Abstract: System and methods are described for generating training data for a natural language search system, the training data including synthetic user queries and associated structured query language (SQL) statements. A method includes generating an abstract query representation from a probabilistic context-free grammar (PCFG), selecting a sample record from a database using the abstract query representation, generating a tagged user query from the abstract query representation and the sample record, and generating the one or more SQL statements from the tagged user query and one or more SQL templates.

Abstract: A database system may receive a natural language query that is associated with a tenant of a multi-tenant system. The natural language query may be parsed into a set of tokens, and the set of tokens may be tagged, using a tenant specific tagging model associated with the tenant, the set of tokens with at least one pre-configured data type identifier that is configured for the plurality of tenants. A global tagging model that supports the plurality of tenants of the multi-tenant system may tag the set of tokens with at least one category identifier. The global tagging model may use the natural language query and the pre-configured data type identifier to identify the at least one category identifier. The system may execute a database query on a database associated with the tenant using the at least one pre-configured data type identifier and the at least one category identifier.

Abstract: System and methods are described for parsing log data items to find events in the log data items, wherein an event comprises at least a portion of a log data item matching an event definition; aggregating a plurality of performance metric definitions into a single expression; and generating a plurality of performance metrics from the events by applying the single expression to the events.

Abstract: Systems and methods are described for applying a plurality of data points of a time series data set representing values of a metric measuring performance of a cloud computing service to a machine learning model to predict a forecast of a most likely value of the metric at a selected future time. The method includes determining whether the plurality of data points of the time series data set are anomalies according to the machine learning model and the forecast and generating a collective anomaly from the anomalies when the plurality of data points is determined to be anomalies. The method further includes determining whether the collective anomaly does not meet one or more cloud computing service level objective (SLO) threshold requirements and sending an alert when the collective anomaly does not meet one or more cloud computing SLO threshold requirements.

Abstract: A computer system is configured to receive a plurality of previous user selections by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to determine weights for the various database fields included in the previous user selections and rank subsequent search results for a subsequent search of the database based on the determined weights, where the one or more weights affect a ranking of a search result based on a match associated with the particular database field. The computer system is further configured to receive customized search result layout settings specifying that one or more specified database fields are displayed to the user when the search results are displayed, where one or more weights for the particular database field are based on the customized search result layout settings.

Abstract: Systems and methods are described for generating metrics from log data items, automatically inferring one or more schemas based at least in part on analyzing samples of the log data items, validating samples of the log data items against the one or more schemas to detect log data item errors, and analyzing the log data item errors according to metrics analytics rules to determine an effect of the log data item errors on a quality measurement of the metrics.

Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating relevance prediction-based ranking and presentation of documents for intelligent searching in cloud computing environments in database systems according to one embodiment. In one embodiment and by way of example, a method includes receiving a query, predicting relevance of documents associated with the query based on content of the query and historical user expectations, where the relevance is predicted based on comparison of a first relevance prediction with a second relevance prediction. The method may further include ranking the documents based on the predicted relevance, where the documents are sorted based on the ranking, and communicating, in response to the query, the ranked and sorted documents to a computing device over a communication network.

Abstract: Systems and methods are described for generating metrics from log data items, automatically interring one or more schemas based at least in part on analyzing samples of the log data items, validating samples of the log data items against the one or more schemas to detect log data item errors, and analyzing the log data item errors according to metrics analytics rules to determine an effect of the log data item errors on a quality measurement of the metrics.

Abstract: System and methods are described for parsing log data items to find events in the log data items, wherein an event comprises at least a portion of a log data item matching an event definition; aggregating a plurality of performance metric definitions into a single expression; and generating a plurality of performance metrics from the events by applying the single expression to the events.

Abstract: Systems and methods are described for applying a plurality of data points of a time series data set representing values of a metric measuring performance of a cloud computing service to a machine learning model to predict a forecast of a most likely value of the metric at a selected future time. The method includes determining whether the plurality of data points of the time series data set are anomalies according to the machine learning model and the forecast and generating a collective anomaly from the anomalies when the plurality of data points is determined to be anomalies. The method further includes determining whether the collective anomaly does not meet one or more cloud computing service level objective (SLO) threshold requirements and sending an alert when the collective anomaly does not meet one or more cloud computing SLO threshold requirements.

