs, CDs), various types of RAM, ROM, or flash memory, hard drives, floppy drives, detachable memory drives (e.g., USB drives), or other type of storage device.

Communications subsystem524provides an interface to other computer systems and networks. Communications subsystem524serves as an interface for receiving data from and transmitting data to other systems from computer system500. For example, communications subsystem524may enable computer system500to connect to one or more devices via the Internet. In some embodiments communications subsystem524can include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi (IEEE 802.11 family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments communications subsystem524can provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface.

In some embodiments, communications subsystem524may also receive input communication in the form of structured and/or unstructured data feeds526, event streams528, event updates530, and the like on behalf of one or more users who may use computer system500.

By way of example, communications subsystem524may be configured to receive data feeds526in real-time from users of social networks and/or other communication services such as Twitter® feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources.

Communications subsystem524may also be configured to output the structured and/or unstructured data feeds526, event streams528, event updates530, and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system500.

One embodiment of the present application, shown inFIG.6A, is a semantic deduplication architecture600that includes logging service608, and one or more computing devices602(602ato602n). Each of the computing devices602may include an event log manager604and an event log606.

The logging service608may be a software component or a dedicated service that captures and records events and messages generated by various systems, applications, or devices. In one embodiment, the logging service608is used for software development, IT operations, and cybersecurity to gather and store log data for analysis, troubleshooting, and monitoring purposes.

One of the functions of the logging service608may be to collect and store log entries, which may be textual or numerical records containing information about specific events, actions, errors, or warnings. These log entries can include timestamps, log levels, error codes, user actions, system states, and other relevant details depending on the context and purpose of logging.

The logging service608may perform troubleshooting and debugging, where log data provides valuable insights into the behavior of software applications and systems. Developers and system administrators can analyze logs to identify and diagnose issues, trace the flow of execution, and fix problems more efficiently.

In one embodiment, the logging service608is used for performance monitoring. By monitoring and analyzing log data, organizations can gain visibility into the performance of their systems, identify bottlenecks or areas of improvement, and optimize resource allocation.

Further, the logging service608can be used for security assurance and compliance. Security Assurance may be a measure of confidence that the security features, practices, procedures, and architecture of the system600accurately mediate and enforce the security policy. The security assurance process assists human cognitive ability to follow a pattern of concern across a string of sliding time windows to aid in troubleshooting and investigations. Having high volume of records in the event logs606to go through can present troubleshooting challenges and result in additional steps to filter out unwanted records manually during a troubleshooting process.

Certain embodiments of the present application identify one representative record and a count of occurrences of the underlying issue to reason about an event or condition during troubleshooting sessions and the security event. Accordingly, the central logging service608may determine a measure of confidence based on the representative log records and associated counts. In one embodiment, the central logging service608displays the representation log records and associated counts to a human being, who analyzes for security assurance.

Event logs606can be used for detecting and investigating security incidents. By analyzing the event logs606, security teams can identify potential threats, track malicious activities, and ensure compliance with regulatory requirements by maintaining an audit trail of system events.

In another embodiment, the logging service608is used for auditing and accountability. The logging service608assists in establishing accountability by recording user actions and system activities, which may be applicable to environments where multiple users or processes interact with critical systems.

In yet another embodiment, the logging service608provides analytics and insights. For example, aggregated log data can be analyzed using various techniques, such as data mining or machine learning, to extract meaningful patterns, trends, and insights, which can be useful for capacity planning, anomaly detection, and improving overall system performance. The logging service608may be accompanied by tools for log aggregation, search, and visualization, allowing users to efficiently manage and search through large volumes of log data.

The logging service608may be connected to any number of sources of event logs via links609. As an example, the logging service608may be connected to computing devices (e.g., computing devices602a-602n). The logging service608may control or manage components (e.g., event log managers604a-604n) executing on the computing devices. The logging service608may execute pull operations to pull event logs. Alternatively or additionally, the logging service608may receive event logs via push operations executed by components executing on the computing devices.

