Calculate the Pareto frontier with minimum arm energy ripple and conduction loss of an MMC when the second and fourth harmonic of the circulating current is used as free parameters. ParetoMMC attempts to solve min F(X, lambda), X where F(X, lambda) = E_ripple(X)*lambda + P_loss*(1 - lambda). X denotes the amplitudes and phases of the second and fourth harmonic of the circulating current. lambda is the weighting scalar in the range 0 <= lamda <= 1. The MMC dc side is connected to a dc voltage source, while the ac side is a symmetric three phase voltage with isolated star point. A third harmonic in the common mode voltage is assumed.:ParetoMMC.m F_eval.m LICENSE.GNU_AGPLv3

DBMS are a ubiquitous building block of the software stack in many complex applications. Middleware technologies, application servers and mapping approaches hide the core database technologies just like power, networking infrastructure and operating system services. Furthermore, many enterprise-critical applications demand a certain degree of quality of service (QoS) or guarantees, e.g. wrt. response time, transaction throughput, latency but also completeness or more generally quality of results. Examples of such applications are billing systems in telecommunication, where each telephone call has to be monitored and registered in a database, Ecommerce applications where orders have to be accepted even in times of heavy load and the waiting time of customers should not exceed a few seconds, ERP systems processing a large number of transactions in parallel, or systems for processing streaming or sensor data in realtime, e.g. in process automation of traffic control. As part of complex multilevel software stack, database systems have to share or contribute to these QoS requirements, which means that guarantees have to be given by the DBMS, too, and that the processing of database requests is predictable. Todays mainstream DBMS typically follow a best effort approach: requests are processed as fast as possible without any guarantees: the optimization goal of query optimizers and tuning approaches is rather to minimize resource consumption instead of just fulfilling given service level agreements. However, motivated by the situation described above there is an emerging need for database services providing guarantees or simply behave in a predictable manner and at the same time interact with other components of the software stack in order to fulfill the requirements. This is also driven by the paradigm of service-oriented architectures widely discussed in industry. Currently, this is addressed only by very specialized solutions. Nevertheless, database researchers have developed several techniques contributing to the goal of QoS-aware database systems. The purpose of the tutorial is to introduce database researchers and practitioners to the scope, the challenges and the available techniques to the problem of predictability and QoS agreements in DBMS.

There are many more ways to automate the application process like using some commercial software’s that are used in big organizations to scan bills and forms, but this application is only for the static frames or formats. In our application, we are trying to automate the non-static frames as the study certificate we get are from different counties with different universities. Each and every university have there one format of certificates, so we try developing a very new application that can commonly work for all the frames or formats. As we observe many applicants are from same university which have a common format of the certificate, if we implement this type of tools, then we can analyze this sort of certificates in a simple way within very less time. To make this process more accurate we try implementing SVM and Clustering methods. With these methods we can accurately map courses in certificates to ASE study path if not to exclude list. A grade calculation is done for courses which are mapped to an ASE list by separating the data for both labs and courses in it. At the end, we try to award some points, which includes points from ASE related courses, work experience, specialization certificates and German language skills. Finally, these points are provided to the chair to select the applicant for master course ASE.

Der Kartendienst OpenStreetMap ist einer der beliebtesten Anbieter für OpenData-Karten. Diese Karten konzentrieren sich jedoch derzeitig auf Außenraumumgebungen, da sich bereits existierende Ansätze zur Innenraumkartografierung nicht durchsetzen konnten. Als einer der Hauptgründe wird die mangelnde Unterstützung der verbreiteten Karteneditoren angesehen. Die vorliegende Bachelorarbeit befasst sich daher mit der Implementierung eines Plug-Ins für die Erstellung von Innenraumkarten im Editor „JOSM“, und dem Vergleich des Arbeitsaufwandes zur Innenraumkartenerstellung mit und ohne diesem Hilfsmittel.

