A Sort Lists instruction is provided to perform a sort and/or a merge operation. The instruction is an architected machine instruction of an instruction set architecture and is executed by a general-purpose processor of the computing environment. The executing includes sorting a plurality of input lists to obtain one or more sorted output lists, which are output.

A transformation service is provided for a plurality of transformation jobs associated with one or more storage containers. A single transformation user runs enumeration tasks associated with data stored in the one or more storage containers. When two or more of the plurality of transformation jobs are active during a time interval, the single transformation user runs an enumeration task for all of the active transformation jobs during said time interval.

Implementations of the present specification provide a method for deploying a smart contract. According to the method in the implementations, in a phase of deploying a smart contract, a bytecode included in a contract module corresponding to the contract is obtained; and then the bytecode is parsed into executable instruction codes, and the executable instruction codes are stored in a cache memory. Further, a function index table is determined for import and export functions in the bytecode, where the function index table is used to indicate a memory address of an instruction code corresponding to each of the import and export functions; and the function index table is stored in the cache memory.

The invention relates to applying a query tag in an object relational mapping tool. According to an embodiment of the present invention, a system comprises: a Repository API; a relational database management system (RDBMS); an object relational mapping (ORM) Tool that receives input from the Repository API and translates the input to the RDBMS; and a processor, coupled to the Repository API, the ORM Tool and the RDMBS, configured to perform the steps comprising: determine, at the Repository API, a query tag that identifies an origination source code; propagate the query tag to a database connection context within the ORM Tool; and inject the query tag into a generated SQL command.

Systems and methods for extending reasoning capability for data analytics in Internet of Things (IoT) platform(s) are provided. Traditional systems and methods for executing IoT analytics tasks suffer as IoT analytics techniques are generated in different programming language platforms, and this leads to a manual intervention or an asynchronous and sequential analysis of IoT analytics task(s). Embodiments of the method disclosed provide for overcoming the limitations faced by the traditional systems and methods by dynamically creating procedural functions from a plurality of programming languages upon determining an absence of pre-defined procedural functions, and extracting, using the dynamically created procedural functions, one or more semantic rules in a real-time, wherein the one or more semantic rules extend a reasoning capability for executing the one or more data analytics tasks in a plurality of IoT platforms.

Techniques are provided for selectively or completely redacting the text of database commands submitted to a database system. The database server receives the clear text version of the commands, parses the commands, and generates an execution plan, as normal. However, prior to providing the text of the commands to any location that is externally visible, the database server determines whether the command qualifies as sensitive. If the command qualifies as sensitive, then a redacted version of the command is generated. In the case of selective redaction, portions of the redacted version remain in clear text, while selected portions are replaced with encrypted text. In the case of total redaction, the entire command is replaced with encrypted text.

Implementations of the present specification provide a method for deploying a smart contract. According to the method in the implementations, in a phase of deploying a smart contract, a bytecode included in a contract module corresponding to the contract is obtained; and then the bytecode is parsed into executable instruction codes, and the executable instruction codes are stored in a cache memory. Further, a function index table is determined for import and export functions in the bytecode, where the function index table is used to indicate a memory address of an instruction code corresponding to each of the import and export functions; and the function index table is stored in the cache memory.

Disclosed is a data exploration management method, including: acquiring data input by a user, where the data includes data content and an exploration variable; acquiring a pre-stored flow selected by the user, where the pre-stored flow is used to perform data exploration on the data; acquiring an operation, a method, and flow program code of the pre-stored flow, and generating and storing output program code; running the output program code, and acquiring and storing a running result; and displaying the pre-stored flow, the output program code, and the running result. According to the foregoing method, scientific management on a data exploration process ensures that the data exploration process is repeatable, and that an operation and a method of the data exploration process can be invoked, shared, and reused in real time.

A method and a system for dynamically scanning, filtering, and blocking harmful database queries that would otherwise consume significant resources and adversely impact overall system performance are provided. The method includes: receiving a user request for data from a database, the first request including a query; applying database access rules to the query in order to determine whether the query is potentially harmful; when the query is determined as not potentially harmful, forwarding the request to a server configured to respond to the request; and when the first query is determined as being potentially harmful, transmitting a warning message to the user. The database access rules may include a maximum memory consumption rule and a maximum CPU consumption rule. Machine learning techniques are used for adjusting the database access rules based on historical data.

A Sort Lists instruction is provided to perform a sort and/or a merge operation. The instruction is an architected machine instruction of an instruction set architecture and is executed by a general-purpose processor of the computing environment. The executing includes sorting a plurality of input lists to obtain one or more sorted output lists, which are output.