Abstract: Techniques for routing items addressed to an unstructured address are described. One embodiment includes receiving an order for delivery of a first package, the order specifying a first address that does not comply with a defined address format. The first address is processed using one or more hybrid machine learning algorithms to determine a Normalized Delivery Location (NDL) associated with the first address. A sorting zone that encompasses the NDL is determined. The sorting zones correspond to a predefined geographic region. Embodiments facilitate transport of the first package to a physical shipping location within the predefined geographic region.

Abstract: Techniques for determining a Next Best Action (NBA) are disclosed, with the determination being based on a position within an application, past actions by a user when experiencing a similar application context, and/or tasks in queue for the user to execute from current state of the application. Techniques are also disclosed for displaying an interface that includes the NBA in conjunction with a specific rationale for presenting the NBA, such as recommendations by a trusted person, based on the user's and/or other users' past behavior, and descriptions developed based on the specific NBA. Also, methods for determining a best NBA are disclosed, with the NBA being selected by applying static rules to a data set, heuristically analyzing the data set, and/or applying a machine learning model to the data set.

Abstract: A method of traversing across multiple user interface layers in a hierarchy of an application by an electronic device is provided. The method includes displaying a first user interface layer of the application on a screen of an electronic device. Further, the method includes detecting an input on a first graphical element displayed within the first user interface layer, and displaying, on the screen, the first user interface layer, a second user interface layer of the application, and at least one first indicia indicating a relation between the first user interface layer and the second user interface layer based on the input to traverse to the second user interface layer.

Abstract: Embodiments herein provide method for automatically managing activities of an application in an electronic device. The method includes detecting by an activity controller an input on a data item of the application displayed on a screen of the electronic device. Further, the method includes determining by the activity controller at least one activity associated with the data item. Furthermore, the method includes causing by the activity controller to automatically display an at least one activity interface and performing the at least one activity associated with the data item within the at least one activity interface. The at least one activity interface comprises a synchronization element to synchronize a result of the at least one performed activity with the application displayed on the screen of the electronic device.

Abstract: Techniques for routing items addressed to an unstructured address are described. One embodiment includes receiving an order for delivery of a first package, the order specifying a first address that does not comply with a defined address format. The first address is processed using one or more hybrid machine learning algorithms to determine a Normalized Delivery Location (NDL) associated with the first address. A sorting zone that encompasses the NDL is determined. The sorting zones correspond to a predefined geographic region. Embodiments facilitate transport of the first package to a physical shipping location within the predefined geographic region.

Abstract: A method of traversing across multiple user interface layers in a hierarchy of an application by an electronic device is provided. The method includes displaying a first user interface layer of the application on a screen of an electronic device. Further, the method includes detecting an input on a first graphical element displayed within the first user interface layer, and displaying, on the screen, the first user interface layer, a second user interface layer of the application, and at least one first indicia indicating a relation between the first user interface layer and the second user interface layer based on the input to traverse to the second user interface layer.

Abstract: A job profile includes characteristics of a job to be executed, where the characteristics of the job profile relate to map tasks and reduce tasks of the job, and where the map tasks produce intermediate results based on input data, and the reduce tasks produce an output based on the intermediate results. In response to a failure in a system, numbers of failed map tasks and reduce tasks of the job based on a time of the failure are computed, and numbers of remaining map tasks and reduce tasks are computed. A performance model is provided, and a performance parameter of the job is estimated using the performance model.

Abstract: A job profile is received that includes characteristics of a job to be executed, where the characteristics of the job profile relate to map tasks and reduce tasks of the job. The map tasks produce intermediate results based on input data, and the reduce tasks produce an output based on the intermediate results. The characteristics of the job profile include at least one particular characteristic that varies according to a size of data to be processed. The at least one particular characteristic of the job profile is set based on the size of the data to be processed.

Abstract: Various methods are provided directed to a compiler-guided software accelerator for iterative HADOOP® jobs. A method includes identifying intermediate data, generated by an iterative HADOOP® application, below a predetermined threshold size and used less than a predetermined threshold time period. The intermediate data is stored in a memory device. The method further includes minimizing input, output, and synchronization overhead for the intermediate data by selectively using at any given time any one of a Message Passing Interface and Distributed File System as a communication layer. The Message Passing Interface is co-located with the HADOOP® Distributed File System.

