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This study is about estimating the reproducibility of finding palpation points of three different anatomical landmarks in the human body (Xiphoid Process and the 2 Hip Crests) to support a navigated ultrasound application. On 6 test subjects with different body mass index the three palpation points were located five times by two examiners. The deviation from the target position was calculated and correlated to the fat thickness above each palpation point. The reproducibility of the measurements had a mean error of ≈13.5 mm +- 4 mm, which seems to be sufficient for the desired application field.
Sleep analysis using a Polysomnography system is difficult and expensive. That is why we suggest a non-invasive and unobtrusive measurement. Very few people want the cables or devices attached to their bodies during sleep. The proposed approach is to implement a monitoring system, so the subject is not bothered. As a result, the idea is a non-invasive monitoring system based on detecting pressure distribution. This system should be able to measure the pressure differences that occur during a single heartbeat and during breathing through the mattress. The system consists of two blocks signal acquisition and signal processing. This whole technology should be economical to be affordable enough for every user. As a result, preprocessed data is obtained for further detailed analysis using different filters for heartbeat and respiration detection. In the initial stage of filtration, Butterworth filters are used.
Theoretical foundation, effectiveness, and design artefact for machine learning service repositories
(2022)
Machine learning (ML) has played an important role in research in recent years. For companies that want to use ML, finding the algorithms and models that fit for their business is tedious. A review of the available literature on this problem indicates only a few research papers. Given this gap, the aim of this paper is to design an effective and easy-to-use ML service repository. The corresponding research is based on a multi-vocal literature analysis combined with design science research, addressing three research questions: (1) How is current white and gray literature on ML services structured with respect to repositories? (2) Which features are relevant for an effective ML service repository? (3) How is a prototype for an effective ML service repository conceptualized? Findings are relevant for the explanation of user acceptance of ML repositories. This is essential for corporate practice in order to create and use ML repositories effectively.
The tale of 1000 cores: an evaluation of concurrency control on real(ly) large multi-socket hardware
(2020)
In this paper, we set out the goal to revisit the results of “Starring into the Abyss [...] of Concurrency Control with [1000] Cores” and analyse in-memory DBMSs on today’s large hardware. Despite the original assumption of the authors, today we do not see single-socket CPUs with 1000 cores. Instead multi-socket hardware made its way into production data centres. Hence, we follow up on this prior work with an evaluation of the characteristics of concurrency control schemes on real production multi-socket hardware with 1568 cores. To our surprise, we made several interesting findings which we report on in this paper.
In this paper, we propose a radical new approach for scale-out distributed DBMSs. Instead of hard-baking an architectural model, such as a shared-nothing architecture, into the distributed DBMS design, we aim for a new class of so-called architecture-less DBMSs. The main idea is that an architecture-less DBMS can mimic any architecture on a per-query basis on-the-fly without any additional overhead for reconfiguration. Our initial results show that our architecture-less DBMS AnyDB can provide significant speedup across varying workloads compared to a traditional DBMS implementing a static architecture.
In this paper, we present a new approach for achieving robust performance of data structures making it easier to reuse the same design for different hardware generations but also for different workloads. To achieve robust performance, the main idea is to strictly separate the data structure design from the actual strategies to execute access operations and adjust the actual execution strategies by means of so-called configurations instead of hard-wiring the execution strategy into the data structure. In our evaluation we demonstrate the benefits of this configuration approach for individual data structures as well as complex OLTP workloads.
The basic idea behind a wearable robotic grasp assistancesystem is to support people that suffer from severe motor impairments in daily activities. Such a system needs to act mostly autonomously and according to the user’s intent. Vision-based hand pose estimation could be an integral part of a larger control and assistance framework. In this paper we evaluate the performance of egocentric monocular hand pose estimation for a robot-controlled hand exoskeleton in a simulation. For hand pose estimation we adopt a Convolutional Neural Network (CNN). We train and evaluate this network with computer graphics, created by our own data generator. In order to guide further design decisions we focus in our experiments on two egocentric camera viewpoints tested on synthetic data with the help of a 3D-scanned hand model, with and without an exoskeleton attached to it.We observe that hand pose estimation with a wrist-mounted camera performs more accurate than with a head-mounted camera in the context of our simulation. Further, a grasp assistance system attached to the hand alters visual appearance and can improve hand pose estimation. Our experiment provides useful insights for the integration of sensors into a context sensitive analysis framework for intelligent assistance.
