Welcome to the website of the "Enterprise Platform and Integration Concepts" (EPIC) group.
Our activities are focused on the following research topics:
In-Memory Data Management for Enterprise Applications
Human-Centered Software Design and Engineering
In-Memory Data Management for Life Sciences
Our group includes PostDocs, PhD students, and student assistants, and is headed by Prof. Dr. Hasso Plattner. If you are interested in our work or want to join our team, please contact Dr. Matthias Uflacker.
Our team is giving a series of lectures and seminars with a focus on enterprise systems design and in-memory data management. Strong links to the industry ensure a close connection between theory and its implementation in the real world.
Our research focuses on the technical aspects of business software and the integration of different software systems to meet customer requirements. This involves studying the conceptual and technological aspects of in-memory databases, design principles, and programming methods for enterprise applications.
We continually strive to translate our research into practical outputs that improve the quality of enterprise applications. A close link to industry partners ensures relevance and impact of our work. Get here an overview of our current and previous projects.
The current data deluge demands fast and real-time processing of large datasets to support various applications, also for textual data, such as scientific publications. Natural language processing (NLP) is the field of automatically processing textual documents. Processing and semantically annotating large textual collection is a time-consuming and tiresome task, which requires integration of various tools. In-memory database (IMDB) technology comes as an alternative given its ability to process large document collections quickly in real time.
We have a open position for students, please contact us!
Olelo is our NLP platform and integrate various NLP-related tasks for the biomedical domain.
NLP includes a variety of tasks such as tokenization (delimitation of words), part-of-speech tagging (assignment of syntactic categories to words), chunking (delimitation of phrases) and syntactic parsing (construction of syntactic tree for a sentence). Further, NLP also involves semantic-related tasks such as named-entity recognition (delimitation of predefined entity types, e.g., person and organization names), relation extraction (identification of pre-defined relations from text) and semantic role labeling (determining pre-defined semantic arguments). We have implemented many NLP methods in the SAP HANA database, as follow:
Chunking/shallow parsing and semantic role labeling (MP ss2015)
Named-entity recognition(BP 2015/2016)
Relation extraction (MP ws2015/2016)
There are many NLP applications that can be developed for various scenarios and domains, such as automatically generating summaries of one or more documents (summarization), retrieval of documents relevant for a particular query (information retrieval), extraction of specific information from a huge document collection (information extraction) and automatically answering questions posed by the users (question answering). We have developed NLP methods and applications for many of these task, as follow:
Deep learning to extract exact answers (Master thesis Georg Wiese)
Semantic role labeling to support question answering (Master thesis Fabian Eckert)
Olelo: intelligent navigation through the biomedical scientific literature (BP 2015/2016)
Kraus M, Niedermeier J, Jankrift M, Tietböhl S, Stachewicz T, Folkerts H, Uflacker M and Neves M. Olelo: a web application for intuitive exploration of biomedical literature, Nucleic Acids Research Web service issue (accepted).
Neves M, Folkerts H, Jankrift M, Niedermeier J, Stachewicz T, Tietböhl S, Kraus M and Uflacker M. Olelo: A Question Answering Application for Biomedicine, ACL'17 Demo, Vancouver, Canada. (accepted)
Habibi M, Weber L, Neves M, Wiegandt D L and Leser U. Deep Learning with Word Embeddings improves Biomedical Named Entity Recognition, ISMB/ECCB 2017, Prague, Czech Republic. (accepted)
Folkerts H and Neves M. Olelo’s named-entity recognition web service in the BeCalm TIPS task, BeCalm Workshop 2017, Barcelona, Spain.
Nentidis A, Yang Z, Neves M, Kim J-D, Krithara A, Paliouras G and Kakadiaris I. BioASQ and PubAnnotation: Using linked annotations in biomedical question answering, BLAH3 workshop, 2017, Tokyo, Japan.
Neves M and Kraus M. BioMedLAT Corpus: Annotation of the Lexical Answer Type for Biomedical Questions, Open Knowledge Base and Question Answering Workshop, Coling 2016, Osaka, Japan.
Schulze F and Neves M. Entity-Supported Summarization of Biomedical Abstracts, Proceedings of the Firth Workshop on Building and Evaluating Resources for Biomedical Text Mining, Coling 2016, Osaka, Japan.
Neves M, Rey M and Wittig U. Text Mining to Support Data Curation for SABIO-RK, BLAHmuc workshop, 2016, Munich, Germany.
Cohen K B, Demner-Fushman D, Fort K, Grouin C, Hunter L E, U. Leser U, Neveol A, Neves M and Zweigenbaum P. Towards the Last Annotation Tool, BLAHmuc workshop, 2016, Munich, Germany.
Bojar O, Chatterjee R, Federmann C, Graham Y, Haddow B, Huck M, Jimeno Yepes A, Koehn P, Logacheva V, Monz C, Negri M, Neveol A, Neves M, Popel M, Post M, Rubino R, Scarton C, Specia L, Turchi M, Verspoor K and Zampieri M. Findings of the 2016 Conference on Machine Translation, ACL 2016, Proceedings of the First Conference on Machine Translation (WMT16), pp. 131-198, 2016, Berlin, Germany.
Grundke M, Jasper J, Perchyk M, Sachse J P, Krestel R, Neves M. TextAI: Enhancing TextAE with Intelligent Annotation Support, 7th International Symposium on Semantc Mining for Biomedicine (SMBM), 2016, Potsdam, Germany.
Schulze F, Schüler R, Draeger T, Dummer D, Ernst A, Flemming P, Perscheid C, Neves M. Biomedical Question Answering Based on In-Memory Technology, ACL 2016, BioASQ Challenge, 2016, Berlin, Germany.
Neves M, Jimeno-Yepes A and Névéol A. The Scielo Corpus: a Parallel Corpus of Scientific Publications for Biomedicine, International Conference on Language Resources and Evaluation (LREC), 2016, Portoroz, Slovenia.
Neves M. HPI Question Answering System in the BioASQ 2015 Challenge , Working Notes for the CLEF BioASQ Challenge, 2015,Toulouse, France.
Neves M and Leser U. Question Answering for Biology, Methods, 2015.
Mariana Neves: HPI in-memory-based database system in Task 2b of BioASQ Working Notes for the CLEF BioASQ Challenge, 2014
Konrad Herbst, Cindy Fähnrich, Mariana Neves, Matthieu-P. Schapranow: Applying In-Memory Technology for Automatic Template Filling in the Clinical Domain, CLEF 2014 Evaluation Labs and Workshop, Online Working Notes, 2014
Mariana Neves, Konrad Herbst, Matthias Uflacker, Hasso Plattner: Preliminary evaluation of passage retrieval in biomedical multilingual question answering, BioTxtM 2014, Fourth Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing, 2014
Mariana Neves: Preliminary evaluation of question answering to support biological curation, Poster in the BioCuration Conference (ISB2014), 2014, Toronto, Canada.
We are proud to announce " A Course in In-Memory Data Management" by Prof. Dr. h.c. Hasso Plattner. This book is the culmination of six years work of in-memory research. As such, it provides the technical foundation for combined transactional and analytical workloads inside one single database as well as examples of new applications that are now possible given the availability of the new technology. The book is available at Springer.