In some embodiments, the links609may be intra-network links when the computing devices602a-602nare within a same network as the logging service608. The links609may be inter-network links when the computing devices602a-602nare in a different network than the logging service608. The links609may, in some implementations, be limited with regard to data transmission speed or overall data throughput.

Furthermore, the links609may be congested or overloaded with data transmissions between the computing devices602a-602nand the logging service609. Certain embodiment reduces the amount of data that has to be transmitted over the links609. Specifically, embodiments transmit representative event records instead of transmitting all event records over the links609from the computing devices602a-602nto the logging service608.

Event logs606may be records of events, activities, and errors that occur within a computer system, and they may be stored in a structured or unstructured format for later review and analysis. In one embodiment, the event log manager604provides a user interface or command-line interface (CLI) that allows users, such as system administrators or IT professionals, to view, filter, search, clear logs606, analyze events and/or configure event logging.

Event logs606may include application logs, security logs, system logs, and custom logs generated by installed software. Users can apply filters and search criteria to quickly find specific events of interest. Filtering may be particularly useful when troubleshooting issues or investigating security incidents. In one embodiment, the event log manager604provides detailed information about each event, including timestamps, event IDs, severity levels, source, and description. Analyzing the relevant data helps in understanding system behavior and diagnosing problems.

The event logs606can grow in size over time, consuming disk space. The event log manager604allows users to clear logs, either manually or through automated scheduling, to free up space and keep the logs manageable. Users can configure the behavior of event logging, such as specifying which types of events to log, setting log size limits, and enabling or disabling specific event categories. In some cases, especially in large-scale environments, event log forwarding allows centralizing event logs606from multiple systems to the central logging service608for centralized monitoring and analysis.

In an embodiment, the event log manager604, may be a software tool or a component of an application or an operating system that enables users to view, analyze, and manage event logs606generated by the system or applications. The event log manager604allows users to view the contents of various event logs606stored on the system.

The event log managers604may be used as tools for system administrators and IT professionals to maintain system health, troubleshoot issues, monitor security events, and gather valuable insights about the overall system performance. Different operating systems may have their own built-in event log managers604. Further as illustrated inFIG.6B, the event log managers604may include a categorizing engine610, similarity criteria612, a feature vector generator614and a comparison engine616.

The categorizing engine610may be a software component or system that classifies or categorizes data, information, or objects into predefined categories or classes based on certain criteria or characteristics. In one embodiment, the categorizing engine610is a type of machine learning or artificial intelligence technology that uses algorithms to assign items to specific categories without human intervention.

One of the purposes of the categorizing engine610is to organize and manage large volumes of unstructured or semi-structured data by assigning them to appropriate categories. The categorizing engine610can be used for content organization, customer support, email filtering, sentiment analysis, e-commerce product categorization, fraud detection, etc.

The categorization engines610may use various machine learning techniques, such as supervised learning, unsupervised learning, or deep learning, to learn from labeled training data and then apply that knowledge to categorize new, unseen data. The quality of the categorization may depend on the accuracy of the underlying models and the richness and quality of the training data.

The event log manager604may further encompass the similarity criteria612, which may be quantitative methods used to determine the similarity or dissimilarity between two objects, data points, or entities. The similarity criteria612may be employed in data analysis, machine learning, information retrieval, pattern recognition, recommendation systems, to name a few.

In one embodiment, the similarity criteria612compare the characteristics or features of two entities such as log entries, for example, and compute a numerical value that represents their similarity. In another embodiment, the higher the value, the more similar the entities are, and vice versa. There may be different types of similarity criteria612, depending on the nature of the data and the specific application.

One embodiment of the present application uses cosine similarity for the similarity criteria612. Cosine similarity may be used in text mining and natural language processing, where cosine similarity may measure the cosine of the angle between two non-zero vectors. One of the applications of the cosine similarity is using the comparison engine616to compare documents or texts represented as vectors of word frequencies created by feature vector generator614.

