The invention relates to a creating a stored procedure that retrieves its queries from a database, e.g., a DB2 table, and then prepares and executes dynamic SQL based on parameters supplied by a calling program. According to an embodiment of the present invention, a computer implemented system implements a dynamic stored procedure tool and comprises: a memory interface that accesses a database table; an interactive interface that receives a user selection; a processor, coupled to the memory interface and the interactive interface, the processor configured to perform the steps comprising: retrieve input values from a calling program; retrieve a cursor from an external source, using the input values; determine whether one or more parameter substitutions are applicable; and execute the cursor dynamically.

A computer system includes a processor and a database. The database includes a plurality of business intelligence (BI) data objects. Each of the BI data objects is associated with one or more data parameters. The processor is programmed with computer-executable instructions that cause the processor to run user code associated with a runtime environment that is hosted on the database. The user code includes executable source code that is not an intrinsic part of the database. The user code is created and deployed into the database and is configured to intercept a data call from a query application to a query interface for data corresponding to one or more of the BI data objects. The data call includes one or more selected parameters. The data call is parsed to ascertain the one or more selected parameters. Data corresponding to the one or more BI data objects is obtained from the database based on the one or more selected parameters. A data prediction result is appended to the obtained data.

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.

A process for recommending pre-constructed queries in data analytics includes writing different records to a correlation data structure correlating different data classifications of data to different queries and, subsequent to the writing, establishing a communicative connection by a data analytics application to an underlying database. Thereafter, a data model for data in the database may be constructed in the data analytics application and at least one of the different queries may be selected in the correlation data structure that correlates to the classification of the data in the data model. Finally, the selected one of the different queries may be displayed in the data analytics application to an end user so as to provide an intelligent recommendation for the addition of the selected one of the different queries without requiring the end user to alone and without assistance discover the suitability of the selected one of the different queries.

Implementations of the present specification provide a method for deploying a smart contract. According to one implementation the method includes: receiving a transaction request for invoking a first contract; obtaining a first instruction code and a function index table, wherein the function index table is used to indicate a memory address of an instruction code corresponding to each of import and export functions in the first contract; determining a first memory address corresponding to the invocation function based on the function index table; and executing the first instruction code in the first memory address based on the determined first memory address.

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.

An improvement to a database management system including receiving a data and creating a record key for the data and modifying the record key by hashing it with a predefined modifier and storing the data with the modified record key. Storing the data with an altered record ID obfuscates the data without an encryption step. In some embodiments hashing includes adding or subtracting a predetermined number from the record key. The record key may be created by combining a user key and a private key. To retrieve data, the method provides for receiving a record request including the public key and hashing the public key with the private key to determine a record identifier and querying the database to return the proper data. These methods may be incorporated into database operations providing a secure database without the resource overhead of encryption.

A server in a cloud-based environment interfaces with storage devices that store shared content accessible by two or more users. Individual items within the shared content are associated with respective object metadata that is also stored in the cloud-based environment. Download requests initiate downloads of instances of a virtual file system module to two or more user devices associated with two or more users. The downloaded virtual file system modules capture local metadata that pertains to local object operations directed by the users over the shared content. Changed object metadata attributes are delivered to the server and to other user devices that are accessing the shared content. Peer-to-peer connections can be established between the two or more user devices. Object can be divided into smaller portions such that processing the individual smaller portions of a larger object reduces the likelihood of a conflict between user operations over the shared content.

Systems for fast views of items in file directories or file folders when interacting with a cloud-based service platform. A server in a cloud-based environment interfaces with one or more storage devices to provide storage of shared content accessible by two or more user devices. A file tree request to view the file directory or file folder of a particular sought after item is issued from an application operating on one of the user devices. Additional file tree items in a file tree hierarchy are prefetched by the cloud-based service platform. The application closes the file tree metadata stream after receiving the portion of the file tree that pertains to the particular item and before receiving the entirety of the metadata pertaining to all of the file tree metadata of all of the items in the directory or folder that contains the particular sought after item.

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.