Getting Your RPA Priorities Straight with Process Mining: The PLOST Framework

Robotic Process Automation (RPA) has gained much attention both in industry and academia. One of the main challenges for a successful implementation of RPA is selecting which tasks should be automated. While different methods exist to identify RPA candidate tasks, they lack in providing objective evidence on why to automate that task. Such objective evidence can be gathered by applying process mining techniques to gain insights into the performance of a process and its tasks. Although this has multiple advantages, it can be time-consuming to analyze all potential processes. We conducted a literature review of existing methods to identify relevant criteria and method components, based on which we designed a framework for identifying and prioritizing suitable RPA candidate tasks: the Prioritized List of Suitable Tasks (PLOST) Framework. The framework includes both qualitative and quantitative assessment criteria and guides the analyst to focus on relevant processes before zooming in on the task level. It also takes into account a customized automation strategy. We conducted a case study to evaluate the applicability and effectiveness of the PLOST Framework and performed thinking-aloud sessions to evaluate its usability, practicality, and completeness. The results show that the framework is easy to apply and feasible

DSN Beowulf Cluster-Based VLBI Correlator

The NASA Deep Space Network (DSN) requires a broadband VLBI (very long baseline interferometry) correlator to process data routinely taken as part of the VLBI source Catalogue Maintenance and Enhancement task (CAT M&E) and the Time and Earth Motion Precision Observations task (TEMPO). The data provided by these measurements are a crucial ingredient in the formation of precision deep-space navigation models. In addition, a VLBI correlator is needed to provide support for other VLBI related activities for both internal and external customers. The JPL VLBI Correlator (JVC) was designed, developed, and delivered to the DSN as a successor to the legacy Block II Correlator. The JVC is a full-capability VLBI correlator that uses software processes running on multiple computers to cross-correlate two-antenna broadband noise data. Components of this new system (see Figure 1) consist of Linux PCs integrated into a Beowulf Cluster, an existing Mark5 data storage system, a RAID array, an existing software correlator package (SoftC) originally developed for Delta DOR Navigation processing, and various custom- developed software processes and scripts. Parallel processing on the JVC is achieved by assigning slave nodes of the Beowulf cluster to process separate scans in parallel until all scans have been processed. Due to the single stream sequential playback of the Mark5 data, some ramp-up time is required before all nodes can have access to required scan data. Core functions of each processing step are accomplished using optimized C programs. The coordination and execution of these programs across the cluster is accomplished using Pearl scripts, PostgreSQL commands, and a handful of miscellaneous system utilities. Mark5 data modules are loaded on Mark5 Data systems playback units, one per station. Data processing is started when the operator scans the Mark5 systems and runs a script that reads various configuration files and then creates an experiment-dependent status database used to delegate parallel tasks between nodes and storage areas (see Figure 2). This script forks into three processes: extract, translate, and correlate. Each of these processes iterates on available scan data and updates the status database as the work for each scan is completed. The extract process coordinates and monitors the transfer of data from each of the Mark5s to the Beowulf RAID storage systems. The translate process monitors and executes the data conversion processes on available scan files, and writes the translated files to the slave nodes. The correlate process monitors the execution of SoftC correlation processes on the slave nodes for scans that have completed translation. A comparison of the JVC and the legacy Block II correlator outputs reveals they are well within a formal error, and that the data are comparable with respect to their use in flight navigation. The processing speed of the JVC is improved over the Block II correlator by a factor of 4, largely due to the elimination of the reel-to-reel tape drives used in the Block II correlator

A Deep Space Network Portable Radio Science Receiver

The Radio Science Receiver (RSR) is an open-loop receiver installed in NASA s Deep Space Network (DSN), which digitally filters and records intermediate-frequency (IF) analog signals. The RSR is an important tool for the Cassini Project, which uses it to measure perturbations of the radio-frequency wave as it travels between the spacecraft and the ground stations, allowing highly detailed study of the composition of the rings, atmosphere, and surface of Saturn and its satellites

Energy cost and return for hunting in African wild dogs and Cheetahs

African wild dogs (Lycaon pictus) are reported to hunt with energetically costly long chase distances. We used high-resolution GPS and inertial technology to record 1,119 high-speed chases of all members of a pack of six adult African wild dogs in northern Botswana. Dogs performed multiple short, high-speed, mostly unsuccessful chases to capture prey, while cheetahs (Acinonyx jubatus) undertook even shorter, higher-speed hunts. We used an energy balance model to show that the energy return from group hunting and feeding substantially outweighs the cost of multiple short chases, which indicates that African wild dogs are more energetically robust than previously believed. Comparison with cheetah illustrates the trade-off between sheer athleticism and high individual kill rate characteristic of cheetahs, and the energetic robustness of frequent opportunistic group hunting and feeding by African wild dogs