A system and method for data aggregation and process automation is disclosed. The method includes receiving a first data object from a first integration point through a first smart adapter, identifying an appropriate rules library from a plurality of rules libraries using a rules engine, the appropriate rules library being identified using the first data object, and applying the appropriate rules library through the rules engine. The rules are applied by instructing a transformation module to transform the first data object into a transformed data object, instructing a validation module to validate at least one of the first data object and the transformed data object, and instructing an aggregation module to perform a statistical analysis on one of the first data object and the transformed data object. Finally, the method includes sending the transformed data object to a second integration point associated with a second smart adapter.

Systems and methods for determining a query for a data store are described. A natural language text may be analyzed using heuristic processing and one or more machine learning models. Query parameters may be determined from the heuristic processing and machine learning and combined to form a query in a query language. In the heuristic processing, parsing rules may be used to remove conditional terms to generate a base question. The base question may be input to the one or more machine learning model to generate a base query which may be combined with query parameters related to the conditional terms.

Methods and systems are disclosed for processing policies. A query policy tree with expressions and combination rules is received. A second policy tree stored in a policy repository is searched to select a sub-policy in a neutral format, reflecting a structure with a combination rule and a leaf with a condition expression. A depth of the structure is reduced through removal of redundant combination rules and combinable leaf expressions. The sub-policy tree is selected from the second stored policy tree by comparing the query policy tree. The selected sub-policy tree is copied to a third policy tree. Leaves of the third policy tree are evaluated against the leaves of the query policy. The nodes of the third policy are joined against the node of the query policy. The third policy is reduced to the neutral format by combining evaluated leaves and removing redundant combination rules. Finally, a response is provided.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.

Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolvedrequest by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency database analysis system, and outputting the results data for presentation to a user.

Computer-readable media, methods, and systems are disclosed for applying rules and roles to generate optimized queries for optimized queries implementing restricted access to data. receiving, from a querying user, a data query including a data type and a query action. Roles associated with the querying user are retrieved from the querying user corresponding to the data type and the query action. A plurality of rules associated with the roles are retrieved by a security controller. Based on the rules and by way of the security controller, a query restrictor is computed to secure the data query for the action. One or more conditions associated with the rules are combined by conjunction. The rules associated with the role and the roles are combined by disjunction, to form restriction terms associated with the query restrictor. Finally, the data query is executed at a database server and results are returned.

According to certain aspects, a method can include creating a backup copy of data associated with a virtual machine (VM) on one or more secondary storage devices, wherein the backup copy includes corresponding secondary copies of a plurality of files associated with the VM; analyzing metadata associated with the secondary copies to determine which of the plurality files are eligible to be removed from the primary storage device; in response determining that one or more files are eligible to be removed from the primary storage device, for respective file of the one or more files: determining whether the respective file has been changed since a first time at which the backup copy of the data associated with the VM was created; in response to determining that the respective file has not changed since the first time, removing the respective file; and adding a file placeholder for the removed file.

The disclosed embodiments include a method performed by a data intake and query system. The method includes receiving a search query by a search head, defining a search process for applying the search query to indexers, delegating a first portion of the search process to indexers and a second portion of the search process to intermediary node(s) communicatively coupled to the search head and the indexers. The first portion can define a search scope for obtaining partial search results of the indexers and the second portion can define operations for combining the partial search results by the intermediary node(s) to produce a combination of the partial search results. The search head then receives the combination of the partial search results, and outputs final search results for the search query, where the final search results are based on the combination of the partial search results.

A recommender system includes an information storage and a recommender. The recommender configures the information storage according to a recommender data structure. The data structure includes multiple nodes, each including an inquiry hook and an option hook connected to each other. The inquiry hook includes an inquiry text corresponding to a user inquiry. In the data structure, a first node is associated with one or more child nodes, wherein the user inquiry text of the first node has one or more option values, each option value is included respectively in the option hook of the child nodes. The inquiry hook of at least one of the multiple nodes includes an end point hook, which corresponds to one or more of the following recommendation actions: recommending a carrier, manual submission, and declining coverage. The recommender system may traverse the recommender data structure to retrieve recommendation action(s) to the user.

A data security evaluation computing device for evaluating data security of a target system is coupled to a plurality of data sources including the target system, and receives a request message requesting data security review, the request message including an identifier of the target system. The computing device queries a first data source to receive data representing whether the target system has previously breached, and locally caches the data. The computing device queries a second data source to receive data associated with a potential for a future data breach, and locally caches the data. The computing device also generates a data security score by analyzing the locally cached data, the data security score representing a likelihood the target system was or will be the subject of a data breach, compiles the data security score and additional data into a data security report, and transmits a response message including the report.