Despite the widespread adoption of electronic medical records (EMR), the variations in format and terminology across institutions hinder inter-institutional comparisons and feature extraction. This paper proposes a demonstration of extracting frequent disease-specific instruction sequences and efficiently visualizing them with statistical insights, e.g. statistical trends, and abnormal inspection result rates from real multi-institutional EMR data. The utility of the developed visualization tool was presented for decision support and improving clinical processes.

With the increasing use of electronic medical records, medical support from analysis of the accumulated medical information is expected. Currently, new treatment methods and drugs are being developed for the treatment of new diseases, but the transition history of medical orders has yet to be visualized for diseases such as COVID-19. In this paper, we use sequential pattern mining to extract frequent medical orders and then apply the longest common subsequence variant (LCSV) and merged sequence variant (MSV) to analyze the differences in treatment patterns at different times. We also propose three types of sliding window (time interval window, sequence number window, and time-sequence number window) to analyze the transition history of medical orders. As an example, we applied these methods to Japanese electronic medical records covering the first to the fifth waves of COVID-19 and analyzed the differences in medical-order patterns for the five infection waves and the transition history of medical orders. We then visualized the difference with MSV. The results showed that the proposed method can successfully visualize the differences in medical orders between infection waves, and the transition history of medical orders can be revealed. The validity of the results was confirmed by the medical staff involved.

Electronic medical record systems have been adopted by many large hospitals worldwide, enabling the recorded data to be analyzed by various computer-based techniques to gain a better understanding of hospital-based disease treatments. Among such techniques, sequential pattern mining, already widely used for data mining and knowledge discovery in other application domains, has shown great potential for discovering frequent patterns in sequences of disease treatments. However, studies have yet to evaluate the use of medical-order sequence variants , where a “frequent pattern” can include some limited variations to the pattern, or have considered the factors that lead to these variants. Such a study would be meaningful for medical tasks such as improving the quality of a particular treatment method, comparing treatments with multiple hospitals, recommending the best-suited treatment for each patient, and optimizing the running costs in hospitals. This article proposes methods for evaluating medical-order sequence variants and understanding variant factors based on a statistical approach. We consider the safety and efficiency of sequences and related information about the variants, such as gender, age, and test results from hospitals. Our proposal has been demonstrated as effective by experimentally evaluating an electronic medical record system’s real dataset and obtaining feedback from medical workers. The experimental results indicate that the medical treatment history and specimen test results after hospitalization are significant in identifying the factors that lead to variants.

Next-item recommendation solutions based on sequential pattern mining have been widely used in empirical studies. However, current solutions do not consider recommendations involving large combinations of items with varied values. For example, inspecting many specimens is key to understanding a patient's current health status and to checking a medical prescription's effectiveness. Typically, a specimen inspection may involve dozens of inspection items selected from more than a thousand possible items, with each item being associated with a measured value. The values themselves will differ for different items. Based on the pattern of previous item values, recommending the next specimen inspection from a huge number of candidate ones, requires that a combination of many inspection items must be processed efficiently. This paper presents a method for vectorizing a combination of items and item values, and identifying clusters of item-set types. From the item-set types that best suit the target input, a set of items is recommended using both frequency and uniqueness. The method was tested experimentally, using real data from a university hospital's electronic medical record system. The results showed that the proposed method can successfully recommend specific item types with the highest precision and recall ratios. The validity of the results was confirmed by the hospital's medical staff.

With the recent performance improvements in commodity hardware, low-cost commodity server-based storage has become a practical alternative to dedicated-storage appliances. Because of the high failure rate of commodity servers, data redundancy across multiple servers is required in a server-based storage system. However, the extra storage capacity for this redundancy significantly increases the system cost. Although erasure coding (EC) is a promising method to reduce the amount of redundant data, it requires distributing and encoding data among servers. There remains a need to reduce the performance impact of these processes involving much network traffic and processing overhead. Especially, the performance impact becomes significant for random-intensive applications. In this article, we propose a new lightweight redundancy control for server-based storage. Our proposed method uses a new local filesystem-based approach that avoids distributing data by adding data redundancy to locally stored user data. Our method switches the redundancy method of user data between replication and EC according to workloads to improve capacity efficiency while achieving higher performance. Our experiments show up to 230% better online-transaction-processing performance for our method compared with CephFS, a widely used alternative system. We also confirmed that our proposed method prevents unexpected performance degradation while achieving better capacity efficiency.

With the increasing prevalence of Internet-of-Things (IoT) application, Wireless Sensor Networks (WSNs), as a layer in the IoT hierarchy which collects data from the environment, have received wide attention. An efficient category of routing protocols for WSN is cluster routing. However, secured messages routing has long been a concern, as traditional network security practices are inappropriate for WSNs due to their inherent hardware limitations. Hence, this paper introduces and explains a novel centralized statistics-based trust scheme with consideration on power budget, within the context of WSN cluster routing, aiming at detecting unreliable or potentially malicious sensor nodes. The proposed scheme is theoretically able to secure confidentiality, integrity and authenticity of messages in a WSN cluster routing setup, while the simulation results indicate that the computed trust values can correctly identify the node that causes message loss, which helps to improve messages availability.