In one embodiment, the feature vector generator614is a component or process that transforms raw data or input of an event record into a structured and numerical representation known as a feature vector. The feature vector generator614may, for example, generate a feature vector from a subset or set of attributes associated with the event record. The event records may be created with their vector representations and may be used for numerical comparisons with other event records, i.e., with other feature vectors associated with the event records.

A feature vector may be a list of numeric values that represent the relevant characteristics or features of an object, sample, or data point. The feature vectors may be used as inputs to various machine learning algorithms, statistical models, or data analysis techniques.

The feature vector generator614may create a feature vector by converting the raw data or input into a format that can be easily understood and processed by machine learning algorithms. The choice of features in the vector may determine the information that the model will use to make predictions or perform analysis. The process typically involves data preprocessing, feature extraction, feature encoding, and feature representation.

In one embodiment, before creating the feature vector, the raw data may need to undergo various preprocessing steps, such as cleaning, normalization, or scaling, to ensure that it is in a suitable format for analysis. In another embodiment, the feature extraction may involve selecting relevant features from the raw data that best capture the essential information needed for the specific task. Feature extraction techniques can range from simple methods like selecting specific columns in a dataset to more complex methods like dimensionality reduction or feature engineering. In yet another embodiment, categorical features or non-numeric data are encoded into numerical values for inclusion in the feature vector. Once the features are extracted and encoded, they may be organized into a structured vector, forming the feature vector for each data point.

For example, in an instance of classifying a dataset of images, the feature vector generator may take each image and extract relevant features, such as color histograms, edge orientations, or texture descriptors, and organize them into a numerical vector. This vector could then be used as input to a machine learning algorithm to train a classifier for image categorization.

In one embodiment, the comparison engine616may use numerical similarity measures to compare documents or texts represented as vectors of word frequencies created by feature vector generator614. The comparison engine616may be a system or application that is designed to compare multiple items, data sets, or entities to identify similarities, differences, or relationships between them. One of the purposes of the comparison engine616may be to facilitate decision-making, analysis, or evaluation by presenting relevant information side-by-side for adequate comparison. In one example, the comparison engine616may be configured to compare event records to compute a similarity score.

The operation of the comparison engine616may depend on the type of data being compared and the specific requirements of the application. The comparison engine616may employ various algorithms and techniques to process and analyze the data, calculate similarities or differences, and present the results in a user-friendly format. One embodiment of the present application uses numerical similarity measures, such as cosine similarity, for example.

FIG.7illustrates an example set of operations for semantic deduplication in accordance with one or more embodiments. One or more operations illustrated inFIG.7may be modified, rearranged, or omitted all together. Accordingly, the particular sequence of operations illustrated inFIG.7should not be construed as limiting the scope of one or more embodiments.

In step702, the system identifies a first subset of characteristics associated with a target event record received in an event stream. In one embodiment, the individual characteristics may be the service type, the channel type, the topic name, to name a few, and these characteristics may be included in the first subset of the characteristics, which may define an event category.

The event stream may include, for example, application events, security events, system events, and custom events generated by installed software. Users can apply filters and search criteria to find specific events of interest. In one embodiment, the event log manager604may determine detailed information about each event, including timestamps, event IDs, severity levels, source, and description. Users can configure the behavior of event logging, such as specifying which types of events to log, setting log size limits, and enabling or disabling specific event categories.

In step704, the system determines whether there are any candidate representative event records associated with the first subset of characteristics. In an example, each identified combination of a subset of characteristics is mapped to a corresponding representative event record. The representative event record represents the event records associated with the same subset of characteristics. Once the first subset of characteristics is identified, the system determines whether any event record (e.g., representative event record) has already been mapped to the first subset of characteristics corresponding to the target event record.

In one example, a hierarchical tree may be used to represent the various combinations of characteristics. Each leaf in the hierarchical tree represents a particular combination of characteristics. Determining whether a representative event record exists for the first subset of characteristics, corresponding to the target event record, may include identifying a leaf node corresponding to the first subset of characteristics and determining whether any representative event record is stored in association with that leaf node. In another example, there is an event record that is the already existing root element of a (first) set, disjointed from any other set by its own subset of characteristics.