Abstract: Methods, computer readable media, and devices for modeling heterogeneous feature sets for use in personalized search are provided. The method may include generating a similarity factor for each of a plurality of personalization features. For each of the plurality of personalization features, a personalization feature weight is calculated. Each personalization feature weight is converted into a probability distribution and each similarity factor is scaled based on a corresponding probability distribution. Based on each scaled similarity factor, a most recently used affinity value is generated for each corresponding personalization feature. The most recently used affinity values are used to generate a ranking function for use as part of personalized search.

Abstract: A database system may receive a natural language query that is associated with a tenant of a multi-tenant system. The natural language query may be parsed into a set of tokens, and the set of tokens may be tagged, using a tenant specific tagging model associated with the tenant, the set of tokens with at least one pre-configured data type identifier that is configured for the plurality of tenants. A global tagging model that supports the plurality of tenants of the multi-tenant system may tag the set of tokens with at least one category identifier. The global tagging model may use the natural language query and the pre-configured data type identifier to identify the at least one category identifier. The system may execute a database query on a database associated with the tenant using the at least one pre-configured data type identifier and the at least one category identifier.

Abstract: A computer system is configured to receive a plurality of previous user selections by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to determine weights for the various database fields included in the previous user selections and rank subsequent search results for a subsequent search of the database based on the determined weights, where the one or more weights affect a ranking of a search result based on a match associated with the particular database field. The computer system is further configured to receive customized search result layout settings specifying that one or more specified database fields are displayed to the user when the search results are displayed, where one or more weights for the particular database field are based on the customized search result layout settings.

Abstract: System and methods are described for generating training data for a natural language search system, the training data including synthetic user queries and associated structured query language (SQL) statements. A method includes generating an abstract query representation from a probabilistic context-free grammar (PCFG), selecting a sample record from a database using the abstract query representation, generating a tagged user query from the abstract query representation and the sample record, and generating the one or more SQL statements from the tagged user query and one or more SQL templates.

Abstract: A computer system is configured to receive a plurality of previous user selection by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to analyze the plurality of previous user selections to identify how frequently the same values are included in the various previous database entries. The computer system is configured to determine weights for the various database fields and rank subsequent search results for a subsequent search of the database based on the determined weights.

Abstract: A computer system is configured to receive a plurality of previous user selection by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to analyze the plurality of previous user selections to identify how frequently the same values are included in the various previous database entries. The computer system is configured to determine weights for the various database fields and rank subsequent search results for a subsequent search of the database based on the determined weights.

Abstract: A map generator machine generates influence maps based on profiles of entities, such as members of an online social networking service. The entities can be treated as nodes within a social graph, and each node may be represented by a corresponding node profile. The machine is configured to access a database of node profiles and rank the nodes according to seniority information contained in the node profiles. The machine is further configured to group nodes into clusters based on skill similarity based on skill descriptors included in their corresponding node profiles. The machine is also configured to generate one or more maps to depict one or more the subsets of the nodes. As generated, such a map is a graphical presentation of at least some of the nodes of the social graph, and the map may be generated with visual indicators of seniority and skill similarity.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which provide for a personalized expertise searching. When a user of the social networking service enters a search query, the system determines if the user is searching for members who possess a particular skill. If the user is searching for members who possess a particular skill, the search results are post-processed by personalizing the search results using one or more machine-learning models which utilize one or more observed features about the user that enters the query, the skills of the members of the social networking service, and the query itself. In some examples, the system may utilize multiple machine-learning models in multiple passes to fine tune the relevance of the search results and to ensure that the post-processing returns search results in a timely manner.

Abstract: Techniques for inferring a professional reputation for a member of an online social networking service are described. A recommendation graph is generated from professional recommendations submitted by members of the online social networking service for other members of the online social networking service. Using the generated recommendation graph, a reputation model is trained to learn a respective importance for each respective feature of a set of features of electronic profiles on the online social networking service. A professional reputation of a member of the online social networking service is estimated by applying the trained reputation model to a feature vector of the electronic profile of the member, producing a score representing the professional reputation of the member.

Abstract: A map generator machine generates influence maps based on profiles of entities, such as members of an online social networking service. The entities can be treated as nodes within a social graph, and each node may be represented by a corresponding node profile. The machine is configured to access a database of node profiles and rank the nodes according to seniority information contained in the node profiles. The machine is further configured to group nodes into clusters based on skill similarity based on skill descriptors included in their corresponding node profiles. The machine is also configured to generate one or more maps to depict one or more the subsets of the nodes. As generated, such a map is a graphical presentation of at least some of the nodes of the social graph, and the map may be generated with visual indicators of seniority and skill similarity.