Hasso-Plattner-Institut für Digital Engineering gGmbH
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Given a query record, record matching is the problem of finding database records that represent the same real-world object. In the easiest scenario, a database record is completely identical to the query. However, in most cases, problems do arise, for instance, as a result of data errors or data integrated from multiple sources or received from restrictive form fields. These problems are usually difficult, because they require a variety of actions, including field segmentation, decoding of values, and similarity comparisons, each requiring some domain knowledge. In this article, we study the problem of matching records that contain address information, including attributes such as Street-address and City. To facilitate this matching process, we propose a domain-specific procedure to, first, enrich each record with a more complete representation of the address information through geocoding and reverse-geocoding and, second, to select the best similarity measure per each address attribute that will finally help the classifier to achieve the best f-measure. We report on our experience in selecting geocoding services and discovering similarity measures for a concrete but common industry use-case.
Wendepunkt für Gesundheit
(2019)
Die Zukunft der Medizin
(2019)
Die Medizin im 21. Jahrhundert wird sich so schnell verändern wie nie zuvor – und mit ihr das Gesundheitswesen. Bahnbrechende Entwicklungen in Forschung und Digitalisierung werden die Auswertung und Nutzung riesiger Datenmengen in kurzer Zeit ermöglichen. Das wird unsere Kenntnisse über Gesundheit und gesund sein, sowie über die Entstehung, Prävention und Heilung von Krankheiten vollkommen verändern. Gleichzeitig wird sich die Art und Weise, wie Medizin praktiziert wird, fundamental verändern. Das Selbstverständnis nahezu aller Akteure wird sich rasch weiterentwickeln müssen. Das Gesundheitssystem wird in allen Bereichen umgebaut und teilweise neu erfunden werden. Digitale Transformation, Personalisierung und Prävention sind die Treiber der neuen Medizin.
Deutschland darf den Anschluss nicht verpassen. Im Vergleich mit anderen Ländern ist das deutsche Gesundheitswesen in vielen Punkten bedrohlich rückständig und fragmentiert. Um die Medizin und das Gesundheitswesen in Deutschland langfristig zukunftsfest zu machen, bedarf es vieler Anstrengungen – vor allem aber Offenheit gegenüber Veränderungen, sowie einen regulatorischen Rahmen, der ermöglicht, dass die medizinischen und digitalen Innovationen beim Patienten ankommen.
DIE ZUKUNFT DER MEDIZIN beschreibt Entwicklungen und Technologien, die die Medizin und das Gesundheitswesen im 21. Jahrhundert prägen werden. Das Buch informiert über die zum Teil dramatischen, disruptiven Innovationen in der Forschung, die durch Big Data, Künstliche Intelligenz und Robotik möglich werden.
Die Autoren sind führende Vordenker ihres Fachs und beschreiben aus langjähriger Erfahrung im In- und Ausland zukünftige Entwicklungen, die jetzt bereits greifbar sind.
Background Heart failure (HF) is a complex, chronic condition that is associated with debilitating symptoms, all of which necessitate close follow-up by health care providers. Lack of disease monitoring may result in increased mortality and more frequent hospital readmissions for decompensated HF. Remote patient management (RPM) in this patient population may help to detect early signs and symptoms of cardiac decompensation, thus enabling a prompt initiation of the appropriate treatment and care before a manifestation of HF decompensation. Objective The objective of the present article is to describe the design of a new trial investigating the impact of RPM on unplanned cardiovascular hospitalisations and mortality in HF patients. Methods The TIM-HF2 trial is designed as a prospective, randomised, controlled, parallel group, open (with randomisation concealment), multicentre trial with pragmatic elements introduced for data collection. Eligible patients with HF are randomised (1:1) to either RPM + usual care or to usual care only and are followed for 12 months. The primary outcome is the percentage of days lost due to unplanned cardiovascular hospitalisations or all-cause death. The main secondary outcomes are all-cause and cardiovascular mortality. Conclusion The TIM-HF2 trial will provide important prospective data on the potential beneficial effect of telemedical monitoring and RPM on unplanned cardiovascular hospitalisations and mortality in HF patients.
Applications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk and resources of the data center without affecting the service quality of applications.
Graphs are ubiquitous in computer science. Moreover, in various application fields, graphs are equipped with attributes to express additional information such as names of entities or weights of relationships. Due to the pervasiveness of attributed graphs, it is highly important to have the means to express properties on attributed graphs to strengthen modeling capabilities and to enable analysis. Firstly, we introduce a new logic of attributed graph properties, where the graph part and attribution part are neatly separated. The graph part is equivalent to first-order logic on graphs as introduced by Courcelle. It employs graph morphisms to allow the specification of complex graph patterns. The attribution part is added to this graph part by reverting to the symbolic approach to graph attribution, where attributes are represented symbolically by variables whose possible values are specified by a set of constraints making use of algebraic specifications. Secondly, we extend our refutationally complete tableau-based reasoning method as well as our symbolic model generation approach for graph properties to attributed graph properties. Due to the new logic mentioned above, neatly separating the graph and attribution parts, and the categorical constructions employed only on a more abstract level, we can leave the graph part of the algorithms seemingly unchanged. For the integration of the attribution part into the algorithms, we use an oracle, allowing for flexible adoption of different available SMT solvers in the actual implementation. Finally, our automated reasoning approach for attributed graph properties is implemented in the tool AutoGraph integrating in particular the SMT solver Z3 for the attribute part of the properties. We motivate and illustrate our work with a particular application scenario on graph database query validation.
High-dimensional data is particularly useful for data analytics research. In the healthcare domain, for instance, high-dimensional data analytics has been used successfully for drug discovery. Yet, in order to adhere to privacy legislation, data analytics service providers must guarantee anonymity for data owners. In the context of high-dimensional data, ensuring privacy is challenging because increased data dimensionality must be matched by an exponential growth in the size of the data to avoid sparse datasets. Syntactically, anonymising sparse datasets with methods that rely of statistical significance, makes obtaining sound and reliable results, a challenge. As such, strong privacy is only achievable at the cost of high information loss, rendering the data unusable for data analytics. In this paper, we make two contributions to addressing this problem from both the privacy and information loss perspectives. First, we show that by identifying dependencies between attribute subsets we can eliminate privacy violating attributes from the anonymised dataset. Second, to minimise information loss, we employ a greedy search algorithm to determine and eliminate maximal partial unique attribute combinations. Thus, one only needs to find the minimal set of identifying attributes to prevent re-identification. Experiments on a health cloud based on the SAP HANA platform using a semi-synthetic medical history dataset comprised of 109 attributes, demonstrate the effectiveness of our approach.
Kyub
(2019)
We present an interactive editing system for laser cutting called kyub. Kyub allows users to create models efficiently in 3D, which it then unfolds into the 2D plates laser cutters expect. Unlike earlier systems, such as FlatFitFab, kyub affords construction based on closed box structures, which allows users to turn very thin material, such as 4mm plywood, into objects capable of withstanding large forces, such as chairs users can actually sit on. To afford such sturdy construction, every kyub project begins with a simple finger-joint "boxel"-a structure we found to be capable of withstanding over 500kg of load. Users then extend their model by attaching additional boxels. Boxels merge automatically, resulting in larger, yet equally strong structures. While the concept of stacking boxels allows kyub to offer the strong affordance and ease of use of a voxel-based editor, boxels are not confined to a grid and readily combine with kuyb's various geometry deformation tools. In our technical evaluation, objects built with kyub withstood hundreds of kilograms of loads. In our user study, non-engineers rated the learnability of kyub 6.1/7.
