A clinical pathway (CP) is a tool for effectively managing a care process. There are several research efforts on developing clinical pathways (CPs) in the process mining domain. However, the nature of the data affects data analysis results, and patient clinical variability makes it challenging to develop CPs. Thus, it is crucial to determine candidate care processes that can be standardized as CPs before applying process mining techniques. This paper proposed a method for assessing CP feasibility regarding clinical complexity using clinical order logs from electronic health records. The proposed method consists of data preparation, activity & trace homogeneity evaluations, and process inspection using process mining. Each step consists of metrics to measure the homogeneity of processes and a visualization method to demonstrate the diversity of processes based on the log. The case study was conducted with five surgical groups of patients from a tertiary hospital in South Korea to validate the proposed method. The five groups of patients were successfully assessed. In addition, the visualization methods helped clinical experts grasp the diversity of care processes.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Predictive business process monitoring aims at providing the predictions about running instances by analyzing logs of completed cases of a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using LSTM with online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.

Interests in manufacturing process management and analysis are increasing, but it is difficult to conduct process analysis due to the increase of manufacturing data. Therefore, we suggest a manufacturing data analysis system that collects event logs from so-called big data and analyzes the collected logs with process mining. There are two kinds of big data generated from manufacturing processes, structured data and unstructured data. Usually, manufacturing process analysis is conducted by using only structured data, however the proposed system uses both structured and unstructured data for enhancing the process analysis results. The system automatically discovers a process model and conducts various performance analysis on the manufacturing processes.

Energy consumption is an important issue in data centers, and disks use a significant proportion of the total energy. A promising approach to reducing disk energy consumption is to use multispeed disks with lower rotational speeds, and allowing disks to run slowly when workloads are light can reduce their large contribution to the power used by video servers. We formulate the prerequisites for speed reductions, and derive an energy model constrained by retrieval period. We then analyze the relationship between retrieval period, buffer size, and disk speed, and examine the effect of buffer allocation on energy consumption. We then propose a new video data retrieval scheme in which the retrieval period and the speed of each disk is dynamically changed to adjust disk bandwidth utilization, with the aim of allowing disks to run at lower speeds while guaranteeing jitter-free speed transitions. Experimental results show that our scheme achieves appreciable energy savings under all workloads. They also reveal that increasing the number of available speeds reduces energy consumption but the benefits gradually tail off.

An effective way for reducing CPU power consumption is to reduce its operating frequency. But this slows down program execution, which may violate the real-time requirements of video playback. What is worse, it is difficult to predict future decoding times, and unduly conservative policies may miss viable opportunities to reduce the CPU frequency. The effectiveness of such power-saving techniques is thus dependent on an ability to estimate future demands on the CPU. We present the design, implementation and evaluation of a dynamic voltage scaling (DVS) scheme for portable media players. We measured decoding times on real videos and extracted a precise relationship between frame size and decoding time using logarithmic regression. Based on this model, we propose a frequency selection algorithm which accepts some deadline misses, and selects the frequencies required to achieve a specified deadline miss ratio. We implemented this scheme in MPlayer running on the Linux 2.6. Experimental results show that its system-wide energy consumption is up to 17% less than conventional DVS schemes and up to 24% less than non-DVS schemes.

Energy consumption is an important issue for data centers, and disks use a significant proportion of the total energy. Mirrored video servers handle data requests adaptively from their primary or backup copies allowing some disks to go into standby mode, and disk bandwidth utilization has a great effect on the total energy consumption. We analyze the relationship between the amount of cached data and the power consumption of each disk, and propose a new energy-aware interval caching (EAIC) scheme which considers the power state of the disks, so as to reduce their total energy consumption. Simulation results show that EAIC can save as much as 22% more energy than a conventional interval caching scheme.

Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions using decision mining, also referred to as decision point analysis. In this paper we describe how the resulting model (including the discovered data dependencies) can be represented as a Colored Petri Net (CPN), and how further perspectives, such as the performance and organizational perspective, can be incorporated. We also present a CPN Tools Export plug-in implemented within the ProM framework. Using this plug-in, simulation models in ProM obtained via a combination of various process mining techniques can be exported to CPN Tools. We believe that the combination of automatic discovery of process models using ProM and the simulation capabilities of CPN Tools offers an innovative way to improve business processes. The discovered process model describes reality better than most hand-crafted simulation models. Moreover, the simulation models are constructed in such a way that it is easy to explore various redesigns.

Energy consumption is an important issue in data centers, and disks use a significant proportion of the total energy. A promising approach to reducing disk energy consumption is to use multi-speed disks with lower rotational speeds. But changing the speed requires a significant time, which reduces the number of viable opportunities to decrease the disk speed. We propose a new energy-aware data prefetching scheme for multi-speed disks in video servers. We start by examining the power and performance characteristics of multi-speed disks. We then analyze the disk bandwidth and buffer requirements needed for jitter-free speed transitions and propose a new data prefetching scheme to extend the time that disks stay at low speed. The effectiveness of the proposed scheme is evaluated through simulations.

In this paper we propose storage allocation schemes to minimize the buffer requirements for fault-tolerant video-on-demand (VOD) servers with disk arrays. To guarantee the high reliability of video service, the servers usually adopt parity-encoding techniques in which data blocks and their associated parity block form a parity group. For real-time video service, all the blocks in a parity group are prefetched to cope with disk failure. Buffer overhead incurred by a prefetch can be reduced by decreasing the parity group size, which demands more storage space for parity blocks. Our proposed scheme called round-level parity grouping (RPG) aims at restoring VBR video streams efficiently. Based on RPG we have developed a heuristic algorithm working towards effective buffer management. The experimental results show that our proposed schemes produce near-optimal buffer requirements.