Abstract: Techniques for generating a client-side Application Programming Interface (API) are described herein. The techniques may include analyzing source code that is related to an API of a service provider and/or content that describes the source code and/or the API of the service provider. The analysis may identify characteristics of the API of the service provider, such as routines, characteristics of the routines, data constructs, characteristics of the data constructs, and so on. The techniques may also include generating a representation to represent the characteristics of the API of the service provider and generating a client-side API based on the representation. The client-side API may include a library of client-side routines and/or data constructs that provide access to routines and/or data constructs that are made available via the API of the service provider.

Abstract: Disclosed herein is a system and method for searching or processing queries for searching for documents contained in a domain specific knowledge base. The system takes a query and generates from the query a modified version of the query by passing the query through one or more filters in a query processor. The query processor adds or removes terms from the query. The query processor can add or recognize that two words that appear to be separate words actually identify a specific software entity or can determine that a number appearing in a query is not just a number but refers to a specific version or a number relevant to the specific problem.

Abstract: Disclosed herein is a system and method for generating an enhanced index for documents contained in a knowledge base. Documents or configuration snapshots are tokenized and then passed through a number of filters. The filters modify the token stream to generate an enhanced token stream for the document by removing words from the stream and adding domain level knowledge to the tokens in the stream. The token stream is then added to the index for the document so that searches can be completed against the knowledge base when searches are generated from configuration snapshots or from inputted search queries.

Abstract: A hierarchical network analytics system operated by a computing device or system is described. In some example techniques, the analytics system may determine results of a plurality of first level analyses each based at least in part on results of a respective plurality of data queries that return respective subsets of a plurality of types of network data. The analytics system may determine a result of a second level analysis based at least in part on results of the plurality of first level analyses.

Abstract: Aspects of the subject disclosure are directed towards automatically inferring the significant parts of bug reports, including by querying a knowledge base built by mining information from a large number of earlier bug reports. Common phrases in the earlier bug reports are filtered to provide a subset of phrases relevant to a bug domain, which are stored in the knowledge base and mapped to an ontology model of the knowledge base. When a new bug report is received for inference/analysis, the phrases therein are tagged based upon the ontology model, and the knowledge base queried with the tagged phrases to determine problems, activities and actions that are likely related to the new bug report.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a system that that can include an event analysis component and one or more processing devices configured to execute the event analysis component. The event analysis component can be configured to obtain events from event logs, the events reflecting failures by one or more network devices in one or more data centers and characterize a service level of an application or a network device based on the events. For example, the event analysis component can be configured to characterize the availability of an application based on one or more network stamps of the application.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifested as a system that can include an inference engine and at least one processing device configured to execute the inference engine. The inference engine can be configured to perform automated detection of concepts expressed in failure logs that include unstructured data. For example, the inference engine can analyze text of support tickets or diary entries relating to troubleshooting of an electronic network to obtain concepts identifying problems, actions, or activities. The inference engine can also be configured to generate output that reflects the identified concepts, e.g., via a visualization or queryable programming interface.

Abstract: The discussion relates to middlebox reliability. One example can apply event filters to a dataset of middlebox error reports to separate redundant middlebox error reports from a remainder of the middlebox error reports of the dataset. The example can categorize the remainder of the middlebox error reports of the dataset by middlebox device type. The example can also generate a graphical user interface that conveys past reliability and predicted future reliability for an individual model of an individual middlebox device type.