Additive Manufacturing is increasingly used in the industrial sector as a result of continuous development. In the Production Planning and Control (PPC) system, AM enables an agile response in the area of detailed and process planning, especially for a large number of plants. For this purpose, a concept for a PPC system for AM is presented, which takes into account the requirements for integration into the operational enterprise software system. The technical applicability will be demonstrated by individual implemented sections. The presented solution approach promises a more efficient utilization of the plants and a more elastic use.
Additive manufacturing (AM) is a promising manufacturing method for many industrial sectors. For this application, industrial requirements such as high production volumes and coordinated implementation must be taken into account. These tasks of the internal handling of production facilities are carried out by the Production Planning and Control (PPC) information system. A key factor in the planning and scheduling is the exact calculation of manufacturing times. For this purpose we investigate the use of Machine Learning (ML) for the prediction of manufacturing times of AM facilities.
The blockchain technology enables a common data basis between the participants. Entries are logged and the authenticity of the participants is guaranteed. In the case of a relationship between customers and producers, this would lead to verifiable cooperation, which would be a major step as companies enter into service contracts based on the flow of many small transactions through communication. This paper proposes an architecture that enables the creation and processing of orders between the customer and producers via a blockchain based production network. The handling of larger files which are traceable via the blockchain is also shown and the use of a public or permissioned blockchain for an application case is also considered.
The performance and scalability of modern data-intensive systems are limited by massive data movement of growing datasets across the whole memory hierarchy to the CPUs. Such traditional processor-centric DBMS architectures are bandwidth- and latency-bound. Processing-in-Memory (PIM) designs seek to overcome these limitations by integrating memory and processing functionality on the same chip. PIM targets near- or in-memory data processing, leveraging the greater in-situ parallelism and bandwidth.
In this paper, we introduce pimDB and provide an initial comparison of processor-centric and PIM-DBMS approaches under different aspects, such as scalability and parallelism, cache-awareness, or PIM-specific compute/bandwidth tradeoffs. The evaluation is performed end-to-end on a real PIM hardware system from UPMEM.
Even though near-data processing (NDP) can provably reduce data transfers and increase performance, current NDP is solely utilized in read-only settings. Slow or tedious to implement synchronization and invalidation mechanisms between host and smart storage make NDP support for data-intensive update operations difficult. In this paper, we introduce a low-latency cache-coherent shared lock table for update NDP settings in disaggregated memory environments. It utilizes the novel CCIX interconnect technology and is integrated in neoDBMS, a near-data processing DBMS for smart storage. Our evaluation indicates end-to-end lock latencies of ∼80-100ns and robust performance under contention.
Multi-versioning and MVCC are the foundations of many modern DBMSs. Under mixed workloads and large datasets, the creation of the transactional snapshot can become very expensive, as long-running analytical transactions may request old versions, residing on cold storage, for reasons of transactional consistency. Furthermore, analytical queries operate on cold data, stored on slow persistent storage. Due to the poor data locality, snapshot creation may cause massive data transfers and thus lower performance. Given the current trend towards computational storage and near-data processing, it has become viable to perform such operations in-storage to reduce data transfers and improve scalability. neoDBMS is a DBMS designed for near-data processing and computational storage. In this paper, we demonstrate how neoDBMS performs snapshot computation in-situ. We showcase different interactive scenarios, where neoDBMS outperforms PostgreSQL 12 by up to 5×.
This work is a report on practical experiences with the issue of interoperability in German practice management systems (PMSs) from an ongoing clinical trial on teledermatology, the TeleDerm project. A proprietary and established web-platform for store-and-forward telemedicine is integrated with the IT in the GPs’ offices for automatic exchange of basic patient data. Most of the 19 different PMSs included in the study sample lack support of modern health data exchange standards, therefore the relatively old but widely available German health data exchange interface “Gerätedatentransfer” (GDT) is used. Due to the lack of enforcement and regulation of the GDT standard, several obstacles to interoperability are encountered. As a partial, but reusable working solution to cope with these issues, we present a custom middleware which is used in conjunction with GDT. We describe the design, technical implementation and observed hindrances with the existing infrastructure. A discussion on health care interfacing standards and the current state of interoperability in German PMS software is given.
Motivation
In order to enable context-aware behavior of surgical assistance systems, the acquisition of various information about the current intraoperative situation is crucial. To achieve this, the complex task of situation recognition can be delegated to a specialized system. Consequently, a standardized interface is required for the seamless transfer of the recognized contextual information to the assistance systems, enabling them to adapt accordingly.
