Big Data Analytics Research Lab (BAREL@DBIS)
The objective of the Big Data and Analytics Research Lab at DBIS is to unify the research activities in data analytics from the perspective of both information systems and computer science. Our approach is based on the interdisciplinary binding between data management technologies and analytics.
Research Area: Data Management Technologies
Our work is concentrated on the evaluation and development of
We are interested to benchmark new software platforms for storing and processing massive amounts of data and for analytics beyond what conventional relational systems can do. We are interested to test such systems against domain specific workloads to perform data clustering, predictive modeling, and complex statistics. In addition, we are investigating graph-based DBMSes for social-network-style analysis.
Research Area: Linked Open Data (LOD)
Linked Open Data are structured data that are published online in order to be accessed automatically by computers. By combining different sources huge amount of similar or related structured data are brought together in order to be queried and analyzed.
This research area is closely related to the Semantic Web and its standards stack like RDF* and OWL. We are interested in analyzing and benchmarking existing storage solutions and to apply the idea of LOD to selected applications.
Research Area: Data Analytics / Data Science
Data analytics is a sub-area of information systems focused on the extraction and analysis of user feedback from portals/websites, online social networks and digital information spaces in general. The lab aims to support teaching activities in the areas of Business Information Processing and Analytics.
The themes that Barel@DBIS is currently active are the following:
Opinion Mining with applications on electronic commerce (emphasis on online product reviews e.g. Amazon)
The research work undertaken on opinion mining in the context of DBIS mainly focuses on the use of user-generated content for extracting valuable information related with user behavior with a direct practical implication that we have under focus its application on electronic commerce.
User behavior on online social networks (e.g. Facebook, Twitter)
The research work undertaken on user behavior on online social networks in the context of DBIS focuses on online sociability and the impact of socio-metric and individual characteristics on the growth and sustainability on online social networks and online communities. A particular research theme that we pursue in that area is the concept of self-revealing in online social networks and its implications.
User feedback in online information spaces (Online Communities, Information seeking behavior, preference elicitation)
This research theme tackles with the understanding of factors that influence user feedback in online information spaces. We consider user feedback in the form of user generated content both in unstructured (e.g. comments on online spaces) and structured terms (e.g. navigation logs). A particular point of interest we currently undertake in this area is the case of participation in online information spaces (e.g. online communities) as well as the factors that influence them both in behavioral (e.g. game theory) and content specific aspects (e.g. sentiment analysis).
Research Area: Data Processing Integration
In practice, data queries and updates often involve a heterogeneous mix of data sources and computations. For example, a real-time news aggregator may continuously scan an input stream of news articles, call a remote service to automatically translate articles in foreign languages, use a custom python script to filter out unimportant stop words, and then query a database of past articles to gauge which parts of the article are truly newsworthy.
Within this research area we explore tools and domain-specific languages to:
Big Data for Social Good
Every day, 2.5 quintillion bytes of data are created. This data comes from digital pictures, videos, posts to social media sites, intelligent sensors, purchase transaction records, cell phone GPS signals to name a few. This is Big Data. There is a great interest both in the commercial and in the research communities around Big Data. It has been predicted that “analyzing Big Data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus”, according to research by MGI and McKinsey’s Business Technology Office. But very few people seem to look at how Big Data can be used for solving social problems.
Most of the work in fact is not in this direction. Why this? What can be done in the international research community to make sure that some of the most brilliant ideas do have an impact also for social issues? Big Data is clearly of interest to marketers and enterprises a like who wish to offer their customers better services and better quality products. Ultimately their goal is to sell their products/services. This is good, but how about digging into Big Data to help people in need? Preventing / predicting natural catastrophes, helping offering services “targeting” to people and structures in social need?
One motivation of our Lab is to encourage the international research community to work on Big Data problems that have a potential positive social impact for mankind.
Big Data Analytics Day 2013
Location: Goethe University Frankfurt
Date: to be announced
Organizer: Big Data Analytics Research Lab at the Institute of Computer Science, Goethe University of Franklfurt.
Every day, 2.5 quintillion bytes of data are created. This data comes from digital pictures, videos, posts to social media sites, intelligent sensors, purchase transaction records, cell phone GPS signals to name a few. This is Big Data.
There is no doubt that Big Data and especially what we do with it has the potential to become a driving force for innovation and value creation.
The day will be structured in two parts:
Key note speaker
Matthew Eric Bassett
Rill, S., Drescher, J., Reinel, D., Scheidt, J., Schütz, O., & Wogenstein, F. (2012). A Generic Approach to Generate Opinion Lists of Phrases for Opinion Mining Applications. Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM). ACM, 2012, Beijing, China, DOI: 10.1145/2346676.2346683
Wu, P. F., Van der Heijden, H., & Korfiatis, N. (2011). The Influences of Negativity and Review Quality on the Helpfulness of Online Reviews. Proceedings of the International Conference on Information Systems (ICIS), 2011. Presented at the International Conference on Information Systems, Shanghai, China.
