Big spatial data processing frameworks: feature and performance evaluation: experiments & analyses

Nowadays, a vast amount of data is generated and collected every moment and often, this data has a spatial and/or temporal aspect. To analyze the massive data sets, big data platforms like Apache Hadoop MapReduce and Apache Spark emerged and extensions that take the spatial characteristics into account were created for them. In this paper, we analyze and compare existing solutions for spatial data processing on Hadoop and Spark. In our comparison, we investigate their features as well as their performances in a micro benchmark for spatial filter and join queries. Based on the results and our experiences with these frameworks, we outline the requirements for a general spatio-temporal benchmark for Big Spatial Data processing platforms and sketch first solutions to the identified problems

Putting Pandas in a Box

Pandas - the Python Data Analysis Library - is a powerful and widely used framework for data analytics. In this work we present our approach to push down the computational part of Pandas scripts into the DBMS by using a transpiler. In addition to basic data processing operations, our approach also supports access to external data stored in files instead of the DBMS. Moreover, user-defined Python functions are transformed automatically to SQL UDFs executed in the DBMS. The latter allows the integration of complex computational tasks including machine learning. We show the usage of this feature to implement a so-called model join, i.e. applying pre-trained ML models to data in SQL tables

Processing large raster and vector data in apache spark

Spatial data processing frameworks in many cases are limited to vector data only. However, an important type of spatial data is raster data which is produced by sensors on satellites but also by high resolution cameras taking pictures of nano structures, such as chips on wafers. Often the raster data sets become large and need to be processed in parallel on a cluster environment. In this paper we demonstrate our STARK framework with its support for raster data and functionality to combine raster and vector data in filter and join operations. To save engineers from the burden of learning a programming language, queries can be formulated in SQL in a web interface. In the demonstration, users can use this web interface to inspect examples of raster data using our extended SQL queries on a Apache Spark cluster

The INFluence of Remote monitoring on Anxiety/depRession, quality of lifE, and Device acceptance in ICD patients: a prospective, randomized, controlled, single-center trial.

Leppert F, Siebermair J, Wesemann U, et al. The INFluence of Remote monitoring on Anxiety/depRession, quality of lifE, and Device acceptance in ICD patients: a prospective, randomized, controlled, single-center trial. Clinical research in cardiology : official journal of the German Cardiac Society. 2020.BACKGROUND: Impact of telemedicine with remote patient monitoring (RPM) in implantable cardioverter-defibrillator (ICD) patients on clinical outcomes has been investigated in various clinical settings with divergent results. However, role of RPM on patient-reported-outcomes (PRO) is unclear. The INFRARED-ICD trial aimed to investigate the effect of RPM in addition to standard-of-care on PRO in a mixed ICD patient cohort.; METHODS AND RESULTS: Patients were randomized to RPM (n=92) or standard in-office-FU (n=88) serving as control group (CTL). At baseline and on a monthly basis over 1 year, study participants completed the EQ-5D questionnaire for the primary outcome Quality of Life (QoL), the Hospital Anxiety and Depression Scale, and the Florida Patient Acceptance Survey questionnaire for secondary outcomes. Demographic characteristics (82% men, mean age 62.3years) and PRO at baseline were not different between RPM and CTL. Primary outcome analysis showed that additional RPM was not superior to CTL with respect to QoL over 12months [+1.2 vs.+3.9 points in CTL and RPM group, respectively (p=0.24)]. Pre-specified analyses could not identify subgroups with improved QoL by the use of RPM. Neither levels of anxiety (-0.4 vs. -0.3, p=0.88), depression (+0.3 vs.±0.0, p=0.38), nor device acceptance (+1.1 vs.+1.6, p=0.20) were influenced by additional use of RPM.; CONCLUSION: The results of the present study show that PRO were not improved by RPM in addition to standard-of-care FU. Careful evaluation and planning of future trials in selected ICD patients are warranted before implementing RPM in routine practice

