Patent classifications
G06F16/2477
SYSTEMS AND METHODS FOR MATCHING ELECTRONIC ACTIVITIES WITH RECORD OBJECTS BASED ON ENTITY RELATIONSHIPS
The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.
Systems, apparatuses, and methods for request throttling
Techniques for request throttling in a provider network environment are described. A throttle handler controls whether requests will be processed through maintaining a token-based record, per type of request, having a token value indicative of a number of requests that can be processed over a time period. For a request, the token value of the token-based record corresponding to the request type is updated based on calculating an elapsed time between a last update time of the token-based record and the current time, calculating an intermediate token value as the existing token value plus a value of the elapsed time multiplied by a rate, and updating the token value to be the minimum between the intermediate token value and a burst value. The request is serviced when the updated token value is determined to be greater than or equal to a number of tokens needed to perform the request.
Data query method and apparatus
This disclosure provides a data query method and apparatus. The method includes: dividing an object storage system into a plurality of storage spaces based on time slices, establishing a mapping relationship between the storage spaces, the time slices, and operation records, recording time slice information corresponding to a snapshot after generating the snapshot, and implementing services such as a read-only service, a snapshot rollback service, and an object change service based on the snapshot, so as to improve query performance without adding extra storage overheads.
Past-state backup generator and interface for database systems
An interface for requesting, and technique for generation of, a backup of a past state of a database table are provided. Changes made to a database table are accumulated, in durable storage, and snapshots of partitions of the table are obtained. The accumulated changes and the successive partition snapshots are used to generate a past state of the database at any point in time across a continuum between successive snapshots. Although each partition of the table may have a snapshot that was generated at a time different from when other partition snapshots were generated, changes from respective change logs may be selectively log-applied to distinct partitions of a table to generate backup in the past of the entire table at common point-in-time across partitions.
Information system with temporal data
A method for accessing information. The information is received by a computer system from sources for distribution to client computer systems. A piece of the information received from the sources without temporal data is identified by the computer system. The temporal data for the piece of the information based on a policy is identified by the computer system. The temporal data is associated with the piece of the information by the computer system, enabling analyzing the information by a client computer system with increased accuracy.
SYSTEMS AND METHOD FOR PROCESSING TIMESERIES DATA
In some implementations, events measured at various points in time may be organized in a data structure that defines an event represented by a document. In particular, events can be organized in columns of documents referred to as buckets. These buckets may be indexed using B-trees by addressing metadata values or value ranges. Buckets may be defined by periods of time. Documents may also be geoindexed and stored in one or more locations in a distributed computer network. One or more secondary indexes may be created based on time and/or metadata values within documents.
Generating search commands based on cell selection within data tables
A search interface is displayed in a table format that includes one or more columns, each column including data items of an event attribute, the data items being of a set of events, and a plurality of rows forming cells with the one or more columns, each cell including one or more of the data items of the event attribute of a corresponding column. Based on a user selecting one or more of the cells, a list of options if displayed corresponding to the selection, and one or more commands are added to a search query that corresponds to the set of events, the one or more commands being based on at least an option that is selected from the list of options and the event attribute for each of the one or more of the data items of each of the selected one or more cells.
Guided workflows for machine learning-based data analyses
Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.
Server-side operations for edge analytics
Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.
Updating high definition maps based on age of maps
A computer-implemented method may include monitoring an age of a tile of a map, where the map includes multiple tiles including the tile. The method may also include, based on the age exceeding a threshold age, determining that the tile of the map is to be updated, and receiving a location indicator from a vehicle. The method may additionally include transmitting an update message to a vehicle traversing a track within the tile as indicated by the location indicator, where the update message includes instructions to cause the vehicle to gather and submit sensor data to a computing system. The method may also include receiving the sensor data from the vehicle, and updating the tile of the map based on the received sensor data.