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Die Verarbeitung von Datenströmen rückt zunehmend in den Fokus beim Aufbau moderner Big Data Infrastrukturen. Der Praxispartner dieser Master-Thesis, die integrationfactory GmbH & Co. KG, möchte zunehmend den Big Data Bereich ausbauen, um den Kunden auch in diesen Aspekten als Beratungshaus Unterstützung bieten zu können. Der Fokus wurde von Anfang an auf Apache Flink gelegt, einem aufstrebenden Stream-Processing-Framework. Das Ziel dieser Arbeit ist die Implementierung verschiedener typischer Anwendungsfälle des Unternehmens mithilfe von Flink und die anschließende Evaluierung dieser. Im Rahmen dessen wird am Anfang zunächst die zentrale Problemstellung festgehalten und daraus die Zielstellungen abgeleitet. Zum besseren Verständnis werden im Nachgang wichtige Grundbegriffe und Konzepte vermittelt. Es wird außerdem dem Framework ein eigenes Kapitel gewidmet, um den Leser einen umfangreichen aber dennoch kompakten Einblick in Flink zu geben. Dabei wurde auf verschiedene Quellen eingegangen, mitunter wurde auch ein direkter Kontakt mit aktiven Entwicklern des Frameworks aufgebaut. Dadurch konnten zunächst unklare Sachverhalte durch fehlende Informationen aus den Primärquellen im Nachgang geklärt und aufbereitet in das Kapitel hinzugefügt werden. Im Hauptteil der Arbeit wird eine Implementierung von definierten Anwendungsfällen vorgenommen. Dabei kommen die Datastream-API und FlinkSQL zum Einsatz, dessen Auswahl auch begründet wird. Die Ausführung der programmierten Jobs findet im firmeneigenen Big Data Labor statt, einer virtualisierten Umgebung zum Testen von Technologien. Als zentrales Problem dieser Master-Thesis sollen beide Schnittstellen auf die Eignung hinsichtlich der Anwendungsfälle evaluiert werden. Auf Basis des Wissens aus den Grundlagen-Kapiteln und der Erfahrungen aus der Entwicklung der Jobs werden Kriterien zur Bewertung mithilfe des Analytic Hierarchy Processes aufgestellt. Im Nachgang findet eine Auswertung statt und die Einordnung des Ergebnisses.:1. Einleitung 1.1. Motivation 1.2. Problemstellung 1.3. Zielsetzung 2. Grundlagen 2.1. Begriffsdefinitionen 2.1.1. Big Data 2.1.2. Bounded vs. unbounded Streams 2.1.3. Stream vs. Tabelle 2.2. Stateful Stream Processing 2.2.1. Historie 2.2.2. Anforderungen 2.2.3. Pattern-Arten 2.2.4. Funktionsweise zustandsbehafteter Datenstromverarbeitung 3. Apache Flink 3.1. Historie 3.2. Architektur 3.3. Zeitabhängige Verarbeitung 3.4. Datentypen und Serialisierung 3.5. State Management 3.6. Checkpoints und Recovery 3.7. Programmierschnittstellen 3.7.1. DataStream-API 3.7.2. FlinkSQL & Table-API 3.7.3. Integration mit Hive 3.8. Deployment und Betrieb 4. Implementierung 4.1. Entwicklungsumgebung 4.2. Serverumgebung 4.3. Konfiguration von Flink 4.4. Ausgangsdaten 4.5. Anwendungsfälle 4.6. Umsetzung in Flink-Jobs 4.6.1. DataStream-API 4.6.2. FlinkSQL 4.7. Betrachtung der Resultate 5. Evaluierung 5.1. Analytic Hierarchy Process 5.1.1. Ablauf und Methodik 5.1.2. Phase 1: Problemstellung 5.1.3. Phase 2: Struktur der Kriterien 5.1.4. Phase 3: Aufstellung der Vergleichsmatrizen 5.1.5. Phase 4: Bewertung der Alternativen 5.2. Auswertung des AHP 6. Fazit und Ausblick 6.1. Fazit 6.2. Ausblick

The goal of this thesis is to design architecture capable of processing big data with focus on data leaks. For this purpose multiple data storage systems were described a and compared. The proposed architecture can load, process, store and access data for analytic purposes while taking into account authentication and authorisation of users and principles of modern agile infrastructure.

Web servers often become overloaded with irregular surges in web traffic. Several techniques have been explored to cope with these overloads such as distributing load throughout different servers. This thesis presents Pipelined Apache HTTP Server, a modified version of the Apache Software Foundation’s HTTP Server that utilizes a pipelined execution of Apache’s request cycle. We discuss Apache’s original architecture, the modifications necessary for implementation of pipelined execution, and analyze its run time. Ultimately, we hoped to increase throughput of Apache but fall short because of unbalanced request phases and pipelining overhead.

This master's thesis is focused on developement of billing system with the options of monitoring individual calls for software exchange Asterisk. Billing of calls is adaptible with the help of group of individual rules, consisting of tariff impulses, numerical prefix, with help of outgoing trunk and cost of the billed unit. The first part of this work is focused on instalation, configuration and preparation of individual components of the billing system. In this work is explained the architecture of the billing system and highlighted the purpose of work of the model database. Next we focused on the purpose and the principal system invidual function of the system including solution. At last there is a simple manual to operate the system with the help of created web interface.