The system determines whether an existing event category includes the individual characteristics (the first subset of characteristics) of the target event record. If such a category does exist, the category is already represented by a candidate representative event record, i.e., the root element of the (first) disjoint set. The system may determine that such an event category exists, if the category includes an event record that, indeed, is the candidate representative event record (the root element) of this event category referred to as a (first) disjoint set. Otherwise, the decision of step704may be that no candidate representative event record is found, i.e., that no log entries exist representing the (first) disjoint set.

In step706, the system creates a new representative event record in an event log based on the target event record. The system creates the new representative event record in response to determining (in step704) that no candidate representative event records were found with the same subset of characteristics as the target event record.

Creating a new representative event record may include generating the new representative event record based on all or a portion of the information in the target event record. In an example, the new representative event record includes a portion of the information from the target event record that has been requested by the logging service608. Other information from the target event record, that is not used in generation of the representative event record, may be discarded.

For example, the feature vector generator614may transform raw data or input regarding the target event record into a structured and numerical representation known as a feature vector of the target event record. The feature vector generator614may then generate a feature vector from a subset or set of attributes associated with the target event record and in step706, assign the feature vector to a new representative record. Accordingly, the newly created feature vector of the representative record could be used for future comparisons, e.g., for cosine similarity computations between the new feature vector (and the representative event record accordingly), and any subsequent feature vectors that correspond to future event records.

A feature vector may be a list of numeric values that represent the relevant characteristics or features of an object, sample, or data point. The feature vectors may be used as inputs to various machine learning algorithms, statistical models, or data analysis techniques.

The feature vector generator614may create a feature vector by converting the raw data or input into a format that can be easily understood and processed by machine learning algorithms. The choice of features in the vector may determine the information that the model will use to make predictions or perform analysis. The process may involve data preprocessing, feature extraction, feature encoding, and feature representation.

In one embodiment, if the target event record from step702is associated with a subset of characteristics that have not yet appeared in the existing log entries, then the target event record from step702would establish the (first) disjoint set, i.e., a new event category that the target event record would represent. Accordingly, in step706, the system would create a new representative event record in the event log606based on the target event record from step702. In one example, step706would be the resulting step of an instance where no root element of the (first) disjoint set is found in step704.

The establishment of the new disjoint set would be transmitted to the logging service608in step718. Consequently, the event log manager604would update the event log606by adding a new root element with a fresh subset of characteristics, and the newly updated event log606would be used for comparison with the future events.

The system executes step708in response to identifying one or more candidate representative event records associated with the subset of characteristics associated with the target event record. In step708, the system computes a similarity between the target event record and the candidate representative event record(s) identifying in Step704. The target event record and the candidate representative event record(s) may be associated with the same subset of characteristics, for a first set of attributes, identified in Step702. The system may compute the similarity score based on respective characteristics, of the target event record and the candidate representative event record(s), for a second set of attributes. In one embodiment, the comparison technique may use the similarity criteria612to determine cosine similarity between a second set of characteristics for the target event record and a second set of characteristics for the candidate representative event record.

In one embodiment, cosine similarity is a metric used to measure the similarity between two vectors in a multi-dimensional space. This technique may determine the cosine of the angle between the vectors, representing the degree of alignment or similarity between them. Cosine similarity may measure the cosine of the angle between the non-zero vectors for the two compered event records. In one embodiment, the comparison engine616compares the two event records as vectors of word frequencies created by feature vector generator614.

The two vectors to be compared may be represented as numerical feature vectors in a vector space. Each dimension of the vector may represent a feature or attribute. Cosine similarity may range from −1 to 1, where −1 indicates completely opposite or dissimilar vectors, 0 indicates orthogonality or independence, and 1 indicates identical or highly similar vectors. In another embodiment, the cosine similarity value measures the cosine of the angle between the two vectors, and the larger the value, the closer the vectors are in direction and similarity.

