Autocompleting into an invite box for purposes of sharing an executable computing resource such as an application or portion thereof. However, the autocomplete is populated with potential sharees of multiple tenants or with identities that are not registered with the tenant directory of the user. Thus, potentially any potentially sharee worldwide may be populated within the list of potential sharees. As the desired potential sharee comes into view, that potential sharee may be selected, and added to a list of one or more selected sharees. At some point, a control may be selected to allow the executable computing resource to be shared with the selected sharees within the list.

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.

A system and method for rearranging search operators of a search query are disclosed. In some example embodiments, a first initial search query is received from a first user. The first initial search query comprises at least one search operator and at least one search term. A first rewritten search query is generated based on the first initial search query. The generating of the first rewritten search query comprises rewriting at least one optional search operator in the first initial search query using at least one required search operator and at least one exclusion search operator in response to a determination that the first initial search query comprises the at least one optional search operator and does not comprise any required search operators. A first set of search results is generated for the first rewritten search query and caused to be presented to the first user.

Techniques for providing user-influenced search results may be provided. The system can introduce an enhanced query language that allows users to reprogram a computer system responsible for providing search results. In an embodiment, the enhanced query language is generated by the system and exposed to users. In another embodiment, a query language is generated by the user and translated by the system to an enhanced query language.

Methods and apparatus consistent with the invention allow a user to submit an ambiguous search query and to receive relevant search results. Queries can be expressed using character sets and/or languages that are different from the character set and/or language of at least some of the data that is to be searched. A translation between these character sets and/or languages can be performed by examining the use of terms in aligned text. Probabilities can be associated with each possible translation. Refinements can be made to these probabilities by examining user interactions with the search results.

Systems and methods are disclosed that provide for extracting information from a text string. In certain embodiments, a system is disclosed to receive a textual string representative of a distributed computing operation. The system is configured to isolate an identifier included in the textual string based on information in the textual string and/or other information associated with the distributed computing operation. The system is also configured to provide the identifier to a computer device over a network.

Disclosed are systems and methods for generating search result data based on machine-encoded text generated by computer vision optical character recognition machine learning techniques performed on digital media. The disclosed systems and methods provide a novel framework for performing machine learning visual search or machine learning text extraction techniques on digital media in order to extract and analyze the data therein and further conduct search queries based on the extracted and analyzed data. The disclosed framework may leverage the aforementioned computer vision machine learning techniques in order to provide a user with relevant search results regarding objects and text detect in digital media captured on a user device.

Devices and techniques are generally described for translation compression. In various examples, first text comprising at least one word composed in a first language may be determined. In some examples, first data that indicates a target language for translation may be received. First symbol data representing an encoded translation of the first text in the target language may be retrieved from memory. A symbol decoder may be determined for decoding the first symbol data. Second text may be generated by inputting the first symbol data into the symbol decoder. The second text may represent a translation of the first text into the target language. In various examples, the second text may be output as video and/or audio.

To execute function-step-based graph queries on a graph engine that has its own graph query language, rather than re-implementing an existing infrastructure to support function-step-based graph protocols, function-step-based graph queries are transformed to the graph query language that is understood by the graph engine. The existing infrastructure computes the results of the transformed queries. Result sets are then transformed to function-based-based result sets, which are returned to customers. In this manner, the graph engine supports function-step-based graph query workloads without implementation of the function-step-based graph protocol.

Methods, systems, and computer programs are presented for improving search speed and quality using differential indexing. One method includes an operation for building a first index for a database, the first index being for first tokens resulting from normalizing words in input data. Further, the method includes building a second index for the database, the second index being for second tokens comprising words of the input data eliminated from the first index during the normalizing. The method further includes operations for receiving a raw query for a search of the database, and for generating a search query based on tokens of the raw query. The search query comprises a combined search of the first index and the second index. A search is performed based on the search query, and results of the search are returned for presentation on a display.