Big Data Management Systems in Business and Industrial Applications

Co-located with the 17th Conference on Database Systems for Business, Technology, and Web

Benjamin Klöpper, Lena Wiese

Big Data stands for the intelligent and efficient handling and usage of large, heterogeneous and fast changing amounts of data. The ultimate goal of big data is the generation of valuable insights from ever growing amounts of data. Hence, the application of Big Data in business and industry contexts has proven to be very valuable. However, several challenges related to big data remain; these challanges go beyond the often used catchphrases: volume, velocity, variety, and veracity and also address security, privacy, linked-data technologies in the context of a wide range of AI applications. Big Data is more than just data analysis since the outcome influences the way how digital businesses and a knowledge economy are organized now and in the future.

This workshop is dedicated to the application of Big Data concepts and technologies in real-world systems for a variety of application domains like Manufacturing, Logistics, Media, Healthcare, and Finance.

Topics of Interest

Topics of interest include, but are not limited to:

• Tools and Technologies
• Big data technologies (MapReduce, NoSQL, InMemory, Parallel Data Processing)
• Applied security in big data contexts
• Tools for data mapping, data exploration and data cleaning
• Software architectures for Big Data and Data Analytics
• Development methodologies for Big Data Applications
• Technologies for the generation of big data (sensor networks)
• Stream processing architectures
• Big Data and Data Analytics Applications in Business and Industry
• Process Control and Optimization
• Event-Driven Business Process Management
• Decision Support
• Preventive and Predictive Maintenance
• Energy management
• Industrial security
• Environmental compliance enforcement
• Social media analytics for market intelligence
• Algorithms and Methods for Business and Industrial Analytics
• Predictive Analytics, Anomaly Detection, Machine Learning, Visual Analytics
• Data quality and cleaning
• Mining heterogeneous data
• Event detection in complex data
• Complex Event Processing
• Unsupervised and supervised learning from large amounts of data
• Self-optimization of complex industrial processes

Submission Guidelines

We welcome practical experience reports as well as theoretical analyses of novel approaches and algorithms. Full (4 to 8 pages) and short (2 to 3 pages) papers are solicited. Submissions can be written in German or English and must consist of an original unpublished work not being under review elsewhere. Submission must adhere to the LNI formatting guidelines of the BTW main conference and must be uploaded as PDF documents through the BTW online submission system (Workshop BigBIA17). Submissions will be reviewed in a single blind review process by the workshop program committee members. Accepted papers will be published in GI Lecture Notes in Informatics (LNI).

Important Dates

Workshop Organizers

• Dr. Benjamin Klöpper (ABB Research)
• Dr. Lena Wiese (Georg-August-Universität Göttingen)

Program Committee

• Stefan Edlich, Beuth Hochschule für Technik
• Beate Heisterkamp, Materials Consulting
• Holger Kache, IBM Deutschland RD GmbH
• Mikro Kämpf, Cloudera
• Carsten Lanquillion, Hochschule Heilbronn
• Stefan Mandl, EXASOL AG
• Carlos Paiz Gatica, Weidmüller Interface GmbH & Co KG
• Daniel Ritter, SAP SE
• Klaus Schmid, Universität Hildesheim
• Benedikt Schmidt, ABB Forschungszentrum GmbH
• Kerstin Schneider, Hochschule Harz
• Heiko Thimm, Hochschule Pforzheim
• Liesbeth Vanherpe, École polytechnique fédérale de Lausanne