Snapshot isolation for transactional stream processing

Transactional database systems and data stream management systems have been thoroughly investigated over the past decades. While both systems follow completely different data processing models, the combined concept of transactional stream processing promises to be the future data processing model. So far, however, it has not been investigated how well-known concepts found in DBMS or DSMS regarding multi-user support can be transferred to this model or how they need to be redesigned. In this paper, we propose a transaction model combining streaming and stored data as well as continuous and ad-hoc queries. Based on this, we present appropriate protocols for concurrency control of such queries guaranteeing snapshot isolation as well as for consistency of transactions comprising several shared states. In our evaluation, we show that our protocols represent a resilient and scalable solution meeting all requirements for such a model

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

Selective caching: a persistent memory approach for multi-dimensional index structures

After the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads

Non-crossing partitions

Non-crossing partitions have been a staple in combinatorics for quite some time. More recently, they have surfaced (sometimes unexpectedly) in various other contexts from free probability to classifying spaces of braid groups. Also, analogues of the non-crossing partition lattice have been introduced. Here, the classical non-crossing partitions are associated to Coxeter and Artin groups of type $\mathsf{A}_n$, which explains the tight connection to the symmetric groups and braid groups. We shall outline those developments.Comment: Survey article, 34 pages, 7 figure

Stability Analysis and Clustering of Electrical Transmission Systems

A proper understanding and modelling of the behaviour of heavily loaded large-scale electrical transmission systems is essential for a secure and uninterrupted operation. In this paper we present a descriptive analysis especially of low frequency oscillations within an electricity network and methods to assess the stability of the whole system based on an ARMAX model and the ESPRIT algorithm. Further we present two methods to separate the network into local areas, which is necessary for an efficient modelling of a large electrical system. The first method has its foundation in the results of the ARMAX based stability analysis and the second method concentrates on the network topology. In the last part of this paper we present an approach how an modelling of such local areas within an large electrical system based on stochastic differential equation models is possible

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Einsatz von Pseudo-Random-Arrays (PRA) für die objektive Perimetrie

One of the most important tasks in ophthalmology is the detection of thc visual field. The method therefore is called perimetry and the used devise is a permeter. Conventional perimeters work with subjective methods, where the patient has to signal the recognition of an applied light-stimulus by pressing a button. The cooperation of the patient is absolute necessary for a good result of the measurement. But, due to different causes, many patients can or will not cooperate that is why an objective method for perimetry is to be bound. A method of objective detection of the visual field is the usage of Visual Evoked Potentials (VHP). To reduce the measuring-time, multifocal stimulation has to be used. For multifucal Stimulation special sequences are necessary. A new form of sequences for perimetry is presented in this article

Adaptive SNR-Anhebung von VEP mit Statistik höherer Ordnung

Visual evoked potentials (VHP) are diagnostic features in assessment of the visual System. Because of very low SNR (Signal-lo-Noise Ratio) repetitive stimulus responses are averaged to obtain sufficient signal strength. Sucessive averaging is time consumptive. In multichannel KHG recordings we save the measurement time improving the SNR simultaneously by adaptive channel delay control. The adaptive algorithm is controlled by higher-order correlation coefficient, which is a scalar giving the similarity of multi-channel recordings

Verbesserung des SNR bei mehrkanaligen EEG-Ableitungen

The main problem in measurements of the focal VEP (Visual Evoked Potential) is its weak SNR (Signal-to-Noise Ratio). The most common methodfor enhancement of the SNR is the Stimulus synchronized averaging. For study ofsingle responses other ways in SNR improvement are needed. In this contribution a new method based on space-time selective measurement is introduced, which can be interpreted äs beaming a Signal source. Since the anatomical structures of sources generating the focal VEP are known in general and if the electrode positions are of sufficient density over the visual cortex, a source beamer can be realized by controlling the channels' delays