Low-tech solution for Smart Cities – Optimization tool CityCalc for solar urban design

Designed as an easily applicable planning and evaluation tool, CityCalc has been developed to assess the energy performance of urban planning projects at early design stages. The tool supports the development of low-tech solutions for smart cities by means of optimising the use of renewable energy on site – including passive and active solar gains. Currently energy planning and assessment tools for early design stages do not take into account the mutual interactions of buildings such as shading and shadowing from adjoining structures as their focus is on the individual buildings. A great variety of tools for urban solar design exist nowadays, however they are not suitable for architects and early design stages (IEA SHC Task 41). In the future it will be of increasing importance to quantify the passive and active solar gains in order to fulfil ambitious legal and funding requirement and to implement future-oriented building concepts (e.g. passive house, zero energy, zero carbon or plus energy standards). The objective was therefore to develop an easily applicable energy planning and assessment tool for urban planning projects for the early design stages. The CityCalc tool focuses on energy efficiency - that is, the reduction of energy demand - with the best possible use of site-specific energy sources (gains from solar thermal and photovoltaic plants, wind energy, combined heat and power). In order to ensure a simple, user-friendly usability for architects, a three-dimensional geometry and data acquisition and an interface with energy calculation software is developed. CityCalc is developed for urban development planning, urban design competitions and urban densification. CityCalc can be used on the one hand by architects for optimizing the conceptual design phase and on the other hand, for the energy assessment of urban planning and architectural competitions. CityCalc combines the simplistic three-dimensional geometry input method of the freely available software SketchUp with proven evaluation algorithms of the energy performance certificate. In addition it refers to a variety of default values for details, which are not defined in detail at this stage of planning. With the assessment tool CityCalc it is possible to assess the potential of active and passive use of solar energy at a very early planning stage. For this purpose, the simplified three-dimensional input of the building and its surroundings in the free software SketchUp is required. CityCalc is available as a plugin for SketchUp. The developed planning and assessment tool has been tested and validated in selected planning competitions and early design projects. The tool and the experiences of the validation will be presented in this paper. Conclusions are a well-adjusted applicability for an early design stage. System boundaries of the assessment have to be shaped based on the available information as well as the flexible parameters of early design stages. Further aspects of smart cities have been identified to be included in future upgrades of the tool, such as: daylight comfort of indoor and outdoor areas, costs for supply and disposal especially energy supply, embodied energy in materials. The project has been funded by the Austrian Ministry for Transport, Innovation and Technology (bmvit) within the research program ‘City of Tomorrow’ (Stadt der Zukunft)

Ventricular Arrhythmias in First Acute Myocardial Infarction:Epidemiology, Mechanisms, and Interventions in Large Animal Models

Ventricular arrhythmia and subsequent sudden cardiac death (SCD) due to acute myocardial infarction (AMI) is one of the most frequent causes of death in humans. Lethal ventricular arrhythmias like ventricular fibrillation (VF) prior to hospitalization have been reported to occur in more than 10% of all AMI cases and survival in these patients is poor. Identification of risk factors and mechanisms for VF following AMI as well as implementing new risk stratification models and therapeutic approaches is therefore an important step to reduce mortality in people with high cardiovascular risk. Studying spontaneous VF following AMI in humans is challenging as it often occurs unexpectedly in a low risk subgroup. Large animal models of AMI can help to bridge this knowledge gap and are utilized to investigate occurrence of arrhythmias, involved mechanisms and therapeutic options. Comparable anatomy and physiology allow for this translational approach. Through experimental focus, using state-of-the-art technologies, including refined electrical mapping equipment and novel pharmacological investigations, valuable insights into arrhythmia mechanisms and possible interventions for arrhythmia-induced SCD during the early phase of AMI are now beginning to emerge. This review describes large experimental animal models of AMI with focus on first AMI-associated ventricular arrhythmias. In this context, epidemiology of first AMI, arrhythmogenic mechanisms and various potential therapeutic pharmacological targets will be discussed