This thesis deals with the management of computer servers. The theoretical part describes the client-server architecture, operating system GNU/Linux and server management tools. The second chapter is devoted to design an own application to manage servers. The next chapter describes the design and implementation of individual modules from which application is build. In the fourth chapter there is described how to install and configure the application on the GNU/Linuw operating system. The penultimate chapter is written as a user manual and acquaints reader with application control. In the last section there are results obtained in testing the application presented and commented.

Sampling is considered to be a core component of data analysis making it possibleto provide a synopsis of possibly large amounts of data by maintainingonly subsets or multisubsets of it. In the context of data streaming, an emergingprocessing paradigm where data is assumed to be unbounded, samplingoffers great potential since it can establish a representative bounded view ofinfinite data streams to any streaming operations. This further unlocks severalbenefits such as sustainable continuous execution on managed memory, trendsensitivity control and adaptive processing tailored to the operations that consumedata streams.The main aim of this thesis is to conduct an experimental study in order tocategorize existing sampling techniques over a selection of properties derivedfrom common streaming use cases. For that purpose we designed and implementeda testing framework that allows for configurable sampling policiesunder different processing scenarios along with a library of different samplersimplemented as operators. We build on Apache Flink, a distributed streamprocessing system to provide this testbed and all component implementationsof this study. Furthermore, we show in our experimental analysis that there isno optimal sampling technique for all operations. Instead, there are differentdemands across usage scenarios such as online aggregations and incrementalmachine learning. In principle, we show that each sampling policy trades offbias, sensitivity and concept drift adaptation, properties that can be potentiallypredefined by different operators.We believe that this study serves as the starting point towards automatedadaptive sampling selection for sustainable continuous analytics pipelines thatcan react to stream changes and thus offer the right data needed at each time,for any possible operation

La neumonía es una de las complicaciones más frecuentes de la ventilación mecánica, la Neumonía Asociada a Ventilación Mecánica (NAV) con una mortalidad muy elevada que oscila entre el 55 % a 71 %, se convierte en una patología que complica el estado de salud del paciente, que, a pesar de las numerosas estrategias aplicadas para reducir su alta incidencia, ni las complicaciones ni su mortalidad se han logrado disminuir en forma considerable. Con el fin de determinar la relación de la puntuación de gravedad del Score APACHE II de ingreso con la presentación de Neumonía Asociada a Ventilación Mecánica en la Unidad de Cuidados Intensivos del Hospital Nacional Daniel Alcides Carrión, Callao en el periodo comprendido entre el 1 de julio del 2011 al 31 de diciembre del 2011, se reclutaron en total de 93 pacientes, de los cuales 18 presentaron neumonía asociada a ventilación, de los cuales 6 presentaron neumonía asociada a ventilador temprana 6 pacientes y tardía 12 pacientes el valor de gravedad de score APACHE II al ingreso fue de 20 y el que presentaron al momento del diagnóstico de neumonía asociada a ventilación fue de 14. Entre las comorbilidades más frecuentes en los pacientes que presentaron neumonía asociada a ventilación los factores destaco la diabetes mellitus - 2 y las enfermedades neurológicas; entre los factores intervinientes destacaron la presencia secreciones bronquiales, la sedo relajación y el estado postquirúrgico. En la microbiología asociada a la neumonía asociada a ventilación se encontró a la Pseudomona sp y el Acinetobacter sp. Se concluyó que la gravedad de severidad del score APACHE II por sí solo no es un buen predictor de neumonía asociada a ventilación. / Trabajo académico

Indiana University-Purdue University Indianapolis (IUPUI) / Network analysis is an important research task which explains the relationships among various entities in a given domain. Most of the existing approaches of network analysis compute global properties of a network, such as transitivity, diameter, and all-pair shortest paths. They also study various non-random properties of a network, such as graph densifi cation with shrinking diameter, small diameter, and scale-freeness. Such approaches enable us to understand real-life networks with global properties. However, the discovery of the local topological building blocks within a network is an important task, and examples include clique enumeration, graphlet counting, and motif counting. In this paper, my focus is to fi nd an efficient solution of k-clique enumeration problem. A clique is a small, connected, and complete induced subgraph over a large network. However, enumerating cliques using sequential technologies is very time-consuming. Another promising direction that is being adopted is a solution that runs on distributed clusters of machines using the Hadoop mapreduce framework. However, the solution suffers from a general limitation of the framework, as Hadoop's mapreduce performs substantial amounts of reading and writing to disk. Thus, the running times of Hadoop-based approaches suffer enormously. To avoid these problems, we propose an e cient, scalable, and distributed solution, kc-spark , for enumerating cliques in real-life networks using the Apache Spark in-memory cluster computing framework. Experiment results show that kc-spark can enumerate k-cliques from very large real-life networks, whereas a single commodity machine cannot produce the same desired result in a feasible amount of time. We also compared kc-spark with Hadoop mapreduce solutions and found the algorithm to be 80-100 percent faster in terms of running times. On the other hand, we compared with the triangle enumeration with Hadoop mapreduce and results shown that kc-spark is 8-10 times faster than mapreduce implementation with the same cluster setup. Furthermore, the overall performance of kc-spark is improved by using Spark's inbuilt caching and broadcast transformations.