Data processing on a continuously growing amount of information and the increasing power restrictions have become an ubiquitous challenge in our world today. Besides parallel computing, a promising approach to improve the energy efficiency of current systems is to integrate specialized hardware. This paper presents a Tensilica RISC processor extended with an instruction set to accelerate basic database operators frequently used in modern database systems. The core was taped out in a 28 nm SLP CMOS technology and allows energy-efficient query processing as well as query optimization by applying selectivity estimation techniques. Our chip measurements show an 1000x energy improvement on selected database operators compared to state-of-the-art systems.

Since data sizes of analytical applications are continuously growing, many data scientists are switching from customized micro-solutions to scalable alternatives, such as statistical and scientific databases. However, many algorithms in data mining and science are expressed in terms of linear algebra, which is barely supported by major database vendors and big data solutions. On the other side, conventional linear algebra algorithms and legacy matrix representations are often not suitable for very large matrices. We propose a strategy for large matrix processing on modern multicore systems that is based on a novel, adaptive tile matrix representation (AT MATRIX). Our solution utilizes multiple techniques inspired from database technology, such as multidimensional data partitioning, cardinality estimation, indexing, dynamic rewrites, and many more in order to optimize the execution time. Based thereon we present a matrix multiplication operator ATMULT, which outperforms alternative approaches. The aim of our solution is to overcome the burden for data scientists of selecting appropriate algorithms and matrix storage representations. We evaluated AT MATRIX together with ATMULT on several real-world and synthetic random matrices.

Data as well as hardware characteristics are two key aspects for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, efficient in-memory processing benefits from novel processing concepts based on lightweight compressed data. Thus, an active research field deals with the adaptation of new hardware features such as vectorization using SIMD instructions to speedup lightweight data compression algorithms. Following this trend, we propose a novel approach for run-length encoding, a well-known and often applied lightweight compression technique. Our novel approach is based on newly introduced conflict detection (CD) instructions in Intel's AVX-512 instruction set extension. As we are going to show, our CD-based approach has unique properties and outperforms the state-of-the-art RLE approach for data sets with small run lengths.

The forecasting of time series data is an integral component for management, planning, and decision making. Following the Big Data trend, large amounts of time series data are available in many application domains. The highly dynamic and often noisy character of these domains in combination with the logistic problems of collecting data from a large number of data sources, imposes new requirements on the forecasting process. A constantly increasing number of time series has to be forecasted, preferably with low latency AND high accuracy. This is almost impossible, when keeping the traditional focus on creating one forecast model for each individual time series. In addition, often used forecasting approaches like ARIMA need complete historical data to train forecast models and fail if time series are intermittent. A method that addresses all these new requirements is the cross-sectional forecasting approach. It utilizes available data from many time series of the same domain in one single model, thus, missing values can be compensated and accurate forecast results can be calculated quickly. However, this approach is limited by a rigid training data selection and existing forecasting methods show that adaptability of the model to the data increases the forecast accuracy. Therefore, in this paper we present CSAR a model that extends the cross-sectional paradigm by adding more flexibility and allowing fine grained adaptations to the analyzed data. In this way, we achieve an increased forecast accuracy and thus a wider applicability.

Data clustering is a highly used knowledge extraction technique and is applied in more and more application domains. Over the last years, a lot of algorithms have been proposed that are often complicated and/or tailored to specific scenarios. As a result, clustering has become a hardly accessible domain for non-expert users, who face major difficulties like algorithm selection and parameterization. To overcome this issue, we develop a novel feedback-driven clustering process using a new perspective of clustering. By substituting parameterization with user-friendly feedback and providing support for result interpretation, clustering becomes accessible and allows the step-by-step construction of a satisfying result through iterative refinement.

With rapidly increasing datasets and more dynamic workloads, adaptive partial indexing becomes an important way to keep indexing efficiently. During times of changing workloads, the query performance suffers from inefficient tables scans while the index tuning mechanism adapts the partial index. In this paper we present the Adaptive Index Buffer. The Adaptive Index Buffer reduces the cost of table scans by quickly indexing tuples in memory until the partial index has adapted to the workload again. We explain the basic operating mode of an Index Buffer and discuss how it adapts to changing workload situations. Further, we present three experiments that show the Index Buffer at work.