Abstract: A processor may execute an asynchronous operation of the program code, hibernate a process related to the asynchronous operation, and free-up related cloud runtime platform excluding the related system memory. Additionally, the processor may execute the asynchronous operation during the hibernation of the process, intercept an initiated completion function to the process after a completion of the asynchronous operation, inject at least one of additional program code and data into the completion function, un-hibernating the process and reallocate freed-up cloud runtime platform related resources of the process, and execute the completion function returning result data of the asynchronous operation and the at least one of additional program code and data to the process.

Abstract: The present disclosure relates to a method for processing a data table. The data table is divided into a plurality of data blocks, and a multidimensional data structure for representing the plurality of data blocks is generated. For each data block of at least part of the plurality of data blocks, a respective data element is added into the multidimensional data structure. The data element has multiple values indicative of a first attribute of the data block. The multidimensional data structure is stored for selectively processing a portion of the data blocks using the multidimensional data structure.

Abstract: The present disclosure provides a computer implemented method and system for processing queries. The first data table comprises a set of data blocks. Each of the set of data blocks may be assigned respective attribute value information. A query involving a query condition on at least a first attribute of the first data table may be received. And a subset of the set of data blocks to be accessed may be selected based on the query condition and using the attribute value information. Furthermore, a guaranteed bound may be determined for a statistical metric on the first attribute based on at least one of the number of data blocks of the subset of data blocks and the attribute value information of the subset of data blocks. The guaranteed bound for the statistical metric may be used when determining a query execution plan for the received query.

Abstract: The present disclosure provides a computer implemented method and system for processing queries. The first data table comprises a set of data blocks. Each of the set of data blocks may be assigned respective attribute value information. A query involving a query condition on at least a first attribute of the first data table may be received. And a subset of the set of data blocks to be accessed may be selected based on the query condition and using the attribute value information. Furthermore, a guaranteed bound may be determined for a statistical metric on the first attribute based on at least one of the number of data blocks of the subset of data blocks and the attribute value information of the subset of data blocks. The guaranteed bound for the statistical metric may be used when determining a query execution plan for the received query.

Abstract: The present disclosure provides a computer implemented method and system for processing queries. The first data table comprises a set of data blocks. Each of the set of data blocks may be assigned respective attribute value information. A query involving a query condition on at least a first attribute of the first data table may be received. And a subset of the set of data blocks to be accessed may be selected based on the query condition and using the attribute value information. Furthermore, a guaranteed bound may be determined for a statistical metric on the first attribute based on at least one of the number of data blocks of the subset of data blocks and the attribute value information of the subset of data blocks. The guaranteed bound for the statistical metric may be used when determining a query execution plan for the received query.

Abstract: The present disclosure provides a computer implemented method and system for processing queries. The first data table comprises a set of data blocks. Each of the set of data blocks may be assigned respective attribute value information. A query involving a query condition on at least a first attribute of the first data table may be received. And a subset of the set of data blocks to be accessed may be selected based on the query condition and using the attribute value information. Furthermore, a guaranteed bound may be determined for a statistical metric on the first attribute based on at least one of the number of data blocks of the subset of data blocks and the attribute value information of the subset of data blocks. The guaranteed bound for the statistical metric may be used when determining a query execution plan for the received query.

Abstract: The present disclosure relates to a method for processing a data table. The data table is divided into a plurality of data blocks, and a multidimensional data structure for representing the plurality of data blocks is generated. For each data block of at least part of the plurality of data blocks, a respective data element is added into the multidimensional data structure. The data element has multiple values indicative of a first attribute of the data block. The multidimensional data structure is stored for selectively processing a portion of the data blocks using the multidimensional data structure.

Abstract: Determining an extremum value of an attribute of a first data table. The first data table includes a set of data blocks. Assigning each data block of the set of data blocks an attribute value information indicative of a range of values of the attribute. Assigning the attribute value information results in an extremum set of indicative maximum and minimum values of the attribute. Determining a reference value as the infimum or supremum of the extremum set. If the extremum is a minimum, selecting a subset of the set of data blocks, each having a respective minimum value of the attribute smaller than the reference value. If the extremum is a maximum, selecting a subset of the set of data blocks, each having a respective maximum value of the attribute higher than the reference value. Processing at least part of the subset of data blocks for identifying the extremum value.

Abstract: The invention is based on Orders specifically developed for and processed by an Order Processing Environment for creation or modification of resource topologies. The Order Processing Environment is partly replaced by a combination of an Order Transformation Environment and standard Workflow Engines in order to execute the Order by standard Workflow Engines. The Order Transformation Environment needs to get two inputs. The first input is the resource topology which is retrieved by using the Relationship Registry of the Order Processing Environment. The second input is the Order. Orders are resource topology independent and include resource specific tasks without arranging those in a sequence. Tasks provide actions for creating and/or modifying resource topologies. The transformation is based on above two inputs resulting in a static standard based workflow. The static, standards-based workflow (e.g. BPEL-based) can then be executed by standards-based process/workflow engines.

Abstract: The invention is based on Orders specifically developed for and processed by an Order Processing Environment for creation or modification of resource topologies. The Order Processing Environment is partly replaced by a combination of an Order Transformation Environment and standard Workflow Engines in order to execute the Order by standard Workflow Engines. The Order Transformation Environment needs to get two inputs. The first input is the resource topology which is retrieved by using the Relationship Registry of the Order Processing Environment. The second input is the Order. Orders are resource topology independent and include resource specific tasks without arranging those in a sequence. Tasks provide actions for creating and/or modifying resource topologies. The transformation is based on above two inputs resulting in a static standard based workflow. The static, standards-based workflow (e.g. BPEL-based) can then be executed by standards-based process/workflow engines.