Abstract: Automated event monitoring is performed utilizing a Knowledge Graph (KG) constructed by grouping and consolidation of a variety of log Entry Types. A log entry is received by a knowledge graph parser (Kg parser). That parser finds contiguous sub-strings in a log entry that have a parameterized basic-format. The parser figures out which basic-formats are present, where, and with which parameters. Given a sub-string, its basic-format and its parameters, the parser can correctly parse the sub-string to components (e.g., keys and values if a key-value format; fields if a structured format). A result of the parsing is an entity type tree structure. Next, a grouping and consolidation capability functions to modify the KG to incorporate an incoming new entry type structure. The KG may be consumed by a user (e.g., visualization; querying), and may provide an artifact to an event monitoring system to automatically trigger certain actions (e.g., alerts).

Abstract: Automated event monitoring is performed utilizing a Knowledge Graph (KG) constructed by grouping and consolidation of a variety of log Entry Types. A log entry is received by a knowledge graph parser (Kg parser). That parser finds contiguous sub-strings in a log entry that have a parameterized basic-format. The parser figures out which basic-formats are present, where, and with which parameters. Given a sub-string, its basic-format and its parameters, the parser can correctly parse the sub-string to components (e.g., keys and values if a key-value format; fields if a structured format). A result of the parsing is an entity type tree structure. Next, a grouping and consolidation capability functions to modify the KG to incorporate an incoming new entry type structure. The KG may be consumed by a user (e.g., visualization; querying), and may provide an artifact to an event monitoring system to automatically trigger certain actions (e.g., alerts).

Abstract: In some embodiments, there is provided a parser comprising at least one data processor; and at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising: receiving a line of text to be parsed; and processing, based on a configuration for the parser, the line of text into parsed text by at least: detecting one or more brackets and one or more separators in the text, determining a hierarchy for the text based on one or more parts, the one or more parts determined from the one or more brackets and one or more separators, and parsing, based on the hierarchy, the text to form the parsed text. Related systems and articles of manufacture are also provided.

Abstract: Systems and methods are provided for parsing a log file to compute a meaning for each event by detecting words in text representing the event and generating a sequence of select words found in the text representing the event, and to compute a syntax for each event by generating an abstract of the text representing the event. The systems and methods further group the events in the log file by meaning and syntax to generate a list of groupings, each grouping of the list of groupings comprising a sequence of select words from the events in the grouping, an abstract for the events in the grouping, and a number of events in the log file that match based on the sequence of select words and abstract.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing enterprise resource textual analysis. An embodiment operates by receiving a request to search a document management system for a keyword. One or more relevant documents of the document management system associated with the keyword are identified. The keyword is compared to a data dictionary corresponding to an enterprise system associated with the document management system. Contextual search results including the one or more relevant documents and a relationship between the keyword and data of the enterprise system are provided.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a log entry instance for creating a definition for a log entry type. The program further generates a markup associated with the log entry instance. The program also generates a document based on the markup associated with the log entry instance. The program further retrieves a set of reference documents. Each reference document is associated with a particular type of event. The program also generates a ranked list of types of events based on the document and the set of reference documents.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a log entry instance for creating a definition for a log entry type. The program further generates a markup associated with the log entry instance. The program also generates a document based on the markup associated with the log entry instance. The program further retrieves a set of reference documents. Each reference document is associated with a particular type of event. The program also generates a ranked list of types of events based on the document and the set of reference documents.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing enterprise resource textual analysis. An embodiment operates by receiving a request to search a document management system for a keyword. One or more relevant documents of the document management system associated with the keyword are identified. The keyword is compared to a data dictionary corresponding to an enterprise system associated with the document management system. Contextual search results including the one or more relevant documents and a relationship between the keyword and data of the enterprise system are provided.

Abstract: A log file including a plurality of log entries is accessed. Each log entry of the plurality of log entries is analyzed to identify components of each log entry. The components of the particular log entry indicate an event. The event is associated with roles. Each role is associated with one or more attributes. Semantic meaning of the event associated with the particular log entry is determined. A mapping is performed by applying contextual information from one or more semantic meaning models stored in a knowledgebase to the identified components of each log entry to derive semantic meaning for the particular log entry. The derived semantic meaning is modeled for the particular log entry. The modeled semantic meaning is recorded in the knowledgebase as a new semantic meaning model for future use.

Abstract: A sample log file including a plurality of log entries for log learning is accessed, using a log interpretation controller, prior to runtime as part of a log learning process. Each of the plurality of log entries is analyzed. A log entry type is assigned to each of the plurality of log entries. A log type and semantic event are assigned to each log entry type. Generation of runtime rules is triggered for analyzing unknown log entries. The runtime rules include characteristics of particular log entry types that allow unique identification of the particular log entry type for a particular unknown log entry. The generated runtime rules are loaded into a runtime parser.

Abstract: Systems and methods are provided for providing schema mapping and data generation. According to one exemplary method, a first mapping formula is obtained for transforming values associated with a source attribute into at least one value associated with a conceptual attribute. The source attribute values comprise a plurality of duplicate values, and the first mapping formula performing a common operation on the duplicate values. The method includes generating mapping data for mapping the conceptual attribute to a target attribute of a target model, and based on the mapping data and the first mapping formula, generating a second mapping formula for transforming the conceptual attribute value into at least one value associated with the target attribute.

Abstract: Systems and methods are provided for providing schema mapping and data generation. According to one exemplary method, a first mapping formula is obtained for transforming values associated with a source attribute into at least one value associated with a conceptual attribute. The source attribute values comprise a plurality of duplicate values, and the first mapping formula performing a common operation on the duplicate values. The method includes generating mapping data for mapping the conceptual attribute to a target attribute of a target model, and based on the mapping data and the first mapping formula, generating a second mapping formula for transforming the conceptual attribute value into at least one value associated with the target attribute.

Abstract: Systems and methods are provided for schema mapping and the transformation of data. In accordance with one implementation, a computer-implemented method is provided that comprises loading a conceptual model, wherein the conceptual model contains at least one conceptual object, loading a source schema and a source instance, converting the source schema and the source instance into a source model, and acquiring, from the user, at least one forward mapping usable to map at least a source attribute of the source model to a conceptual attribute of the conceptual model, wherein the forward mapping includes a mapping formula. Further, the method may include loading a target schema and a target instance, converting the target schema and the target instance into a target model, and reverse mapping at least one of conceptual attributes of the conceptual model to a target attribute of the target model based on the forward mapping and the mapping formula.

Abstract: Systems and methods are provided for schema mapping and the transformation of data. In accordance with one implementation, a computer-implemented method is provided that includes the steps of simultaneously displaying to a user, via a graphical user interface, layouts of the first and second documents, wherein the first and second documents have different data schemas and are instantiated with elements containing data values, and acquiring one or more associations from the user to map a first element of the first document to a second element of the second document. The method further includes the steps of indicating to the user a second association between elements in the first and second documents based on the association acquired from the user, and storing the association including the layout data corresponding to the first and second elements.