https://sdq.kastel.kit.edu/api.php?action=feedcontributions&user=Nv3463&feedformat=atomSDQ-Institutsseminar - Benutzerbeiträge [de]2024-03-28T20:59:02ZBenutzerbeiträgeMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2023-03-09&diff=2429Institutsseminar/2023-03-092023-02-20T07:24:17Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2023-03-09T10:00:00.000Z |online=https://kit-lecture.zoom.us/j/67744231815 }}“</p>
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}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Fabian_Richter&diff=2144Fabian Richter2022-04-27T11:35:09Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Betreuer |email=fabian.richter@kit.edu |homepage=https://dbis.ipd.kit.edu/3143.php |lehrstuhl=IPD Böhm }}“</p>
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<div>{{Vortrag<br />
|vortragender=Klevia Ulqinaku<br />
|email=ukkns@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Edouard Fouché<br />
|termin=Institutsseminar/2021-10-29 Zusatztermin<br />
|kurzfassung=Research papers are commonly classified into categories, and we can see the existing contributions as a massive document directory, with sub-folders. However, research typically evolves at an extremely fast pace; consider for instance the field of computer science. It can be difficult to categorize individual research papers, or to understand how research communities relate to each other.<br />
In this thesis we will analyze and visualize semantics from massive document directories. The results will be displayed using the arXiv corpus, which contains domain-specific (computer science) papers of the past thirty years. The analysis will illustrate and give insight about past trends of document directories and how their relationships evolve over time.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2021-10-29_Zusatztermin&diff=1762Institutsseminar/2021-10-29 Zusatztermin2021-09-09T12:23:15Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2021/10/29 11:30:00 |raum=https://conf.dfn.de/webapp/conference/979160755 }}“</p>
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<div>{{Vortrag<br />
|vortragender=Elena Schediwie<br />
|email=elena.astankow@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Florian Kalinke<br />
|termin=Institutsseminar/2021-04-30 Zusatztermin<br />
|kurzfassung=Kurzfassung<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2021-04-30_Zusatztermin&diff=1590Institutsseminar/2021-04-30 Zusatztermin2021-03-15T06:08:57Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2021/04/30 11:30 |raum=https://conf.dfn.de/webapp/conference/979160755 }}“</p>
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}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=A_comparative_study_of_subgroup_discovery_methods&diff=1567A comparative study of subgroup discovery methods2021-02-17T08:56:28Z<p>Nv3463: </p>
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<div>{{Vortrag<br />
|vortragender=Mohamed Amine Chalghoum<br />
|email=uwejw@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Vadim Arzamasov<br />
|termin=Institutsseminar/2021-02-19 Zusatztermin<br />
|kurzfassung=Subgroup discovery is a data mining technique that is used to extract interesting relationships in a dataset related to to a target variable. These relationships are described in the form of rules. Multiple SD techniques have been developed over the years. This thesis establishes a comparative study between a number of these techniques in order to identify the state-of-the-art methods. It also analyses the effects discretization has on them as a preprocessing step . Furthermore, it investigates the effect of hyperparameter optimization on these methods. <br />
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Our analysis showed that PRIM, DSSD, Best Interval and FSSD outperformed the other subgroup discovery methods evaluated in this study and are to be considered state-of-the-art . It also shows that discretization offers an efficiency improvement on methods that do not employ internal discretization. It has a negative impact on the quality of subgroups generated by methods that perform it internally. The results finally demonstrates that Apriori-SD and SD-Algorithm were the most positively affected by the hyperparameter optimization.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2021-02-19_Zusatztermin&diff=1566Institutsseminar/2021-02-19 Zusatztermin2021-02-17T08:55:59Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2021/02/19 11:30:00 |raum=https://conf.dfn.de/webapp/conference/979160755 }}“</p>
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}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Theory-Guided_Data_Science_for_Lithium-Ion_Battery_Modeling&diff=1545Theory-Guided Data Science for Lithium-Ion Battery Modeling2021-01-26T12:26:34Z<p>Nv3463: </p>
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<div>{{Vortrag<br />
|vortragender=Nico Denner<br />
|email=nico.denner@gmx.de<br />
|vortragstyp=Proposal<br />
|betreuer=Pawel Bielski<br />
|termin=Institutsseminar/2021-01-29 Zusatztermin<br />
|kurzfassung=Lithium-ion batteries are driving innovation in the evolution of electromobility and renewable energy. These complex, dynamic systems require reliable and accurate monitoring through Battery Management Systems to ensure the safety and longevity of battery cells. Therefore an accurate prediction of the battery voltage is essential which is currently realized by so-called Equivalent Circuit (EC) Models. <br />
<br />
