A method, system and computer program product for processing metrics data with graph data context analysis. Graph data representing one or more devices or sensors is stored into a first database, and metrics data generated by the devices or sensors is stored in a second database. The metrics data is then applied to the graph data for the context analysis, wherein the context analysis reflects the relationships of the devices or sensors in the graph data to the metrics data generated by the devices or sensors. The graph data comprises nodes for representing the devices or sensors, edges for representing a topology of the devices or sensors, and properties for storing the metrics data associated with the nodes and edges; and the metrics data comprises time-series data that is logged by the devices and sensors.

Techniques for creating a custom metric type to be added to a set of metrics generated by a data monitoring platform at run-time are disclosed. A system receives values defining properties of a custom metric type based on a custom metric template and a custom schema template. The system generates an instruction set, based on the values associated with the custom metric template, for generating the custom metric type on an executing data monitoring system. The system validates the instruction set and the custom schema to verify that the definitions for the custom metric type and the custom schema may be executed by the data monitoring system. The system adds the custom metric type, at run-time, to a set of metrics generated by the data monitoring system.

In non-limiting examples of the present disclosure, systems, methods and devices for identifying a target value in a data set are presented. A request for a first target value in a data array may be received, wherein the request comprises first reference value. The data array may comprise a plurality of values and a plurality of value types included in one or more tables in a computer-implemented spreadsheet application. An index comprising a plurality of sorted reference numbers corresponding to a plurality of values in the data array may be generated based on the request. The index may be searched, and based at least on the search, the first reference value may be identified. In additional examples, the index may be cached and utilized to process additional target value requests that reference an overlapping range value with the first request.

System and methods providing for categorizing individual virtual machines, as well as the associated application that they form by working in concert, into groups based on the feasibility of hosting the processes that occur on a virtual machine within a container, as well as the relative difficulty of doing so on a virtual machine and application level. The data used to create these scores is collected from the individual machines, at regular intervals through the use of an automated scoring engine that collects and aggregates the data. Said data is then analyzed by the system, that with the aid of passed in configuration data, is configured to generate the scores to allows for an educated and focused effort to migrate from hosting applications on virtual machines to hosting applications on containers.

A system receives a request to import data file(s) from a source data store into a target database. The system reserves a first portion of computing resources that host the target database to import the data file(s). The reservation of the first portion of computing resources permits the import throughput rate of the data file(s) through the first portion of computing resources while maintaining a second portion of the computing resources to support client access to the target database at an access throughput rate. The system initiates import of the data file(s) from the source data store to the target database through one or more storage nodes at the import throughput rate according to the first portion of computing resources. The target database is able to receive access requests from one or more clients during the import of the data file(s) to the target database.

The system provides a method and apparatus for sorting and displaying information from a BBS. The system provides a method of sorting and presenting messages from a BBS in a way so that the relationship in message threads can be easily observed and related messages can be identified. The system provides a way to view messages and map message threads in two and three dimensions so that the content of messages can be easily reviewed and the relationship between messages can be seen and followed. The system allows a user to enter into a message thread at any point and to then produce a visualization of the related threads and messages associated with each individual message. The system provides interfaces for either a linear or threaded BBS or even a hybrid BBS that is some combination of linear and threaded.

A method for database syncing is provided. The method includes receiving database change indicators indicating changes to a database. For each database change indicator, the method also includes determining whether the respective change to the database corresponds to a single change to a single version of the database, one of multiple changes to a common version of the database, or one of multiple changes to different versions of the database. The method further includes generating a change log compaction data structure comprising entries. Each entry includes an exact entry defining a corresponding single change to a corresponding single version of the database, a range entry defining corresponding multiple changes to a corresponding common version of the database, or an interval entry defining corresponding multiple changes to corresponding different versions of the database.

A data processing method, apparatus, and system, where the method includes: receiving a data insertion request sent by service software, where the data insertion request includes a data record, and the data record includes an object type identifier, a field value, a field identifier, and an object instance identifier; generating a field index based on the object type identifier, the field value, and the object instance identifier; generating a relationship index based on the object type identifier; writing the data record to a database; and caching an index record and an operation for the index record into a cache apparatus, where the index record includes the relationship index and the field index. Using the foregoing solution, high-performance data processing of a flat-wide table is implemented.

The present invention discloses a gene sequencing data compression preprocessing, compression and decompression method, a system, and a computer-readable medium. The preprocessing method implementation steps include: obtaining reference genome data; obtaining a mapping relationship between a short string K-mer and a prediction character c to obtain a prediction data model P1 containing any short string K-mer in the positive strand and negative strand of a reference genome and the prediction character c in a corresponding adjacent bit. The compression and decompression methods relate to performing compression/decompression on the basis of the prediction data model P1. The system is a computer system including a program for executing the previous method. The computer-readable medium includes a computer program for executing the previous method. The present invention can be oriented towards lossless gene sequencing data compression, provides fully effective information for a high-performance lossless compression and decompression algorithm for gene sequencing data.

As described herein, a N-Gram index may be created and the search may be conducted using the index, which will lead to faster search results. The N-Gram index may also include partial N-Gram components to capture more relevant data. Moreover, as described herein, the search may also take into account recent log data that has not yet been indexed. Techniques for building an index store using log data and efficiently searching the index store and log data to process search requests are described herein.