Electronic medical record (EMR) systems have now been widely adopted to support medical workers. There also has been much interest in the machine-based generation of clinical pathways that can utilize sequential pattern mining (SPM) to extract them from historical EMR systems. However, the existing methods do not protect individual privacy, even though they involve sensitive medical data. To ensure the privacy of individual data, this paper describes two algorithms that deploy differential privacy by adding noise during calculations in the SPM considering time interval for guaranteeing privacy. The proposals can limit the amount of added noise by adding noise to the frequency calculations of only a part of candidate closed sequences. Experiments on real medical datasets show that our proposal can ensure the robust and high utility of mining process even with minimum privacy budget and amount of added noise.

Sequential pattern mining (SPM) is widely used for data mining and knowledge discovery in various application domains. Recently, we have proposed an analyzing method to evaluate the sequence pattern variant (SPV) that is the original sequence containing frequent patterns including variants. Such a study is meaningful for medical tasks such as improving the quality of a disease's treatment method. This paper aims to evaluate the effectiveness of the proposed analyzing method in more detail when it was applied to Electronic Medical Record Systems. Using a real dataset, it is observed that the analyzing method is successful in statistically discovering the meaningful indicators that are leading to the difference between comparative SPVs, such as complicated risk, severity risk of the disease, the length of stay in the hospital and the total medical cost. Moreover, it is observed that the length of stay and the medical cost can gain more benefit from increasing the significance level parameter used in comparing the SPVs.

The recent performance improvements in commodity hardware have made commodity server-based storage a practical alternative to dedicated-storage appliances. Because of the low reliability of commodity servers, data redundancy across multiple servers is required for high availability of a server-based storage system. However, the extra storage capacity required to enable this redundancy increases the system cost significantly. Although erasure coding (EC) is a promising approach to reducing the amount of redundant data, it is only available in systems using distributed storage. There remains the need to reduce the performance overhead of using distributed storage, which involves much network traffic and metadata processing. In this paper, we propose a lightweight redundancy-control method called "dynamic redundancy control" for server-based storage systems. Our method adds additional file metadata for EC to the local filesystem, enabling the system to take advantage of EC with reduced network traffic and metadata processing. In addition, our method dynamically controls the data redundancy in user data between replication and EC to improve the capacity efficiency while mitigating performance degradation. Our experiments show that our method achieves up to 76% less processing time for a metadata-intensive workload, up to 150% higher read performance, and up to 147% better online-transaction-processing performance than CephFS, a widely used alternative system. In addition, our method successfully improves capacity efficiency while mitigating performance degradation.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

Energy-aware commodity-based distributed file systems for efficient Big Data processing are increasingly moving towards power-proportional designs. However, current data placement methods for such systems have not given careful consideration to the effect of gear-shifting during operations. If the system wants to shift to a higher gear, it must reallocate the updated datasets that were modified in a lower gear when a subset of the nodes was powered off, but without disrupting the servicing of requests from clients. Inefficient gear-shifting that requires a large amount of data reallocation greatly degrades the system performance. This paper proposes a data placement method known as Accordion, which uses data replication to arrange the data layout comprehensively and provide efficient gear-shifting. Compared with current approaches, Accordion reduces the amount of data transferred, which significantly shortens the period required to reallocate the updated data during gear-shifting. The effect of this reduction is larger with higher gears so Accordion is suitable for smooth gear-shifting in multigear systems. Moreover, the times when the active nodes serve the requests are well distributed so Accordion is capable of higher scalability than existing methods based on the I/O throughput performance. Accordion does not require any strict constraint on the number of nodes in the system therefore our proposed method is expected to work well in practical environments. Extensive empirical experiments using actual machines with an Accordion prototype based on the Hadoop Distributed File System demonstrated that our proposed method significantly reduced the period required to transfer updated data, i.e., by 66% compared with an existing method.

Power-saving storage in data centers is now gaining much interest because of the increase in power requirements. In particular, with the expansion of services requiring distributed-processing frameworks, power proportionality in power-aware file systems is attracting great attention from academia and industry. The concept of power proportionality is that a system should perform work in proportion to the energy it consumes. To provide this important characteristic, data placement methods that enable a system to operate in multiple gears, each containing a different number of active nodes, have been proposed. Of these, RABBIT, with its power proportional data placement, is a novel method that is implemented over a Hadoop Distributed File System (HDFS) and has been shown to be successful in guaranteeing power proportionality for read only tasks. However, the data layout used in RABBIT does not consider write access occurring when the system operates in low gear and there are inactive nodes. In this paper, to discover an appropriate approach for power-efficient distributed file systems, we evaluate the performance of the RABBIT and PARAID methods on read-only tasks and the cost to system performance of write access in low gear. The skewed data placement in PARAID, which was one of the first methods to suggest power proportionality in disk-based storage systems, is supposed to offer good performance when dealing with a frequently updated dataset.