Although state-of-the-art approaches deliver good results, they are hard to train due to the high number of variables, lacking the ability to generalize, and need to make many simplifying assumptions. In contrast to theory-based models, purely data-driven approaches require large datasets and are often unable to produce physically consistent results. Theory-Guided Data Science (TGDS) aims at using scientific knowledge to improve the effectiveness of Data Science models in scientific discovery. This concept has been very successful in several domains including climate science and material research. <br />
<br />
Our work is the first one to apply TGDS to battery systems by working together closely with domain experts. We compare the performance of different TGDS approaches against each other as well as against the two baselines using only theory-based EC-Models and black-box Machine Learning models.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2021-01-29_Zusatztermin&diff=1544Institutsseminar/2021-01-29 Zusatztermin2021-01-26T12:26:07Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2021/01/29 11:30 |raum=https://conf.dfn.de/webapp/conference/979160755 }}“</p>
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<div>{{Vortrag<br />
|vortragender=Alan Mazankiewicz<br />
|email=alan.mazankiewicz@student.kit.edu<br />
|vortragstyp=Masterarbeit<br />
|betreuer=Klemens Böhm<br />
|termin=Institutsseminar/2020-06-05<br />
|kurzfassung=Human Activity Recognition (HAR) from accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perform poorly when applied to new users that were not part of the training data. Previous work has addressed this challenge by personalizing general recognition models to the motion pattern of a new user in a static batch setting. The more challenging online setting has received less attention. No samples from the target user are available in advance, but they arrive sequentially. Additionally, the user's motion pattern may change over time. Thus, adapting to new and forgetting old information must be traded off. Finally, the target user should not have to do any work to use the recognition system by labeling activities. Our work addresses this challenges by proposing an unsupervised online domain adaptation algorithm. It works by aligning the feature distribution of all the subjects, sources and target, within deep neural network layers.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Incremental_Real-Time_Personalization_in_Human_Activity_Recognition_Using_Domain_Adaptive_Batch_Normalization&diff=1385Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization2020-06-02T12:06:45Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Alan Mazankiewicz |email=alan.mazankiewicz@student.kit.edu |vortragstyp=Masterarbeit |betreuer=Klemens Böhm |termin=Institutsseminar/2…“</p>
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<div>{{Vortrag<br />
|vortragender=Alan Mazankiewicz<br />
|email=alan.mazankiewicz@student.kit.edu<br />
|vortragstyp=Masterarbeit<br />
|betreuer=Klemens Böhm<br />
|termin=Institutsseminar/2020-06-12<br />
|kurzfassung=Human Activity Recognition (HAR) from accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perform poorly when applied to new users that were not part of the training data. Previous work has addressed this challenge by personalizing general recognition models to the motion pattern of a new user in a static batch setting. The more challenging online setting has received less attention. No samples from the target user are available in advance, but they arrive sequentially. Additionally, the user's motion pattern may change over time. Thus, adapting to new and forgetting old information must be traded off. Finally, the target user should not have to do any work to use the recognition system by labeling activities. Our work addresses this challenges by proposing an unsupervised online domain adaptation algorithm. It works by aligning the feature distribution of all the subjects, sources and target, within deep neural network layers.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2020-05-29&diff=1302Institutsseminar/2020-05-292020-02-05T09:27:59Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2020/05/29 11:30:00 |raum=Raum 010 (Gebäude 50.34) }} Achtung: Raum 348 an diesem Tag ist nicht verfügbar.“</p>
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Achtung: Raum 348 an diesem Tag ist nicht verfügbar.</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Elaheh_Ordoni&diff=1272Elaheh Ordoni2020-01-09T10:34:22Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Betreuer |email=elaheh.ordoni@kit.edu |homepage=https://dbis.ipd.kit.edu/2548.php |lehrstuhl=IPD Böhm }}“</p>
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|email=elaheh.ordoni@kit.edu<br />
|homepage=https://dbis.ipd.kit.edu/2548.php<br />
|lehrstuhl=IPD Böhm<br />
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<div>{{Termin<br />
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<div>{{Vortrag<br />
|vortragender=Emmanouil Emmanouilidis<br />
|email=ubesb@student.kit.edu<br />
|vortragstyp=Proposal<br />
|betreuer=Vadim Arzamasov<br />
|termin=Institutsseminar/2019-11-29 Zusatztermin<br />
|kurzfassung=PRIM (Patient Rule Induction Method) is an algorithm to create hyperboxes that are human comprehansable. But PRIM requires relatively large datasets. It has been shown, that using ML models (e.g. Random Forrest) that generalize faster can increase performance by around 75%.<br />
In this Thesis we are trying to increase the overall performance even further, using an active learning approach in order to train the models. Acquiring labels for a given dataset can be quite costly, with active learning only a small part of the dataset has to ben labeled (if at all). Furthermore, a preliminary experiment indicated, that combining these methods does indeed increase performance even further.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2019-11-29_Zusatztermin&diff=1227Institutsseminar/2019-11-29 Zusatztermin2019-11-25T15:42:36Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2019/11/29 11:30:00 |raum=Raum 010 (Gebäude 50.34) }}“</p>
