big data analytics framework (13 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Master's Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 7, Queensland University of Technology (Faculty of Science and Engineering), course: Master in Business Process Management, language: English, abstract: This thesis investigates the project management approach for big data projects for industry partner Red Rocks Company. The aim of this project is to understand best practice project management for big data initiatives and to develop a framework to help such projects to deliver the expected advantages. A brief literature review is undertaken to find out how big data projects are managed. From this, a Big Data Analytics Framework is derived which is based on CRISP-DM. The framework is validated through interviews with stakeholders from the corporate sector. For this case study, the first three phases of the Business Process Management Lifecycle are applied: process discovery, analysis and design. Key findings of the case study are that literature recommends an agile project management approach for big data initiatives. On the contrary, the majority of interviewed industry stakeholders confirmes a waterfall approach is conducted more often to deliver such projects. The developed Big Data Analytics Framework will add significant benefits to Red Rocks Company as it will help to successfully deliver big data initiatives in future. Big data is considered a key enabler for future decision making and process automation. The topic is however very new and not well understood yet. Hence 50% of big data projects are not delivering the expected benefits and are costing more than initially planned.

Taschenbuch. Zustand: Neu. Neuware -Master's Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 7, Queensland University of Technology (Faculty of Science and Engineering), course: Master in Business Process Management, language: English, abstract: This thesis investigates the project management approach for big data projects for industry partner Red Rocks Company. The aim of this project is to understand best practice project management for big data initiatives and to develop a framework to help such projects to deliver the expected advantages. A brief literature review is undertaken to find out how big data projects are managed. From this, a Big Data Analytics Framework is derived which is based on CRISP-DM. The framework is validated through interviews with stakeholders from the corporate sector. For this case study, the first three phases of the Business Process Management Lifecycle are applied: process discovery, analysis and design. Key findings of the case study are that literature recommends an agile project management approach for big data initiatives. On the contrary, the majority of interviewed industry stakeholders confirmes a waterfall approach is conducted more often to deliver such projects. The developed Big Data Analytics Framework will add significant benefits to Red Rocks Company as it will help to successfully deliver big data initiatives in future. Big data is considered a key enabler for future decision making and process automation. The topic is however very new and not well understood yet. Hence 50% of big data projects are not delivering the expected benefits and are costing more than initially planned.Books on Demand GmbH, Überseering 33, 22297 Hamburg 28 pp. Englisch.

Taschenbuch. Zustand: Neu. Deriving a big data analytics framework. Approaching the project management process for big data initiatives | A case study | Theres Mitscherling | Taschenbuch | Paperback | 28 S. | Englisch | 2020 | GRIN Verlag | EAN 9783346033543 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 132 | Sprache: Französisch | Produktart: Bücher.

Taschenbuch. Zustand: Neu. Neuware -Big data analytics (BDA) is important to reduce healthcare costs. However, in many hospital systems, new technologies that influence patient data require extensive technical and rigorous usability testing before implementation into production. Therefore, to implement, an existing High Performance Computing (HPC) Linux node clusters were utilized externally, and simulation of patient data benchmarked and cross-referenced with current metadata profiles in operational hospital systems at the Vancouver Island Health Authority (VIHA), Victoria, Canada. Over the tested platform, the data were generated, indexed and stored over a Hadoop Distributed File System (HDFS) to noSQL database (HBase) that represented three billion patient records. Hadoop/MapReduce framework formed the BDA platform with HBase (NoSQL database) using hospital-specific metadata and file ingestion. Queries showed high performance with a variety of Apache tools in Hadoop¿s ecosystem. BDA platform of HBase distributed by Hadoop successfully under high performance at large volumes and the entire data archive. Importance on representation of health informatics using technologies for big data in healthcare is discussed.Books on Demand GmbH, Überseering 33, 22297 Hamburg 136 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -L'analyse des big data (BDA) est importante pour réduire les coûts des soins de santé. Cependant, dans de nombreux systèmes hospitaliers, les nouvelles technologies qui influencent les données des patients nécessitent des tests techniques approfondis et des tests d'utilisation rigoureux avant d'être mises en ¿uvre dans la production. Par conséquent, pour la mise en ¿uvre, une grappe de n¿uds Linux de calcul à haute performance (HPC) existante a été utilisée en externe, et la simulation des données des patients a été comparée et croisée avec les profils de métadonnées actuels dans les systèmes hospitaliers opérationnels de l'Autorité sanitaire de l'île de Vancouver (VIHA), à Victoria, au Canada. Sur la plateforme testée, les données ont été générées, indexées et stockées sur un système de fichiers distribués Hadoop (HDFS) vers une base de données noSQL (HBase) représentant trois milliards d'enregistrements de patients. Le cadre Hadoop/MapReduce a formé la plateforme BDA avec HBase (base de données NoSQL) en utilisant des métadonnées spécifiques à l'hôpital et l'ingestion de fichiers. Les requêtes ont montré des performances élevées avec une variété d'outils Apache dans l'écosystème Hadoop. La plateforme BDA de HBase distribuée par Hadoop a réussi à obtenir des performances élevées pour les grands volumes et l'ensemble des archives de données.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Französisch.

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Taschenbuch. Zustand: Neu. Neuware -In the era of Big Data, firms in every sector are required to deal with a huge amount of data. Given an abundance of data, interest in BDA has led many companies to achieve more benefits by developing Big Data Analytics capabilities to maximize firm performance in the shade of tough competition. However, BDA pays off for some firms but not for others due to investment in BD still poses a lot of challenges because of missing link between analytics capabilities and Firm Performance(FPER). Also, BDA is not being utilized widely and only a few companies have achieved any impact from it. To address this challenge in Telecom domain and in response to the scarcity of research on this subject, this study concentrated on BDA Technology as one of the crucial pillars to get benefits through increased Firm Performance. For the purpose of this research we have adopted qualitative approach, case study method, and technique of data collection is includes semi-structure interview and document analysis. The findings from Delphi technique and interview in Malaysia, confirm the four critical elements that contribute in sustainability BDATech pillar, as an essential dimension, and its impact on FPER.Books on Demand GmbH, Überseering 33, 22297 Hamburg 268 pp. Englisch.

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Hardcover. Zustand: Brand New. 288 pages. 9.19x6.13x0.63 inches. In Stock.