Big Data phenomena is getting a lot of attention from the academia and industry. And one of the most interesting aspects of the Big Data is the analysis part of it, where software tools, applied methods and different aspects of process management are playing significant role. The analytical part of the Big Data brings significant "added value" to the daily business in terms of new knowledge that lead to a better decision making. This is very demanding by industry in a broader sense. The other side of the Big Data Analysis utilization is to help in creation of a brand new business models and concepts such that they are heavily driven by data and analytics. In this sense, the role of the academia is to explore appearing new research domains with accuracy, bring experience of data analysis from academia to the business and also answer open questions addressed by various communities. The focus of the VLBA is to use ideas behind Big Data phenomena with help of various ICT technologies, which are becoming de-facto standards of the Big Data domain. Big Data related research activities within the VLBA are based on a various ICT software/hardware, such as Distributed Data Storage and Processing (e.g., Apache Hadoop, Apache Spark, etc.) and In-Memory Computing (e.g., SAP HANA Appliance), and various expertise domains, such as energy, mobility, ERP, CRM, sustainability, CEMIS etc. Besides research activities, VLBA is embedding Big Data concepts into teaching activities, mainly as a main direction of the bachelor and master level theses and as a 1 year project group topics.
- In-Memory Computing and Big Data Analysis with User-Defined Functions by Viktor Dmitriyev
- Konzeption eines Linkage-Layers zur Unterstützung der Datenintegration by Felix Kruse
- Analysemethode zur Auswertung unstrukturierten Textes zur Früherkennung von Versorgungsrisiken in der Automobilindustrie by Pajam Hassan
- Data Analytics im Zeitalter von Big Data
- Seminar - Was ist Big Data?
- Empower II - Empower Generations Chapter II
- PG Big Data Archive: Data Science zur Energiewende (DAvE)
- Demosystem on HANA (DoHA)
- Data AnalyticS with Hadoop (PGDASH)