% Keywords: Clustering
% Encoding: utf-8
@Article{Karandikar_2021,
  author = {Nikita Karandikar and Rockey Abhishek and Nishant Saurabh and Zhiming Zhao and Alexander Lercher and Ninoslav Marina and Radu Prodan and Chunming Rong and Antorweep Chakravorty},
  journal = {Blockchain: Research and Applications},
  title = {Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering.1},
  year = {2021},
  issn = {2096-7209},
  month = jun,
  pages = {1--35},
  abstract = {Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.},
  doi = {10.1016/j.bcra.2021.100016},
  keywords = {Peak shaving, aggregation analysis, contextual clustering, blockchain, incentivization},
  publisher = {Elsevier (BV)},
  url = {https://www.sciencedirect.com/science/article/pii/S2096720921000117?via=ihub}
}
@InProceedings{Pohl2013a,
  author = {Pohl, Daniela and Bouchachia, Abdelhamid and Hellwagner, Hermann},
  booktitle = {12th International Conference on Machine Learning and Applications},
  title = {Online Processing of Social Media Data for Emergency Management},
  year = {2013},
  address = {Los Alamitos, CA, USA},
  editor = {Tecuci, Gheorghe and Boicu, Mihai and Kubat, Miroslav},
  month = {dec},
  pages = {1-6},
  publisher = {IEEE},
  abstract = {Social media offers an opportunity for emergency management to identify issues that need immediate reaction. To support the effective use of social media, an analysis approach is needed to identify crisis-related hotspots. We consider in this investigation the analysis of social media (i.e., Twitter, Flickr and YouTube) to support emergency management by identifying sub-events. Sub-events are significant hotspots that are of importance for emergency management tasks. Aiming at sub-event detection, recognition and tracking, the data is processed online in real-time. We introduce an incremental feature selection mechanism to identify meaningful terms and use an online clustering algorithm to uncover sub-events on-the-fly. Initial experiments are based on tweets enriched with Flickr and YouTube data collected during Hurricane Sandy. They show the potential of the proposed approach to monitor sub-events for real-world emergency situations.},
  keywords = {Online Clustering, Sub-Event Detection, Crisis Management},
  language = {EN},
  location = {Miami, Florida, USA},
  pdf = {https://www.itec.aau.at/bib/files/Pohl_ICMLA13.pdf},
  talkdate = {2013.12.01},
  talktype = {poster}
}
@InProceedings{Pohl2012c,
  author = {Pohl, Daniela and Bouchachia, Abdelhamid and Hellwagner, Hermann},
  booktitle = {11th International Conference on Machine Learning and Applications},
  title = {Automatic Identification of Crisis-Related Sub-Events using Clustering},
  year = {2012},
  address = {Los Alamitos, CA, USA},
  editor = {Han, Jiawei and Khoshgoftaar, Taghi M and Zhu, Xingquan},
  month = {dec},
  pages = {333-338},
  publisher = {IEEE},
  abstract = {Social media are becoming an important instrument for supporting crisis management, due to their broad acceptance and the intensive usage of mobile devices for accessing them. Social platforms facilitate collaboration among the public during a crisis and also support after-the-fact analysis. Thus, social media are useful for the processes of understanding, learning, and decision making. In particular, having information from social networks in a suitable, ideally summarized, form can speed up such processes. The present study relies on Flickr and YouTube as social media and aims at automatically identifying individual sub-events within a crisis situation. The study applies a two-phase clustering approach to detect those sub-events. The first phase uses geo-referenced data to locate a sub-event, while the second phase uses the natural language descriptions of pictures and videos to further identify the ”what-about” of those sub-events. The results show high potential of this social media-based clustering approach for detecting crisis-related sub-events.},
  keywords = {Clustering, Sub-Event Detection, Crisis Management},
  language = {EN},
  location = {Boca Raton, Florida, USA},
  pdf = {https://www.itec.aau.at/bib/files/06406815.pdf},
  talkdate = {2012.12.12},
  talktype = {registered},
  url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6406815}
}
@InProceedings{Pohl2012b,
  author = {Pohl, Daniela and Bouchachia, Abdelhamid and Hellwagner, Hermann},
  booktitle = {IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)},
  title = {Supporting Crisis Management via Sub-Event Detection in Social Networks},
  year = {2012},
  address = {Toulouse, Fance},
  editor = {Diaz, Michel and Senac, Patrick},
  month = {jun},
  pages = {373 -378},
  publisher = {IEEE},
  abstract = {Social networks give the opportunity to gather and share knowledge about a situation of relevance. This so called user-generated content is getting increasingly important during crisis management. It facilitates the collaboration with citizens or parties involved from the very beginning of the crisis. The information captured in form of images, text or videos is a valuable source of identifying sub-events of a crisis. In this study, we use metadata of images and videos collected from Flickr and YouTube to extract sub-events in crisis situations. We investigate the suitability of clustering techniques to detect sub-events. In particular two algorithms are evaluated on several data sets related to crisis situations. The results show the high potential of the approach proposed.},
  doi = {10.1109/WETICE.2012.58},
  issn = {1524-4547},
  keywords = {Crisis Management, Information Retrieval, Clustering, Sub-Event Detection},
  language = {EN},
  location = {Toulouse, Fance},
  talkdate = {2012.06.26},
  talktype = {registered}
}
@InProceedings{Pohl2012a,
  author = {Pohl, Daniela and Bouchachia, Abdelhamid and Hellwagner, Hermann},
  booktitle = {Proceedings of the 21st International Conference Companion on World Wide Web},
  title = {Automatic Sub-Event Detection in Emergency Management using Social Media},
  year = {2012},
  address = {New York, NY, USA},
  editor = {Mille, Alain and Gandon, Fabien and Misselis, Jacques},
  month = {apr},
  pages = {683--686},
  publisher = {ACM},
  series = {WWW '12 Companion},
  abstract = {Emergency management is about assessing critical situations, followed by decision making as a key step. Clearly, information is crucial in this two-step process. The technology of social (multi)media turns out to be an interesting source for collecting information about an emergency situation. In particular, situational information can be captured in form of pictures, videos, or text messages. The present paper investigates the application of multimedia metadata to identify the set of sub-events related to an emergency situation. The used metadata is compiled from Flickr and YouTube during an emergency situation, where the identification of the events relies on clustering. Initial results presented in this paper show how social media data can be used to detect different sub-events in a critical situation.},
  keywords = {Emergency Management, Social Media, Clustering},
  language = {EN},
  location = {Lyon, France},
  pdf = {https://www.itec.aau.at/bib/files/p683.pdf},
  talkdate = {2012.04.17},
  talktype = {registered}
}