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Hochschule Hochschulteam Prabhune, Ajinkya

Prof. Dr.
Ajinkya Prabhune

Professor

Fakultät für Information, Medien und Design

Ludwig-Guttmann-Straße 6

69123 Heidelberg

Raum arc215

Telefon: +49 (0)6221 6799-213

Telefax: +49 (0)6221 6799-200

E-Mail schreiben

Lehrveranstaltungen

  • Research Projects in Applied Computer Science and Data Analytics
  • Industry Big-data and data analytics case-studies
  • Software Development and Architecture
  • Advanced Databases
  • Big-data Management Concepts, Tools and Systems
  • Smart Data and Data Warehouses

Kurzvita

  • Since 2018 Professor at SRH University Heidelberg, School of Information, Media and Design
  • 2015-2017 Doctorate at Heidelberg University
  • 2014-2015 Software Developer and Researcher at Institute for Data Processing, Karlsruhe Institute for Technology
  • 2013-2017 External Lecturer at SRH University Heidelberg
  • 2011-2014 Software Developer at SAP SE Germany
  • 2010-2011 Master’s in Informatics from SRH University Heidelberg
  • 2008-2010 Software Developer at Atos Origin

Publikationen der letzten fünf Jahre

  • Gotze, Maike; Chouhan, Ashish; Prabhune, Ajinkya. Identifying fan motives during international sport event using social media A case study of 2016 UEFA European Championship, in: International Association for Communication and Sports 2020.
  • Chouhan, Ashish, Prabhune, Ajinkya, et al. “DWreck: A Data Wrecker Framework for Generating Unclean Datasets“, 2020 IEEE International Conference on Big Data Services and Machine Learning, IEEE, 2019.
  • Schmidt, Maria, Bhandare, Ojashree, Prabhune, Ajinkya, et al. “Classifying Cognitive Load for a Proactive In-Car Voice Assistant“, 2020 IEEE International Conference on Big Data Services and Machine Learning, IEEE, 2019.
  • Prabhune, Ajinkya, et al. “Optical Character Recognition using Neural Networks”, 2019, DH Estonia, Use of Digital Cultural Heritage in Research and Education 2019.
  • Prabhune, Ajinkya, et al. “FIF: A NLP-based Feature Identification Framework for Data Warehouses“, 2019 IEEE/ACM International Conference on Web Intelligence (WI). IEEE/ACM, 2019.
  • Grunzke, Richard, Prabhune, Ajinkya, et al. "The MASi repository service—Comprehensive, metadata-driven and multi-community research data management." Future Generation Computer Systems 94 (2019): 879-894.
  • Prabhune, Ajinkya, et al. "MetaStore: an adaptive metadata management framework for heterogeneous metadata models." Distributed and Parallel Databases 36.1 (2018): 153-194.
  • Prabhune, Ajinkya, et al. "P-PIF: a ProvONE provenance interoperability framework for analyzing heterogeneous workflow specifications and provenance traces." Distributed and Parallel Databases 36.1 (2018): 219-264.
  • Prabhune, Ajinkya, et al. "Managing provenance for medical datasets." BIOSTEC (2017): 236.
  • Fischer, Max., Prabhune, A, et al. "Advancing data management and analysis in different scientific disciplines." J. Phys. Conf. Ser. Vol. 898. 2017.
  • Prabhune, Ajinkya, et al. "MetaStore: A metadata framework for scientific data repositories." 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016.
  • Prabhune, Ajinkya, et al. "Prov2ONE: an algorithm for automatically constructing ProvONE provenance graphs." International Provenance and Annotation Workshop. Springer, Cham, 2016.
  • Grunzke, Richard, Prabhune, Ajinkya, et al. "Towards a Metadata-driven Multi-Community Research Data Management Service." IWSG. 2016.
  • Jung, Christopher., Prabhune, Ajinkya, et al. "Progress in multi-disciplinary data life cycle management." Journal of Physics: Conference Series. Vol. 664. No. 3. IOP Publishing, 2015.
  • Prabhune, Ajinkya, et al. "An optimized generic client service API for managing large datasets within a data repository." 2015 IEEE First International Conference on Big Data Computing Service and Applications. IEEE, 2015. Chandna, Swati, Prabhune, Ajinkya, et al. "Software workflow for the automatic tagging of medieval manuscript images (SWATI)." Document Recognition and Retrieval XXII. Vol. 9402. International Society for Optics and Photonics, 2015.

Forschungsprojekte

  • Grotz, M., Chouhan, A. and Prabhune, A. Identifying fan motives during international sport event using social media A case study of 2016 UEFA European Championship. International Association for Communication and Sports 2020.
  • Schroeder, C., Winterbottom, R., Sitter, R., Perdigones, L J., Sprick B., Schulz, F., Prabhune, A. (2019), Explaining a Hype – How Sentiment Analysis can add to the Understanding of a Hype Phenomenon.
  • Reifschneider, V., Bock F., Ruggiero, P., Förstel, S., Gottemeier, J., Prabhune, A. (2019). Detecting and Locating Cars from Overhead Using Deep Learning Methodologies for Union Investment. 
  • Schroeder, C., Winterbottom, R., Sitter, R., Perdigones L J., Prabhune, A. (2019). Big Data Analysis of Human Societal Events: Indications for Forecasting Currency Exchange Rates.
  • Prabhune, A and Thottempudi, G (2019). Optical Character Recognition with Neural Networks. Presentation at DH Estonia, Use of Digital Cultural Heritage in Research and Education
  • Melemenidis, A., Remmel, A., Dwivedi, T., Prabhune, A. (2019). Automatic Detection of Sensitive Data in Structured Data Sets.
  • Chaudhuri, M., Gard, R., Bewoor, R., Medankar, S., Singh, S., Venkateswaran., S, Prabhune, A. (2019). Fake New Detection using topic and author agnostic approach supplemented by Twitter data.
  • Kleinerüschkamp, M., Paz, G., Schwinn, F., Tausend, F., Prabhune, A. (2019). Prediction of Merger and Acquistion Transactions.
  • Reifschneider, V., Bock F., Ruggiero, P., Förstel, S., Gottemeier, J., Prabhune, A. Schulz, F. (2018). Exploring Supervised Learning Algorithms for Email Classification in R: A Case Study for Customer Support Requests for Union Investment.
  • Chouhan, A., Prabhuraj, P., Chaudhari, H., Prabhune, A. (2018). DWreck: An automated framework for generating unclean datasets.
  • Rasbold, T., Hannes, Q., Weber, J., Ruppel, J. (2018) Feature generation in preparation for anomaly detection in robotic logs of the manufacturing industry for ABB Research Center. • Rasbold, T., Hannes, Q., Weber, J., Ruppel, J., Schulz, F., Prabhune, A. (2018). Data driven approach for business investments: A case study in restaurant industry.
  • Prabhune, A and Chandna, S. (2018). PAN: A Page Annotation Framework for MetaStore.