Next, in step710, the system may analyze the results of the similarity computation in step708and produce a decision accordingly. In one embodiment, the resulting cosine similarity values may be compared against a predetermined threshold of similarity. For example, if the threshold of similarity is set at 0.8 (or any other value deemed suitable), then the cosine similarity values computed in step708that exceed the 0.8 threshold would result in an affirmative answer in step710. In the alternative, the answer would be negative, i.e., any existing similarity would be determined insufficient.

In one embodiment, in step708the system compares numerical feature vectors of the target event record and the detected candidate representative event record with the first subset of characteristics in a vector space. In another embodiment, the feature vector generator614creates numerical feature vectors based on the semantic features of the target event record from step702and the candidate representative event record (the root element). Further, the comparison engine616may compute a semantic match score between the target event record and the candidate representative event record. In step710, the computed semantic match score may be compared with the predetermined threshold of similarity (e.g.,0.8, or any other threshold value considered appropriate).

If the similarity computed in step710is considered sufficient, for example, because the computed score exceeds the threshold, then the event log manager604increments a numerical value associated with the representative event record in step712. The numerical value may indicate a number of event records represented by the representative event record.

In one embodiment, the numerical value in step712is a counter that counts event records that have, first, been allocated to the specific disjoint set represented by the representative event record (root element), and second, been determined sufficiently similar to the representative event record. In another embodiment, the criteria for the allocation of the target event record to the first disjoint set may require an exact matching of the first subset of characteristics, while the criteria for the similarity decision may require only exceeding a similarity threshold (as opposed to the exact matching).

Next, in step714, the event log manager604may perform the deduplication by omitting the target event record from the event log606. Namely, action in step714may be a result of two affirmative decisions in steps704and710. Specifically, in this instance the target event record is determined to have the same set of characteristics as an existing candidate representative event record, and, subsequently, a cosine similarity computation produced a sufficient similarity score between the two event records. Accordingly, the system may ascertain that logging the target event record would be redundant and wasteful.

In one embodiment, the event log manager604may perform the deduplication by not logging the target event record in the first disjoint set that it was allocated to due to its similarity to the already existing root element of the first disjoint set, i.e., the representative event record. In another embodiment, the removal of the target event record from the event log606may be complemented by the incrementation of the numerical value in step712. Specifically, while step714may remove the event records determined sufficiently similar to the root element of the set, thereby reducing the event log606, step712may provide information about the magnitude of the achieved deduplication by accounting for the deduplication instances by incrementing the counter.

Lastly, the omission of the target event record and the resulting value of the counter would be transmitted to the logging service608in step718. Consequently, the event log manager604would update the event log606, and the newly updated event log606would be used for comparison with the future events.

As mentioned above, the criteria for the allocation of the target event record to the specific disjoint set may require an exact match of the specific subset of characteristics, while the criteria for the similarity decision may require only exceeding a similarity threshold. In one embodiment, in step704, the criteria for the allocation of the target event record to the disjoint set may be met, but in step710, the criteria for the similarity decision may be unmet, i.e., the computed score may be lower than the threshold. Consequently, step716may follow, which is a decision point on whether there are any other candidate representative event records available to compare against. If so, then the process would return to step708, where the numerical feature vectors of the target event record and the other candidate representative event record would be compared in a vector space, and the computed score would, once again, be evaluated with respect to the threshold in step710.

If not, a new disjoint set would be formed with its new representative event record in step706. Subsequently, the establishment of the new disjoint set would be transmitted to the logging service608in step718. Consequently, the logging service608would update the event log606via the event log manager604by adding a new root element with a fresh subset of characteristics, and the newly updated event log606would be used for comparison with the future events.

FIGS.8A-8Eillustrate changes made to an event log within the semantic deduplication process.FIG.8Ashows an embodiment of an event log606in an initial state. The logging stream may have characteristics such as service type802, channel type804, and topic names806a-806c, and these characteristics together may define a subset of characteristics.

