Sociotechnical systems as applied to knowledge work

This study examines the logic behind choosing variances and the design of forums during the planning of deliberations in non-routine work environments using a Sociotechnical System design approach. This study was accomplished through review and comparison of literature on sociotechnical applications of non-routine, knowledge work environments. The traditional sociotechnical application applied to factory settings with linear and routine work tasks analyzes unit operations within an open system, identifying technical variances that contribute to problems and social roles that control the variances. A new sociotechnical approach has been developed for systems involved in non-routine, knowledge work environments. This approach focuses on deliberations formed around topics, establishes variances that lead to poor deliberations, designs forums that minimize variances and gives control of variances to discretionary coalitions. These results generally support that variances contributing to poor deliberations are well established and that organizations need only identify the key variances that contribute to problems in their system. Organizations need to understand how the key variances affect the development of knowledge and how forums can be designed to enhance deliberations. This study places specific focus on the design of information technology forums that enhance knowledge developmenthttp://www.archive.org/details/sociotechnicalsy00oswaLieutenant, United States NavyApproved for public release; distribution is unlimited

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Robust health stream processing

2014 Fall.Includes bibliographical references.As the cost of personal health sensors decrease along with improvements in battery life and connectivity, it becomes more feasible to allow patients to leave full-time care environments sooner. Such devices could lead to greater independence for the elderly, as well as for others who would normally require full-time care. It would also allow surgery patients to spend less time in the hospital, both pre- and post-operation, as all data could be gathered via remote sensors in the patients home. While sensor technology is rapidly approaching the point where this is a feasible option, we still lack in processing frameworks which would make such a leap not only feasible but safe. This work focuses on developing a framework which is robust to both failures of processing elements as well as interference from other computations processing health sensor data. We work with 3 disparate data streams and accompanying computations: electroencephalogram (EEG) data gathered for a brain-computer interface (BCI) application, electrocardiogram (ECG) data gathered for arrhythmia detection, and thorax data gathered from monitoring patient sleep status

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Security of Cyber-Physical Systems

Cyber-physical system (CPS) innovations, in conjunction with their sibling computational and technological advancements, have positively impacted our society, leading to the establishment of new horizons of service excellence in a variety of applicational fields. With the rapid increase in the application of CPSs in safety-critical infrastructures, their safety and security are the top priorities of next-generation designs. The extent of potential consequences of CPS insecurity is large enough to ensure that CPS security is one of the core elements of the CPS research agenda. Faults, failures, and cyber-physical attacks lead to variations in the dynamics of CPSs and cause the instability and malfunction of normal operations. This reprint discusses the existing vulnerabilities and focuses on detection, prevention, and compensation techniques to improve the security of safety-critical systems

Harnessing Big Data and Machine Learning for Event Detection and Localization

Anomalous events are rare and significantly deviate from expected pattern and other data instances, making them hard to predict. Correctly and timely detecting anomalous severe events can help reduce risks and losses. Many anomalous event detection techniques are studied in the literature. Recently, big data and machine learning based techniques have shown a remarkable success in a wide range of fields. It is important to tailor big data and machine learning based techniques for each application; otherwise it may result in expensive computation, slow prediction, false alarms, and improper prediction granularity.First, we aim to address the above challenges by harnessing big data and machine learning techniques for fast and reliable prediction and localization of severe events. Firstly, to improve storage failure prediction, we develop a new lightweight and high performing tensor decomposition-based method, named SEFEE, for storage error forecasting in large-scale enterprise storage systems. SEFEE employs tensor decomposition technique to capture latent spatio-temporal information embedded in storage event logs. By utilizing the latent spatio-temporal information, we can make accurate storage error forecasting without training requirements of typical machine learning techniques. The training-free method allows for live prediction of storage errors and their locations in the storage system based on previous observations that had been used in tensor decomposition pipeline to extract meaningful latent correlations. Moreover, we propose an extension to include severity of the errors as contextual information to improve the accuracy of tensor decomposition which in turn improves the prediction accuracy. We further provide detailed characterization of NetApp dataset to provide additional insight into the dynamics of typical large-scale enterprise storage systems for the community.Next, we focus on another application -- AI-driven Wildfire prediction. Wildfires cause billions of dollars in property damages and loss of lives, with harmful health threats. We aim to correctly detect and localize wildfire events in the early stage and also classify wildfire smoke based on perceived pixel density of camera images. Due to the lack of publicly available dataset for early wildfire smoke detection, we first collect and process images from the AlertWildfire camera network. The images are annotated with bounding boxes and densities for deep learning methods to use. We then adapt a transformer-based end-to-end object detection model for wildfire detection using our dataset. The dataset and detection model together form as a benchmark named the Nevada smoke detection benchmark, or Nemo for short. Nemo is the first open-source benchmark for wildfire smoke detection with the focus of the early incipient stage. We further provide a weakly supervised Nemo version to enable wider support as a benchmark

