Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: A method of assembling a connector for automotive applications, comprising the steps of: providing a cable having at least one inner conductor; connecting an elongated inner signal contact of the connector to a stripped end of the at least one inner conductor; surrounding the elongated inner signal contact by an insulating element; placing a first shielding part of the connector around a first portion of the insulating element from a first radial direction; placing a second shielding part of the connector around a second portion of the insulating element from a second radial direction generally opposite to the first radial direction; and joining the first and second shielding parts to form a shielding contact of the connector surrounding the insulating element.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: A method of assembling a connector for automotive applications, comprising the steps of: providing a cable having at least one inner conductor; connecting an elongated inner signal contact of the connector to a stripped end of the at least one inner conductor; surrounding the elongated inner signal contact by an insulating element; placing a first shielding part of the connector around a first portion of the insulating element from a first radial direction; placing a second shielding part of the connector around a second portion of the insulating element from a second radial direction generally opposite to the first radial direction; and joining the first and second shielding parts to form a shielding contact of the connector surrounding the insulating element.

Abstract: Methods and systems are presented for providing concurrent data retrieval and risk processing while evaluating a risk source of an online service provider. Upon receiving a request to evaluate the risk source, a risk analysis module may initiate one or more risk evaluation sub-processes to evaluate the risk source. Each risk evaluation sub-process may require different data related to the risk source to perform the evaluation. The risk analysis module may simultaneously retrieve the data related to the risk source and perform the one or more risk evaluation sub-processes such that the risk analysis module may complete a risk evaluation sub-process whenever the data required by the risk evaluation sub-process is made available.

Abstract: A first graph that includes a plurality of containers is accessed. The containers each contain one or more rules that each have corresponding computer code. The containers are configured for sequential execution by a rule engine. The computer code corresponding to the one or more rules in each of the containers is electronically scanned. Based on the electronic scan, an interdependency among the rules is determined. Based on the determined interdependency, a second graph is generated. The second graph includes all of the rules of the containers, but not the containers themselves. At least some of the rules are configured for parallel execution by the rule engine.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Abstract: Methods and systems for automatically discovering data types required by a computer-based rule engine for evaluating a transaction request are presented. Multiple potential paths for evaluating the transaction request according to the rule engine are determined. An abstract syntax tree may be generated based on the rule engine to determine the multiple potential paths. Based on an initial set of data extracted from the transaction request, one or more potential paths that are determined to be irrelevant to evaluating the transaction request are identified. Types of data required to evaluate the transaction request according to the remaining potential paths are determined. Only data that corresponds to the determined types of data is retrieved to evaluate the transaction request.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Abstract: A first graph that includes a plurality of containers is accessed. The containers each contain one or more rules that each have corresponding computer code. The containers are configured for sequential execution by a rule engine. The computer code corresponding to the one or more rules in each of the containers is electronically scanned. Based on the electronic scan, an interdependency among the rules is determined. Based on the determined interdependency, a second graph is generated. The second graph includes all of the rules of the containers, but not the containers themselves. At least some of the rules are configured for parallel execution by the rule engine.

Abstract: Methods and systems are presented for providing concurrent data retrieval and risk processing while evaluating a risk source of an online service provider. Upon receiving a request to evaluate the risk source, a risk analysis module may initiate one or more risk evaluation sub-processes to evaluate the risk source. Each risk evaluation sub-process may require different data related to the risk source to perform the evaluation. The risk analysis module may simultaneously retrieve the data related to the risk source and perform the one or more risk evaluation sub-processes such that the risk analysis module may complete a risk evaluation sub-process whenever the data required by the risk evaluation sub-process is made available.

Abstract: Methods and systems are presented for providing concurrent data retrieval and risk processing while evaluating a risk source of an online service provider. Upon receiving a request to evaluate the risk source, a risk analysis module may initiate one or more risk evaluation sub-processes to evaluate the risk source. Each risk evaluation sub-process may require different data related to the risk source to perform the evaluation. The risk analysis module may simultaneously retrieve the data related to the risk source and perform the one or more risk evaluation sub-processes such that the risk analysis module may complete a risk evaluation sub-process whenever the data required by the risk evaluation sub-process is made available.

Abstract: A method of assembling a connector for automotive applications, comprising the steps of: providing a cable having at least one inner conductor; connecting an elongated inner signal contact of the connector to a stripped end of the at least one inner conductor; surrounding the elongated inner signal contact by an insulating element; placing a first shielding part of the connector around a first portion of the insulating element from a first radial direction; placing a second shielding part of the connector around a second portion of the insulating element from a second radial direction generally opposite to the first radial direction; and joining the first and second shielding parts to form a shielding contact of the connector surrounding the insulating element.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Abstract: Methods and systems are presented for providing concurrent data retrieval and risk processing while evaluating a risk source of an online service provider. Upon receiving a request to evaluate the risk source, a risk analysis module may initiate one or more risk evaluation sub-processes to evaluate the risk source. Each risk evaluation sub-process may require different data related to the risk source to perform the evaluation. The risk analysis module may simultaneously retrieve the data related to the risk source and perform the one or more risk evaluation sub-processes such that the risk analysis module may complete a risk evaluation sub-process whenever the data required by the risk evaluation sub-process is made available.

Abstract: An electrical shielding member for a network connector includes a receiving portion for receiving a cable end of a shielded cable and a contact beam extending from the receiving portion. The contact beam has a first contact point for electrically connecting the electrical shielding member to a counter shielding member of a counter network connector, and a coupling portion, provided at a distal end of the contact beam. The coupling portion is configured to be coupled to a corresponding coupling portion of a network connector housing. The network connector is configured for network communication at data rates of at least 100 megabits per second (Mbit/s) and preferably at least 1 gigabits per second (Gbit/s).