This thesis addresses the challenges of large software and data-intensive systems. We will discuss a Big Data software that consists of quite a bit of Linux configuration, some Scala coding and a set of frameworks that work together to achieve the smooth performance of the system. Moreover, the thesis focuses on the Apache Spark framework and the challenging of measuring the lazy evaluation of the transformation operations of Spark. Investigating the challenges are essential for the performance engineers to increase their ability to study how the system behaves and take decisions in early design iteration. Thus, we made some experiments and measurements to achieve this goal. In addition to that, and after analyzing the result we could create a formula that will be useful for the engineers to predict the performance of the system in production.

The purpose of this research was to determine if significant differences exist between the APACHE II scores of intubated mechanically ventilated patients who ultimately received a tracheostomy and those who did not. In addition to this inquiry, the study also investigated the possibility of a range of APACHE II scores, a particular age group, and the presence of chronic organ insufficiencies and their relationship to the tracheostomy result. Methodology was non-experimental, quantitative, and retrospective. It was observational in that the goal was to simply record and quantify the potential association between these variables. Data was obtained from patients at Bristol Regional Medical Center from January 1- August 31, 2011. Information was calculated using descriptive statistics and the t-test for independent samples. Participants included all intubated mechanically ventilated patients who were at least eighteen years of age with a documented APACHE II score in the allotted time frame. There were 468 total patients, 79 (16.9%) of which received a tracheostomy. The mean APACHE II score for patients who received a tracheostomy was 21.8354 as compared to the mean APACHE II score of 21.6735 for those who were extubated. There was no significant difference between the APACHE II scores of these groups. The tracheostomy group had the highest frequency of patients with APACHE II scores of less than 25 and a range of 20-29. 84.8% of tracheostomy patients had some form of chronic organ dysfunction. Respiratory failure was the most frequent admitting diagnosis for all 468 patients and respiratory insufficiency was the most prevalent co-morbidity for the tracheostomy patients. The age range that included more tracheostomy patients was 65-74. 40% of re-intubated patients eventually received a tracheostomy and 69.6% of tracheostomy patients had the procedure performed early (within the first seven days of intubation). The managerial team of this respiratory therapy department decided to stop calculating the APACHE II score on all intubated patients in an attempt to save time and staff resources.

abstract: Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install Global Positioning System(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) Granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. This paper proposed GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 200 thousand cars over an extensive road network (250 thousand road junctions and 300 thousand road segments). / Dissertation/Thesis / Masters Thesis Computer Engineering 2019

Outlier detection remains a heated area due to its essential role in a wide range of applications, including intrusion detection, fraud detection in finance, medical diagnosis, etc. Local Outlier Factor (LOF) has been one of the most influential outlier detection techniques over the past decades. LOF has distinctive advantages on skewed datasets with regions of various densities. However, the traditional centralized LOF faces new challenges in the era of big data and no longer satisfies the rigid time constraints required by many modern applications, due to its expensive computation overhead. A few researchers have explored the distributed solution of LOF, but existant methods are limited by their grid-based data partitioning strategy, which falls short when applied to high-dimensional data. In this thesis, we study efficient distributed solutions for LOF. A baseline MapReduce solution for LOF implemented with Apache Spark, named MR-LOF, is introduced. We demonstrate its disadvantages in communication cost and execution time through complexity analysis and experimental evaluation. Then an approximate LOF method is proposed, which relies on locality-sensitive hashing (LSH) for partitioning data and enables fully distributed local computation. We name it MR-LOF-LSH. To further improve the approximate LOF, we introduce a process called cross-partition updating. With cross-partition updating, the actual global k-nearest neighbors (k-NN) of the outlier candidates are found, and the related information of the neighbors is used to update the outlier scores of the candidates. The experimental results show that MR-LOF achieves a speedup of up to 29 times over the centralized LOF. MR-LOF-LSH further reduces the execution time by a factor of up to 9.9 compared to MR-LOF. The results also highlight that MR-LOF-LSH scales well as the cluster size increases. Moreover, with a sufficient candidate size, MR-LOF-LSH is able to detect in most scenarios over 90% of the top outliers with the highest LOF scores computed by the centralized LOF algorithm.

Processing real-world data requires the ability to analyze data in real-time. Data processing engines like Hadoop come short when results are needed on the fly. Apache Spark's streaming library is increasingly becoming a popular choice as it can stream and analyze a significant amount of data. To showcase and assess the ability of Spark various metrics were designed and operated using data collected from the USGODAE data catalog. The latency of streaming in Apache Spark was measured and analyzed against many nodes in the cluster. Scalability was monitored by adding and removing nodes in the middle of a streaming job. Fault tolerance was verified by stopping nodes in the middle of a job and making sure that the job was rescheduled and completed on other node/s. A full stack application was designed that would automate data collection, data processing and visualizing the results. Google Maps API was used to visualize results by color coding the world map with values from various analytics.

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Gemeinsamer Workshop von Universitaetsrechenzentrum und Professur Rechnernetze und verteilte Systeme (Fakultaet fuer Informatik) der TU Chemnitz. Workshop-Thema: Infrastruktur der ¨Digitalen Universitaet¨ Es gibt viele Möglichkeiten, dynamische Web Inhalte zu erzeugen. Dieser Vortrag soll einen Überblick über Erweiterungsmöglichkeiten auf der Serverseite geben. Mit Hinblick auf Performance werden im Vergleich zum CGI eingebettete Interpreter beleuchtet und spezielle Scriptsprachen wie PHP,Perl oder Tcl genannnt.

Between 1500 and 1800, Spaniards and their Native allies captured hundreds of Apache Indians and members of neighboring groups from the Rio Grande River Basin and subjected them to a variety of fates. They bought and sold some captives as slaves, exiled others as prisoners of war to central Mexico and Cuba, and forcibly moved others to mines, towns, and haciendas as paid or unpaid laborers. Though warfare and captive exchange predated the arrival of Europeans to North America, the three centuries following contact witnessed the development of new practices of violence and captivity in the North American West fueled by Euroamericans’ interest in Native territory and labor, on the one hand, and the dispersal of new technologies like horses and guns to American Indian groups, on the other. While at times subject to an enslavement and property status resembling chattel slavery, Native peoples of the Greater Rio Grande often experienced captivities and forced migrations fueled more by the interests of empires and nation-states in their territory and sovereignty than by markets in human labor. / text

Shows handicapped pre-school children at the White Mountain Apache Head Start Program and the services available at Head Start.

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