The current document is directed to a query-as-a-service system (“QAAS system”) that collects enormous volumes of data from network-connected entities, referred to as “Things” in the phrase “Internet of Things,” persistently stores the collected data and provides a distributed-query-execution engine that allows remote clients to continuously execute queries against the collected data. In a described implementation, both the raw data and query results are persistently stored in the QAAS system, with the raw data stored for significantly longer periods of time. Query results generated by the query-processing engine are securely transmitted to QAAS remote clients for distribution to file systems, storage appliances, applications, and other data sinks within client systems.

The present approach assigns a code to each node class of a data tree modeling a database. The node class codes may be used to generate a node class path for each node class. This class path may be used as a discriminator to reference a given node class or portion of the tree including the class path and may be stored in a field of the database and/or cached. Use of the class path in query operations reduces the complexity of certain queries, thereby speeding up query performance.

Disclosed are systems, methods and computer program products for performing data backup using an unmanned aerial vehicle (UAV). An example method includes in response to detecting a data backup request from a user device, determining a geographic location of the user device and dispatching the UAV to the geographic location; controlling the UAV to obtain user data from the user device; and controlling the UAV to navigate to a data center to back up the obtained user data onto a cloud storage.

A shared database platform can interface with a cluster computing platform over a network through a connector. The data transferred over the network can include metadata result packages that can be distributed to worker nodes of the cluster computing platform, which receive the metadata objects and access the result data for further processing on a staging platform, such as a scalable storage platform.

Techniques for improving data compression of a storage system in an online manner are described herein. According to one embodiment, in response to a sequence of data to be stored, the sequence of data is partitioned into a plurality of data chunks according to a predetermined chunking algorithm. A sketch for each of the data chunks is generated based on one or more features extracted from the data chunk. Each of the data chunks of the sequence of data is associated with one of a plurality of groups based on the sketch, wherein each group is represented by a sketch. The data chunks of each group are compressed and stored in a compression region of the storage systems, such that similar data chunks are compressed and stored in the same compression region.

Techniques herein generate a query plan that combines a global reporting aggregate calculation and an organizing operation. A method detects an organizing operation, a group aggregate function, and a global aggregate function within a database statement. The organizing operation specifies organizational activities such as grouping, joining, or sorting rows. The method generates an execution plan that specifies calculating all values in a single pass. For each row, the single pass applies the organizing operation and updates an access structure. The pass updates one of multiple cumulative group calculations based on the group aggregate function and updates a cumulative global calculation based on the global aggregate function. Each cumulative group calculation is associated with some of the access structure. Based on the access structure, result rows that satisfy the database statement are generated. Result rows contain a final result of each group calculation and a final result of the global calculation.

A database system supporting persistent queries, using an enhanced persistent query service and various data sources. On receiving a request to create a persistent query from a client software application, the persistent query service: creates a query virtual table; parses the persistent query; creates a plurality of intermediate virtual tables; establishes listeners for the query virtual table; creates a plurality of data source virtual tables; causes the plurality of data source virtual tables to retrieve initial data from data sources; and propagates data via intermediate virtual tables to the persistent query virtual table.

An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

A computer may receive a request to generate a snapshot view of a virtual infrastructure. The virtual infrastructure may comprise a plurality of virtual server management applications, each managing a respective set of virtual machines. The computer may implement a multi-threaded process to contemporaneously query one or more databases and retrieve status and other information of the virtual machines from different virtual server management applications. The computer may aggregate the retrieved information to determine the summary counters and statistic information for the virtual machines. The computer may generate a snapshot view file based on the retrieved information. The snapshot view file may be in hypertext markup language (HTML) format. The computer may transmit a selectable link to the snapshot view file to multiple user devices. A user may select the link and the respective user device may display the snapshot view in an application such as a web browser.

Efficient aggregation of time series data is disclosed, including: obtaining a first entry value corresponding to an item, wherein the first entry value comprises a first recorded data point that is associated with a first time interval; generating a compressed block based at least in part on compressing the first entry value with at least a second entry value; storing the compressed block in a document corresponding to the item; determining that the item matches an aggregation search query; decompressing the compressed block from the document corresponding to the item to obtain the first entry value and the second entry value; and generating an aggregation result in response to the aggregation search query based on at least a portion of the first entry value and the second entry value.