Certain implementations of the disclosed technology may include methods and computing systems for performing high-density data compression, particularly on numerical data that demonstrates various patterns, and patterns of patters. According to an example implementation, a method is provided. The method may include extracting a data sample from a data set, compressing the data sample using a first compression filter configuration, and calculating a compression ratio associated with the first compression filter configuration. The method may also include compressing the data sample using a second compression filter configuration and calculating a compression ratio associated with the second compression filter configuration. A particular compression filter configuration to utilize in compressing the entire data set may be selected based on a comparison of the compression ratio associated with the first compression filter configuration and a compression ratio associated with the second compression filter configuration.

Provided are methods comprising receiving a query for information from a database, determining particular data element types and data element values that are the subject of the query, instantiating a query data structure containing the data element types and the data element values that are the subject of the query, identifying records within the database that contain one or more data element types and/or data element values that are included in the query data structure, and instantiating a results data structure comprising information relating to the identified records.

Provided are methods comprising receiving a query for information from a database, determining particular data element types and data element values that are the subject of the query, instantiating a query data structure containing the data element types and the data element values that are the subject of the query, identifying records within the database that contain one or more data element types and/or data element values that are included in the query data structure, and instantiating a results data structure comprising information relating to the identified records.

Methods, apparatus, systems and articles of manufacture are disclosed for scalable architectures for reference signature matching and updating. An example method for scalable architectures for reference signature matching and updating includes accessing site signatures to be compared to reference signatures from a first group of media sources. Determining if a first reference node is an owner of a first one of the site signatures. Comparing a neighborhood of site signatures including the first site signature to reference signatures in a first subset of reference signatures when the first reference node is the owner of the first site signature, the first subset of references signatures stored in a first memory partition associated with the first reference node. Not comparing site signature to reference signatures when the first reference node is not the owner of the first one of the site signatures.

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user is computed using the neural network based on input obtained from the user (402).

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user is computed using the neural network based on input obtained from the user (402).

A packet processing technique can include receiving a packet, and parsing the packet based on a protocol field to generate a parse result vector. The parse result vector is used to select between forwarding the packet to a virtual machine executing on a host processing integrated circuit, forwarding the packet to a physical media access controller, multicasting the packet to multiple virtual machines executing on the host processing integrated circuit, and sending the packet to a hypervisor.

A packet processing technique can include receiving a packet, and parsing the packet based on a protocol field to generate a parse result vector. The parse result vector is used to select between forwarding the packet to a virtual machine executing on a host processing integrated circuit, forwarding the packet to a physical media access controller, multicasting the packet to multiple virtual machines executing on the host processing integrated circuit, and sending the packet to a hypervisor.

A data processing method, apparatus, and system, a computer device, a readable storage medium, and a computer program product relate to the field of cloud technologies and a blockchain technology, and the method includes: receiving, by using a transceiver component, collection indicator data sent by an edge cluster; performing pre-aggregation processing on the collection indicator data to obtain pre-aggregated indicator data, and sending the pre-aggregated indicator data to a coordinated write component; converting, by the coordinated write component, the pre-aggregated indicator data into conversion indicator data that has a target storage format, and performing merging processing on the conversion indicator data to obtain storage indicator data; writing the storage indicator data into a database component; and writing, by the database component, the storage indicator data into a storage disk.

A data processing method, apparatus, and system, a computer device, a readable storage medium, and a computer program product relate to the field of cloud technologies and a blockchain technology, and the method includes: receiving, by using a transceiver component, collection indicator data sent by an edge cluster; performing pre-aggregation processing on the collection indicator data to obtain pre-aggregated indicator data, and sending the pre-aggregated indicator data to a coordinated write component; converting, by the coordinated write component, the pre-aggregated indicator data into conversion indicator data that has a target storage format, and performing merging processing on the conversion indicator data to obtain storage indicator data; writing the storage indicator data into a database component; and writing, by the database component, the storage indicator data into a storage disk.