User behavior on online social networks
Korfiatis, N., Zicari R., Mahnke V. (2012). On the willingness to acquire new Facebook friends: A study of digital natives and experienced digital workers. (Under review)
User feedback in online information spaces
Kostagiolas, P. A., Samioti, F., Alexias, G., Korfiatis, N., & Niakas, D. (2012). Examining Patterns of Information Behavior Among Healthcare Professionals: A Case Study on Health Psychologists. New Review of Information Networking,17(2), 108-119.
Big Data for Good
Roger Barca, Laura Haas, Alon Halevy, Paul Miller, Roberto V. Zicari. (2012). A distinguished panel of experts discuss how Big Data can be used to create Social Capital. Blog Panel (PDF)
User feedback in online information spaces
Fuzzy Clustering of Web User's Profiles for Analyzing their Behavior and Interests Natascha Hoebel, Stanislav Kreuzer, 2012 in “Fuzzy Methods for Customer Relationship Management and Marketing” edited by Prof. Dr. Andreas Meier, Prof. Dr. Laurent Donzé IGI Global URL
A Framework Analysis for Managing Explicit Feedback of Visitors of a Web Site Clemens Schefels, Roberto V. Zicari. In: International Journal of Web Information Systems (IJWIS), Volume 8 - Issue 1, 2011, pp.127-150, Emerald.
How Do Framing Strategies Influence the User’s Choice of Content on the Web? Ioanna Constantiou, Natascha Hoebel, Roberto V. Zicari. Journal of Concurrency and Computation: Practice and Experience, Volume 24, No. 17, pp. 2207-2220, December 2012, Wiley Special Issue for Managing Web 2.0 Content ISSN 1532-0626 DOI:10.1002/cpe.1794
Reputation, framing strategies and user's choice of content on the Web: an Empirical Study Ioanna Constantiou, Natascha Hoebel, Roberto V. Zicari. Journal of Concurrency and Computation: Practice and Experience, Volume 22, No. 7, pp. 872-889, May 2010, Wiley
How to Find Important Users in a Web Community? Mining Similarity Graphs Clemens Schefels. Proceedings of the First International Conference on DATA ANALYTICS 2012 / NexTech 2012, Barcelona, Spain, 23-28th of September 2012.
Behavioral Analysis of Registered Web Site Visitors with Help of Mouse Tracking" Clemens Schefels, Sven Eschenberg, Christian Schöneberger. Proceedings of the 14th IEEE Conference on Commerce and Enterprise Computing (CEC 2012), Hangzhou, China, 9-11th of September 2012.
Analyzing Web Profiles using Probabilistic Ontologies" Pawel Kozak, Karsten Tolle. ACM 3rd International Conference on Web Science (ACM WebSci 11), Koblenz, Germany, 14-17th of June 2011.
A Framework Analysis for Managing Explicit Feedback of Visitors of a Web Site" Clemens Schefels, Roberto V. Zicari. Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services (iiWAS2010), Paris, France, 8th-10th of November 2010.
CORD: A Hybrid Approach for Efficient Clustering of Ordinal Data using Fuzzy Logic and Self-Organizing Maps. Natascha Hoebel, Stanislav Kreuzer. Proceedings of the 6th International Conference on Web Information Systems and Technologies (WEBIST 2010), Valencia, Spain, 7th-11th of April 2010. ACM SIG WEB (Special Interest Group on Hypertext, Hypermedia and the Web) cooperating conferernce.
Introducing Zones to a Web Site: A Test Based Evaluation on Semantics, Content, and Business Goals" Natascha Hoebel, Naveed Mushtaq, Clemens Schefels, Karsten Tolle, Roberto V. Zicari. 11th IEEE Conference on Commerce and Enterprise Computing (CEC’09), Vienna, Austria, July 20th-23th 2009. IEEE Computer Society Press, pp 265-272, Los Alamitos, USA.
Creating User Profiles of Web Visitors using Zones, Weights and Actions" Natascha Hoebel, Roberto V. Zicari Proceedings of 5th IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE 08), 21th-24th of July 2008, Washington D.C., USA.
On Clustering Visitors of a Web Site by Behavior and Interests" Natascha Hoebel, Roberto V. Zicari Proceedings of the 5th Atlantic Web Intelligence Conference - AWIC'2007, Fontainebleau, France, June 2007. Springer, Advances in Soft Computing, Volume 43/2007, Advances in Intelligent Web Mastering, p.160-167, ISBN: 978-3-540-72574-9
The Design of Gugubarra 2.0: A Tool for Building and Managing Profiles of Web Users" Natascha Hoebel, Sascha Kaufmann, Karsten Tolle, R. V. Zicari IEEE/WIC/ACM International Conference on Web Intelligence, Intelligent Agent Technology and Data Mining, 18-22 December 2006, Hong Kong
The Gugubarra Project: Building and Evaluating User Profiles for Visitors of Web Sites" Natascha Hoebel, Sascha Kaufmann, Karsten Tolle, R. V. Zicari First IEEE Workshop on Hot Topics in Web Systems and Technologies, November 13-15, 2006 Boston, Massachusetts, USA.
Roberto V. Zicari. (2012). Big Data: Challenges and Opportunities
ODBMS Industry Watch Blog Posts on Big Data, by Roberto V. Zicari, http://www.odbms.org/blog/tag/big-data/