Processing stacked data structures is provided. A system receives an input audio signal detected by a sensor of a local computing device, identifies an acoustic signature, and identifies an account corresponding to the signature. The system establishes a session and a profile stack data structure including a first profile layer having policies configured by a third-party device. The system pushes, to the profile stack data structure, a second profile layer retrieved from the account. The system parses the input audio signal to identify a request and a trigger keyword. The system generates, based on the trigger keyword and the second profile layer, a first action data structure compatible with the first profile layer. The system provides the first action data structure for execution. The system disassembles the profile stack data structure to remove the first profile layer or the second profile layer from the profile stack data structure.

Processing stacked data structures is provided. A system receives an input audio signal detected by a sensor of a local computing device, identifies an acoustic signature, and identifies an account corresponding to the signature. The system establishes a session and a profile stack data structure including a first profile layer having policies configured by a third-party device. The system pushes, to the profile stack data structure, a second profile layer retrieved from the account. The system parses the input audio signal to identify a request and a trigger keyword. The system generates, based on the trigger keyword and the second profile layer, a first action data structure compatible with the first profile layer. The system provides the first action data structure for execution. The system disassembles the profile stack data structure to remove the first profile layer or the second profile layer from the profile stack data structure.

The present disclosure provides a method for automatically collecting and matching laboratory data, including: obtaining a creation time of experimental data, determining target experimental data corresponding to a target time in accordance with the creation time, segmenting the target experimental data into a plurality data blocks, generating a data block index table, including at least one data block identifier, according to the data blocks, selecting a target matching mode from a plurality of predetermined matching modes according to the data block index table, obtaining the data block identifier upon determining the target experimental data in a storage node is loaded, and extracting data content in the target experimental data corresponding to the data block identifier by the target matching mode. This method may greatly reduce the number of string matching and may reduce the complexity of the algorithm.

Glyph accessibility techniques are described as implemented by a digital content processing system involving accessing glyphs and glyph alternatives. These techniques include preprocessing techniques in which a base font is used to determine similarity of glyphs within the base font to each other. Glyph metadata that describes this similarity is cached in a storage device and used during runtime to increase efficiency in locating similar glyphs in other fonts.

Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

Systems and methods are disclosed for processing events having raw machine data associated with a timestamp using one or more pivot identifiers and one or more step identifiers to generate one or more journey instances. Based on the one or more pivot identifier field, the system can relate events that have a common field value for the pivot identifier field. Based on the one or more step identifiers, the system can group the related events into a subset of events. Using the subset of events, the system can build a journey instance.

Systems and methods are disclosed for processing events having raw machine data associated with a timestamp using one or more pivot identifiers and one or more step identifiers to generate one or more journey instances. Based on the one or more pivot identifier field, the system can relate events that have a common field value for the pivot identifier field. Based on the one or more step identifiers, the system can group the related events into a subset of events. Using the subset of events, the system can build a journey instance.

Methods and systems for data are disclosed. A system implementation includes a data module for storing data received from an external source. The data module includes a file system for unstructured data, a database for structured data, a transform for operating upon unstructured or structured data, a data broker for receiving data having a first format and providing the data in a second format, a data network for communications within the data module, and a processing module for performing operations upon data. The processing module further includes a process broker and a process container. The process container is for providing one or more instances of processes during a runtime operation. The system further includes an inter-process network for communications within the processing module and an internal gateway for the data module to communicate with the processing module.

The present disclosure relates generally to Lightweight Directory Access Protocol (LDAP), and more particularly, to techniques for improving query performance on an LDAP server. One particular technique includes receiving a LDAP query having search criteria, identifying one or more search filters within the search criteria, determining candidate indices based on the identified one or more search filters, evaluating the candidate indices based on statistics collected for the candidate indices, selecting one or more indices from the candidate indices based on the evaluating, and executing the LDAP query on an LDAP directory using the selected one or more indices.

Techniques for feature engineering pipeline generation for machine learning using decoupled dataset analysis and interpretation are described. A feature engineering engine obtains a dataset and utilizes a number of analyzers to generate data facts associated with the columnar values of the dataset. The data facts are consolidated together as a set of data statements that are used by multiple interpretation engines that implement different strategies for treating the data in order to generate feature engineering pipeline code.