Aligning Incentives in Accountable Care Organizations: The Role of Medical Malpractice Reform

The Patient Protection and Affordable Care Act (ACA) encourages physicians, hospitals, and other health care providers to deliver better coordinated, high-quality care through the institution of the Medicare Shared Savings Program. Many physicians and other providers moved quickly after the ACA was enacted to enter into arrangements that would allow them to take advantage of the MSSP and similar programs sponsored by private insurers that likely would — and did — arrive on the MSSP’s heels. Yet despite the initial enthusiasm, it is by no means clear that ACOs will succeed, whether individually or in the greater goal of changing our health care delivery system. To be successful, ACOs will require a substantial amount of coordination and participant buy-in to a particular practice ethos. How does one convince skeptical and independent-minded physicians to follow guidelines and metrics set forth by ACOs — guidelines and metrics that are devised in part to reduce the volume of certain types of services provided, and hence also potentially lowering physicians’ financial returns? How does one do this, in particular, when physicians not only may be making less money as a result of following these guidelines and metrics, but will also retain full liability for negligent outcomes? If ACOs are to succeed more broadly, it may be important for state legislatures to address medical malpractice to reflect the changes currently underway in our health care system. The question is how to do this while also facilitating better integration of care delivery and, ideally, sufficiently improving the practice of medicine such that a critical mass of physicians will support and participate in the proposed changes. The answer may best be given by an idea last entertained during the heyday of managed care: enterprise liability. As the name suggests, enterprise liability would make a health care entity, such as a hospital or an ACO, financially liable for acts of negligence, rather than or possibly in addition to the individual providers staffing it or otherwise providing services under its auspices. Given the consolidation in the health care market, the increasing movement toward employment of physicians by hospitals, health insurers, and other entities, the incentives that the ACA gives for various forms of integrated care that meet or exceed quality benchmarks, and the persistence of the problems of our traditional means of addressing medical malpractice, this article discusses enterprise liability and argues that the time may be ripe to revisit enterprise liability as a means of rationally revamping our medical liability system

A Pattern-Based Approach to Scaffold the IT Infrastructure Design Process

Context. The design of Information Technology (IT) infrastructures is a challenging task since it implies proficiency in several areas that are rarely mastered by a single person, thus raising communication problems among those in charge of conceiving, deploying, operating and maintaining/managing them. Most IT infrastructure designs are based on proprietary models, known as blueprints or product-oriented architectures, defined by vendors to facilitate the configuration of a particular solution, based upon their services and products portfolio. Existing blueprints can be facilitators in the design of solutions for a particular vendor or technology. However, since organizations may have infrastructure components from multiple vendors, the use of blueprints aligned with commercial product(s) may cause integration problems among these components and can lead to vendor lock-in. Additionally, these blueprints have a short lifecycle, due to their association with product version(s) or a specific technology, which hampers their usage as a tool for the reuse of IT infrastructure knowledge. Objectives. The objectives of this dissertation are (i) to mitigate the inability to reuse knowledge in terms of best practices in the design of IT infrastructures and, (ii) to simplify the usage of this knowledge, making the IT infrastructure designs simpler, quicker and better documented, while facilitating the integration of components from different vendors and minimizing the communication problems between teams. Method. We conducted an online survey and performed a systematic literature review to support the state of the art and to provide evidence that this research was relevant and had not been conducted before. A model-driven approach was also used for the formalization and empirical validation of well-formedness rules to enhance the overall process of designing IT infrastructures. To simplify and support the design process, a modeling tool, including its abstract and concrete syntaxes was also extended to include the main contributions of this dissertation. Results. We obtained 123 responses to the online survey. Their majority were from people with more than 15 years experience with IT infrastructures. The respondents confirmed our claims regarding the lack of formality and documentation problems on knowledge transfer and only 19% considered that their current practices to represent IT Infrastructures are efficient. A language for modeling IT Infrastructures including an abstract and concrete syntax is proposed to address the problem of informality in their design. A catalog of IT Infrastructure patterns is also proposed to allow expressing best practices in their design. The modeling tool was also evaluated and according to 84% of the respondents, this approach decreases the effort associated with IT infrastructure design and 89% considered that the use of a repository with infrastructure patterns, will help to improve the overall quality of IT infrastructures representations. A controlled experiment was also performed to assess the effectiveness of both the proposed language and the pattern-based IT infrastructure design process supported by the tool. Conclusion. With this work, we contribute to improve the current state of the art in the design of IT infrastructures replacing the ad-hoc methods with more formal ones to address the problems of ambiguity, traceability and documentation, among others, that characterize most of IT infrastructure representations. Categories and Subject Descriptors:C.0 [Computer Systems Organization]: System architecture; D.2.10 [Software Engineering]: Design-Methodologies; D.2.11 [Software Engineering]: Software Architectures-Patterns