Abstract: Determining a schedule of a batch workload of MapReduce jobs is disclosed. A set of multi-stage jobs for processing in a MapReduce framework is received, for example, in a master node. Each multi-stage job includes a duration attribute, and each duration attribute includes a stage duration and a stage type. The MapReduce framework is separated into a plurality of resource pools. The multi-stage jobs are separated into a plurality of subgroups corresponding with the plurality of pools. Each subgroup is configured for concurrent processing in the MapReduce framework. The multi-stage jobs in each of the plurality of subgroups are placed in an order according to increasing stage duration. For each pool, the multi-stage jobs in increasing order of stage duration are sequentially assigned from either a front of the schedule or a tail of the schedule by stage type.

Abstract: Parameter values are extracted from information regarding a workload including map tasks and reduce tasks. A benchmark specification is produced based on the extracted parameter values, the benchmark specification including parameters and respective collections of values for the parameters. Based on the benchmark specification, benchmarks are produced that describe respective characteristics of the map and reduce tasks.

Abstract: A job profile describes characteristics of a job. A performance parameter is calculated based on the job profile, and using a value of the performance parameter, an allocation of resources is determined to assign to the job to meet a performance goal associated with a job.

Abstract: At least one benchmark is determined. The at least one benchmark is run on first and second computing platforms to generate platform profiles. Based on the generated platform profiles, a model is generated that characterizes a relationship between a MapReduce job executing on the first platform and the MapReduce job executing on the second platform, wherein the MapReduce job includes map tasks and reduce tasks.

Abstract: A plurality of job profiles is received. Each job profile describes a job to be executed, and each job includes map tasks and reduce tasks. An execution duration for a map stage including the map tasks and an execution duration for a reduce stage including the reduce tasks of each job is estimated. The jobs are scheduled for execution based on the estimated execution duration of the map stage and the estimated execution duration of the reduce stage of each job.

Abstract: A job profile includes characteristics of a job to be executed, where the characteristics of the job profile relate to map tasks and reduce tasks of the job, and where the map tasks produce intermediate results based on input data, and the reduce tasks produce an output based on the intermediate results. In response to a failure in a system, numbers of failed map tasks and reduce tasks of the job based on a time of the failure are computed, and numbers of remaining map tasks and reduce tasks are computed. A performance model is provided, and a performance parameter of the job is estimated using the performance model.

Abstract: An aspect of the present invention simplifies provisioning of asynchronous interaction with enterprise suites having synchronous integration points. In one embodiment, a digital processing system receives, from a user, data indicating that asynchronous interaction is required for a synchronous integration point of an enterprise suite, and in response, auto-generates a set of service oriented architecture (SOA) artifacts designed to synchronously invoke the synchronous integration point and provide asynchronous interaction with external systems. The auto-generated SOA artifacts are then deployed in SOA server, whereby external systems can interact asynchronously to access a functionality provided by the synchronous integration point. According to another aspect, the system, also receives from the user, a second data indicating a set of policies to be applied, and in response, auto-generates the set of SOA artifacts to implement the set of policies during asynchronous interaction with external systems.

Abstract: Various methods are provided directed to a compiler-guided software accelerator for iterative HADOOP jobs. A method includes identifying intermediate data, generated by an iterative HADOOP application, below a predetermined threshold size and used less than a predetermined threshold time period. The intermediate data is stored in a memory device. The method further includes minimizing input, output, and synchronization overhead for the intermediate data by selectively using at any given time any one of a Message Passing Interface and Distributed File System as a communication layer. The Message Passing Interface is co-located with the HADOOP Distributed File System.

Abstract: A job profile is received that includes characteristics of a job to be executed, where the characteristics of the job profile relate to map tasks and reduce tasks of the job. The map tasks produce intermediate results based on input data, and the reduce tasks produce an output based on the intermediate results. The characteristics of the job profile include at least one particular characteristic that varies according to a size of data to be processed. The at least one particular characteristic of the job profile is set based on the size of the data to be processed.

Abstract: Allocations of resources are determined for jobs that have map tasks and reduce tasks. The jobs are ordered according to performance goals of the jobs. The tasks of the jobs are scheduled for execution according to the ordering and the allocations of resources for the respective jobs.

Abstract: A performance model for a collection of jobs that make up a program is used to calculate a performance parameter based on a number of map tasks in the jobs, a number of reduce tasks in the jobs, and an allocation of resources, where the jobs include the map tasks and the reduce tasks, the map tasks producing intermediate results based on segments of input data, and the reduce tasks producing an output based on the intermediate results. The performance model considers overlap of concurrent jobs. Using a value of the performance parameter calculated by the performance model, a particular allocation of resources is determined to assign to the jobs of the program to meet a performance goal of the program.