Systems and methods for providing a binary representation of a web page. A server may compile a source page, such as a hypertext markup language (“HTML”) document, to create a compiled representation of the source page. Creation of a compiled representation may include generating a document object model (“DOM”) for the content associated with the source page. The compiled page may be transmitted to a client as a random-access file or flat file, where the data is encoded in one or more data types other than a string representation. The client may receive the compiled page and load the compiled page directly as a DOM for the page without the need to parse HTML text strings. The server and client may reconcile the data format of the compiled page, such as by exchanging or verifying a compiler dictionary associated with the page.

Disclosed are methods, apparatus, systems, and computer-readable storage media for representing a portion of a user interface of a web browser as a web address. In some implementations, a computing device receives a request to encode as a web address a portion of a user interface provided in a web browser at the computing device. The computing device then generates a web address to identify content of the portion of the user interface. The portion of the user interface represented by the web address includes a primary user interface component and one or more secondary user interface components associated with the primary user interface component.

Disclosed are methods, apparatus, systems, and computer-readable storage media for representing a portion of a user interface of a web browser as a web address. In some implementations, a computing device receives a request to encode as a web address a portion of a user interface provided in a web browser at the computing device. The computing device then generates a web address to identify content of the portion of the user interface. The portion of the user interface represented by the web address includes a primary user interface component and one or more secondary user interface components associated with the primary user interface component.

Described systems and methods allow an automatic translation from a natural language (e.g., English) into an artificial language such as a structured query language (SQL). In some embodiments, a translator module includes an encoder component and a decoder component, both components comprising recurrent neural networks. Training the translator module comprises two stages. A first stage trains the translator module to produce artificial language (AL) output when presented with an AL input. For instance, the translator is first trained to reproduce an AL input. A second stage of training comprises training the translator to produce AL output when presented with a natural language (NL) input.

Described systems and methods allow an automatic translation from a natural language (e.g., English) into an artificial language such as a structured query language (SQL). In some embodiments, a translator module includes an encoder component and a decoder component, both components comprising recurrent neural networks. Training the translator module comprises two stages. A first stage trains the translator module to produce artificial language (AL) output when presented with an AL input. For instance, the translator is first trained to reproduce an AL input. A second stage of training comprises training the translator to produce AL output when presented with a natural language (NL) input.

Technology is described herein for facilitating a user's interaction with a digital ink document. The technology internally represents the ink document using a data structure having a hierarchy of nodes. The nodes describe respective elements in the ink document. The technology leverages the data structure to identify a set of nodes that grows upon the user's repeated selection of a particular part of the ink document. At each stage of the selection, the technology highlights a set of elements in the ink document that correspond to the current set of identified nodes. According to another illustrative aspect, the technology produces the data structure by modifying an original data structure provided by a text analysis engine. The technology performs this task with the objective of accommodating structured interaction by the user with the ink document.

A system to collect and store in a special data structure arranged for rapid searching massive amounts of data. Performance metric data is one example. The performance metric data is recorded in time-series measurements, converted into unicode, and arranged into a special data structure having one directory for every day which stores all the metric data collected that day. The data structure at the server where analysis is done has a subdirectory for every resource type. Each subdirectory contains text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions.

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.

A computer system identifies that a first portion of markup language, extracted from a markup language document of a website, corresponds to a first actionable element, wherein the first portion of markup language is a variable length representation. In response to identifying that the first portion of markup language corresponds to the first actionable element, the computer system utilizes a recurrent neural network (RNN) encoder to create a first code representation that corresponds to the first portion of markup language. The computer system identifies a first additional information that corresponds to one or more pre-defined goals. The computer system creates a final fixed length markup language representation that includes the first code representation and the first additional information. The computer system inputs the final fixed length markup language representation into a model.

A computer system identifies that a first portion of markup language, extracted from a markup language document of a website, corresponds to a first actionable element, wherein the first portion of markup language is a variable length representation. In response to identifying that the first portion of markup language corresponds to the first actionable element, the computer system utilizes a recurrent neural network (RNN) encoder to create a first code representation that corresponds to the first portion of markup language. The computer system identifies a first additional information that corresponds to one or more pre-defined goals. The computer system creates a final fixed length markup language representation that includes the first code representation and the first additional information. The computer system inputs the final fixed length markup language representation into a model.