Pattern research in the digital humanities: how data mining techniques support the identification of costume patterns

Falkenthal, Michael; Barzen, Johanna; Breitenbücher, Uwe; Brügmann, Sascha; Joos, Daniel; Leymann, Frank; Wurster, Michael

doi:10.1007/s00450-016-0331-6

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…As a first approach to analyze the data to identify those significant elements a costume designer uses to achieve a certain effect, a two-step analysis process was introduced [19]. The first step applies data mining techniques -mainly association rule mining -to determine hypotheses about which elements are used to communicate a certain stereotype, for example.…”

Section: Discussionmentioning

confidence: 99%

Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

Barzen

Leymann

Falkenthal³

et al. 2021

Communications in Computer and Information Science

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As quantum computers are becoming real, they have the inherent potential to significantly impact many application domains. In this paper we outline the fundamentals about programming quantum computers and show that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from quantum humanities is discussed, hinting which applications in this domain can already be used in the field of (quantum) machine learning. Finally, a collaborative platform for solving problems with quantum computers-that is currently under construction-is presented.

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Section: Discussionmentioning

confidence: 99%

Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

Barzen

Leymann

Falkenthal³

et al. 2021

Communications in Computer and Information Science

Self Cite

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“…With the rapid development of Internet technology, people have an increasing number of channels for obtaining information, and their lives have become increasingly convenient (Faria et al, 2017). However, a large amount of information also brings many problems: too much information is difficult to assimilate; the amount of information is difficult to distinguish between true and false; information security is difficult to guarantee (Falkenthal et al, 2017). In this case, data mining technology is introduced.…”

Section: Data Miningmentioning

confidence: 99%

Stock Price Prediction Based on Data Mining Combination Model

Chang

Wang

Chuang

2022

Journal of Global Information Management

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Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.

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“…For the identification of new patterns, prior work in this field has already presented a variety of techniques, such as experience-based [8] and collaborative [39,40], but also semiautomated approaches [12]. Identifying teaching patterns that describe effective teaching requires not only describing and characterizing the implementation of teaching practice, but also evaluating its impact.…”

Section: Pattern Miningmentioning

confidence: 99%

“…The Apriori algorithm for association rule mining was initially presented in [47] for the prediction of shopping patterns of customers (as known for "you may also like to buy") and was already applied for pattern research in the field of digital humanities [12]. The idea of the Apriori algorithm approach is to first discover frequent occurring items (support) from a dataset and then identify the likeliness of the occurence of a one itemset, if another itemset is already present (confidence).…”

Section: Pattern Miningmentioning

confidence: 99%

“…The pattern approach, originally introduced by Christopher Alexander in the field of architecture [4,5], was later applied in computer science in object-oriented design [6] and also in education to describe teaching-learning scenarios in schools [7][8][9] and in tertiary education in the field of technology-enhanced learning [10,11]. In this paper, we describe a semi-automated approach, which is based on related work in the field of digital humanities [12,13] and further combine expertise from different domains such as educational science, computer science, and teaching practice.…”

Section: Introductionmentioning

confidence: 99%

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A Pattern Mining Method for Teaching Practices

Standl

Schlomske-Bodenstein

2021

Future Internet

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When integrating digital technology into teaching, many teachers experience similar challenges. Nevertheless, sharing experiences is difficult as it is usually not possible to transfer teaching scenarios directly from one subject to another because subject-specific characteristics make it difficult to reuse them. To address this problem, instructional scenarios can be described as patterns, which has already been applied in educational contexts. Patterns capture proven teaching strategies and describe teaching scenarios in a unified structure that can be reused. Since priorities for content, methods, and tools are different in each subject, we show an approach to develop a domain-independent graph database to collect digital teaching practices from a taxonomic structure via the intermediate step of an ontology. Furthermore, we outline a method to identify effective teaching practices from interdisciplinary data as patterns from the graph database using an association rule algorithm. The results show that an association-based analysis approach can derive initial indications of effective teaching scenarios.

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Pattern research in the digital humanities: how data mining techniques support the identification of costume patterns

Cited by 5 publications

References 7 publications

Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

Stock Price Prediction Based on Data Mining Combination Model

A Pattern Mining Method for Teaching Practices

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