In step702, a target event record may appear in the event log606with of a specific service type802, channel type804, and topic names806a-806c, for example. Accordingly, the first subset of characteristics associated with the target event record is identified. In step704, it may be determined that no candidate representative event record associated with first subset of characteristics exists in the event log606; thus, a new disjoint set A may be created in step706, and the target event record may be logged as the root element A0808of the disjoint set A with the first subset of characteristic.

Subsequently, in the embodiment shown inFIG.8B, a new target event record appears in the event log606as per step702, and the new target event log is associated with its own subset of characteristics. In this example, the new subset of characteristics is the same as the first subset of characteristics associated with the already logged root element A0808, and as a result, step704renders an affirmative decision. Therefore, the new target event log is allocated to the disjoint set A, as element A1810, together with the root element A0808.

Subsequently, in step708, the system computes similarity between the new element A1810and the root element A0808. In one embodiment, a cosine similarity technique is used to measure the semantic similarity between the vectors of the new element A1810and the root element A0808. In step710, the computed similarity value is compared with a threshold value of 0.9, for example. In this example, the computed similarity value is 0.92, i.e., it is greater than 0.9. Therefore, in step712, the event log manager604increments numerical value associated with the root element A0808, thereby indicating a number of event records represented by the root element A0808; concomitantly, however, the new element A1810is discarded as per step714, and the resulting information is sent to the logging service718to update the event log606.

FIG.8Cshows an embodiment of an event log606, where yet another new target event record appears in the event log606as per step702, and the new target event log is, once again, associated with its own subset of characteristics. In this example, the new subset of characteristics is not the same as the first subset of characteristics associated with the already logged root element A0808. As a result, in step704, the system renders a negative decision. Consequently, the new target event log is not allocated to the disjoint set A, but instead, in step706, new representative event record (root element B0812) is created in the event log606based on the new target event record. And the root element B0812designates a new disjoint set B with the new subset of characteristics.

InFIG.8D, the next new target event record appears in the event log606as per step702, and the new target event log is associated with its own subset of characteristics. In this example, the new target event log has the same subset of characteristics of the disjoint set B. Hence, in step704, an affirmative decision is made, and the new target event log is allocated to the disjoint set B, as element B1814, together with the root element B0812.

Subsequently, in step708the system computes similarity between the new element B1814and the root element B0812. In one embodiment, a cosine similarity technique is used to measure the semantic similarity between the vectors of the new element B1814and the root element B0812, and in step710, the computed similarity value is compared with a threshold value of 0.8. In this example, the computed value is 0.85, i.e., it is greater than 0.8. Therefore, in step712, the event log manager604increments numerical value associated with the root element B0812, thereby indicating a number of event records represented by the root element B0812. At the same time, the new element B1814is discarded as per step714, and this information is transmitted to the logging service718to update the event log606.

InFIG.8E, the next new target event record appears in the event log606as per step702, and the new target event log is associated with its own subset of characteristics. In this example, the new target event log has the same subset of characteristics of the disjoint set B. Accordingly, in step704, an affirmative decision is made, and the new target event log is temporarily allocated to the disjoint set B, as element B1814, together with the root element B0812.

Subsequently, in step708the system computes similarity between the new element B1814and the root element B0812. In one embodiment, a cosine similarity technique is used to measure the semantic similarity between the vectors of the new element B1814and the root element B0812, and in step710, the computed similarity value is compared with a threshold value of 0.8. In this example, the computed value is 0.75, i.e., it is lower than 0.8. Thus, in step710a negative determination ensues, leading to the inquiry in step716whether any other candidate representative event records exist with the subset of characteristics of disjoint set B. Being that the answer is negative, the new target event log is no longer allocated to the disjoint set B (or A). Instead, in step706, new representative event record (root element C0816) is created in the event log606based on the new target event record. And the root element C0816designates a new disjoint set C with the new subset of characteristics.