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<div>{{Vortrag<br />
|vortragender=Florian Kalinke<br />
|email=utzzc@student.kit.edu<br />
|vortragstyp=Masterarbeit<br />
|betreuer=Edouard Fouché<br />
|termin=Institutsseminar/2019-11-22<br />
|kurzfassung=Modern data mining often takes place on high-dimensional data streams, which evolve at a very fast pace: On the one hand, the "curse of dimensionality" leads to a sparsely populated feature space, for which classical statistical methods perform poorly. Patterns, such as clusters or outliers, often hide in a few low-dimensional subspaces. On the other hand, data streams are non-stationary and virtually unbounded. Hence, algorithms operating on data streams must work incrementally and take concept drift into account. <br />
<br />
While "high-dimensionality" and the "streaming setting" provide two unique sets of challenges, we observe that the existing mining algorithms only address them separately. Thus, our plan is to propose a novel algorithm, which keeps track of the subspaces of interest in high-dimensional data streams over time. We quantify the relevance of subspaces via a so-called "contrast" measure, which we are able to maintain incrementally in an efficient way. Furthermore, we propose a set of heuristics to adapt the search for the relevant subspaces as the data and the underlying distribution evolves.<br />
<br />
We show that our approach is beneficial as a feature selection method and as such can be applied to extend a range of knowledge discovery tasks, e.g., "outlier detection", in high-dimensional data-streams.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Anytime_Tradeoff_Strategies_with_Multiple_Targets&diff=1206Anytime Tradeoff Strategies with Multiple Targets2019-11-12T14:05:22Z<p>Nv3463: </p>
<hr />
<div>{{Vortrag<br />
|vortragender=Marco Heyden<br />
|email=heydenmarco48@gmail.com<br />
|vortragstyp=Masterarbeit<br />
|betreuer=Edouard Fouché<br />
|termin=Institutsseminar/2019-11-22<br />
|kurzfassung=Modern applications typically need to find solutions to complex problems under limited time and resources. In settings, in which the exact computation of indicators can either be infeasible or economically undesirable, the use of “anytime” algorithms, which can return approximate results when interrupted, is particularly beneficial, since they offer a natural way to trade computational power for result accuracy.<br />
However, modern systems typically need to solve multiple problems simultaneously. E.g. in order to find high correlations in a dataset, one needs to examine each pair of variables. This is challenging, in particular if the number of variables is large and the data evolves dynamically.<br />
<br />
This thesis focuses on the following question: How should one distribute resources at anytime, in order to maximize the overall quality of multiple targets? <br />
First, we define the problem, considering various notions of quality and user requirements. Second, we propose a set of strategies to tackle this problem. Finally, we evaluate our strategies via extensive experiments.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Subspace_Search_in_Data_Streams&diff=1159Subspace Search in Data Streams2019-10-24T13:53:40Z<p>Nv3463: </p>
<hr />
<div>{{Vortrag<br />
|vortragender=Florian Kalinke<br />
|email=utzzc@student.kit.edu<br />
|vortragstyp=Masterarbeit<br />
|betreuer=Edouard Fouché<br />
|termin=Institutsseminar/2019-11-15 Zusatztermin<br />
|kurzfassung=Modern data mining often takes place on high-dimensional data streams, which evolve at a very fast pace: On the one hand, the "curse of dimensionality" leads to a sparsely populated feature space, for which classical statistical methods perform poorly. Patterns, such as clusters or outliers, often hide in a few low-dimensional subspaces. On the other hand, data streams are non-stationary and virtually unbounded. Hence, algorithms operating on data streams must work incrementally and take concept drift into account. <br />
<br />
While "high-dimensionality" and the "streaming setting" provide two unique sets of challenges, we observe that the existing mining algorithms only address them separately. Thus, our plan is to propose a novel algorithm, which keeps track of the subspaces of interest in high-dimensional data streams over time. We quantify the relevance of subspaces via a so-called "contrast" measure, which we are able to maintain incrementally in an efficient way. Furthermore, we propose a set of heuristics to adapt the search for the relevant subspaces as the data and the underlying distribution evolves.<br />
<br />
We show that our approach is beneficial as a feature selection method and as such can be applied to extend a range of knowledge discovery tasks, e.g., "outlier detection", in high-dimensional data-streams.<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Institutsseminar/2019-11-15_Zusatztermin&diff=1158Institutsseminar/2019-11-15 Zusatztermin2019-10-24T13:52:12Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Termin |datum=2019/11/15 11:30:00 |raum=Raum 010 (Gebäude 50.34) }}“</p>
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<div>{{Termin<br />
|datum=2019/11/15 11:30:00<br />
|raum=Raum 010 (Gebäude 50.34)<br />
}}</div>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Pawel_Bielski&diff=1152Pawel Bielski2019-10-15T14:56:34Z<p>Nv3463: Die Seite wurde neu angelegt: „{{Betreuer |email=pawel.bielski@kit.edu |homepage=https://dbis.ipd.kit.edu/2736.php |lehrstuhl=IPD Böhm }}“</p>
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<div>{{Betreuer<br />
|email=pawel.bielski@kit.edu<br />
|homepage=https://dbis.ipd.kit.edu/2736.php<br />
|lehrstuhl=IPD Böhm<br />
}}</div>Nv3463