Methods
Our group analyzed four medical interface standards to determine their suitability for exchanging intraoperative contextual information. The assessment was based on a harmonized data and service model derived from the requirements of expected context-aware use cases. The Digital Imaging and Communications in Medicine (DICOM) and IEEE 11073 for Service-oriented Device Connectivity (SDC) were identified as the most appropriate standards.
Results
We specified how DICOM Unified Procedure Steps (UPS), can be used to effectively communicate contextual information. We proposed the inclusion of attributes to formalize different granularity levels of the surgical workflow.
Conclusions
DICOM UPS SOP classes can be used for the exchange of intraoperative contextual information between a situation recognition system and surgical assistance systems. This can pave the way for vendor-independent context awareness in the OR, leading to targeted assistance of the surgical team and an improvement of the surgical workflow.
Large critical systems, such as those created in the space domain, are usually developed by a large number of organizations and, furthermore, they have to comply with standards. Yet, the different stakeholders often do not have a common understanding of the needed quality of requirements specifications. Achieving such a common understanding is a laborious process that is currently not sufficiently supported. Moreover, such a common understanding must be aligned with the standards. In this paper, we present an approach that can be used to align the different stakeholder perceptions regarding the quality of requirements specifications. Existing quality models for requirements specifications are analyzed for equivalences, and transferred into a common representation, the so-called Aligned Quality Map (AQM). Furthermore, a process is defined that supports the alignment of different stakeholder perspectives with regard to the quality of requirements specifications using AQM, which is validated in a case study in the context of European space projects. AQM has been created and populated with an initial set of quality models. It is designed in such way that it can be extended to include further quality models. The case study has shown that an alignment of different stakeholder perspectives and the quality model of the European Cooperation for Space Standardization using AQM is feasible. The approach allows for aligning different stakeholder perspectives for a common understanding of the quality of requirements specifications in the context of standards. Furthermore, AQM supports the assessment of requirements specifications.
This paper investigates the possibility to effectively monitor and control the respiratory action using a very simple and non invasive technique based on a single lightweight reduced-size wireless surface electromyography (sEMG) sensor placed below the sternum. The captured sEMG signal, due to the critical sensor position, is characterized by a low energy level and it is affected by motion artifacts and cardiac noise. In this work we present a preliminary study performed on adults for assessing the correlation of the spirometry signal and the sEMG signal after the removal of the superimposed heart signal. This study and the related findings could be useful in respiratory monitoring of preterm infants.
Smart meter based business models for the electricity sector : a systematical literature research
(2017)
The Act on the Digitization of the Energy Transition forces German industries and households to introduce smart meters in order to save engery, to gain individual based electricity tariffs and to digitize the energy data flow. Smart meter can be regarded as the advancement of the traditional meter. Utilizing this new technology enables a wide range of innovative business models that provide additional value for the electricity suppliers as well as for their customers. In this study, we followed a two-step approach. At first, we provide a state-of-the-art comparison of these business models found in the literature and identify structural differences in the way they add value to the offered products and services. Secondly, the business models are grouped into categories with respect to customer segmetns and the added value to the smart grid. Findings indicate that most business models focus on the end-costumer as their main customer.
Due to the consequential impact of technological breakdowns, companies have to be prepared to deal with breakdowns or even better prevent them. In today's information technology, several methods and tools exist to downscale this concern. Therefore, this paper deals with the initial determination of a resilient enterprise architecture supporting predictive maintenance in the information technology domain and furthermore, concerns several mechanisms on how to reactively and proactively secure the state of resiliency on several abstraction levels. The objective of this paper is to give an overview on existing mechanisms for resiliency and to describe the foundation of an optimized approach, combining infrastructure and process mining techniques.
While the concepts of object-oriented antipatterns and code smells are prevalent in scientific literature and have been popularized by tools like SonarQube, the research field for service-based antipatterns and bad smells is not as cohesive and organized. The description of these antipatterns is distributed across several publications with no holistic schema or taxonomy. Furthermore, there is currently little synergy between documented antipatterns for the architectural styles SOA and Microservices, even though several antipatterns may hold value for both. We therefore conducted a Systematic Literature Review (SLR) that identified 14 primary studies. 36 service-based antipatterns were extracted from these studies and documented with a holistic data model. We also categorized the antipatterns with a taxonomy and implemented relationships between them. Lastly, we developed a web application for convenient browsing and implemented a GitHub-based repository and workflow for the collaborative evolution of the collection. Researchers and practitioners can use the repository as a reference, for training and education, or for quality assurance.