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Auflistung nach Schlagwort "Text mining"

1 - 6 von 6
  • Zeitschriftenartikel
    A Brief Tutorial on How to Extract Information from User-Generated Content (UGC)
    (KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Egger, Marc; Lang, André
    In this brief tutorial, we provide an overview of investigating text-based user-generated content for information that is relevant in the corporate context. We structure the overall process along three stages: collection, analysis, and visualization. Corresponding to the stages we outline challenges and basic techniques to extract information of different levels of granularity.
  • Zeitschriftenartikel
    Exploring Information Systems Curricula
    (Business & Information Systems Engineering: Vol. 63, No. 6, 2021) Föll, Patrick; Thiesse, Frédéric
    The study considers the application of text mining techniques to the analysis of curricula for study programs offered by institutions of higher education. It presents a novel procedure for efficient and scalable quantitative content analysis of module handbooks using topic modeling. The proposed approach allows for collecting, analyzing, evaluating, and comparing curricula from arbitrary academic disciplines as a partially automated, scalable alternative to qualitative content analysis, which is traditionally conducted manually. The procedure is illustrated by the example of IS study programs in Germany, based on a data set of more than 90 programs and 3700 distinct modules. The contributions made by the study address the needs of several different stakeholders and provide insights into the differences and similarities among the study programs examined. For example, the results may aid academic management in updating the IS curricula and can be incorporated into the curricular design process. With regard to employers, the results provide insights into the fulfillment of their employee skill expectations by various universities and degrees. Prospective students can incorporate the results into their decision concerning where and what to study, while university sponsors can utilize the results in their grant processes.
  • Konferenzbeitrag
    GoPubMed: ontology-based literature search applied to gene ontology and pubmed
    (German Conference on Bioinformatics 2004, GCB 2004, 2004) Delfs, Ralph; Doms, Andreas; Kozlenkov, Alexander; Schroeder, Michael
    The biomedical literature grows at a tremendous rate, so that finding the relevant literature is becoming more and more difficult. To address this problem we introduce ontology-based literature search, which structures search results thorugh the categories of an ontology. We develop and implement GoP- ubMed, which submits keywords to PubMed, extracts GeneOntology-terms from the retrieved abstracts, and presents the relevant sub-ontology for browsing. For GoPubMed we develop a novel term extraction algorithm and evaluate its performance. GoPubMed is available at www.gopubmed.org
  • Textdokument
    Preparing clinical ophthalmic data for research application
    (INFORMATIK 2017, 2017) Rößner, Miriam; Kahl, Stefan; Engelmann, Katrin; Kowerko, Danny
    This paper presents an analysis of clinical examination, diagnostic and patient data belonging to persons with eye diseases like age-related macular degeneration (AMD). Our purpose is to investigate potential correlations of extracted features to discover their impacts on the disease. This is a first step to the predictability of the progression of AMD based on a heterogeneous data set. We focus on the visual acuity as reasonable indicator for the progression of this disease and analyse its temporal trend to classify patients in winners, stabilisers and losers.We describe the retrieval of textual medical reporting data for optical coherence tomography images and evaluate the machine-readable categorisation of these texts. Additionally, we address the topic of ethical guidelines for the work with patients’ data and discuss the potential and limitations of our data set in the context of obtaining structured (mass) data for training neural networks as future perspective.
  • Zeitschriftenartikel
    The Role of Gender in Business Process Management Competence Supply
    (Business & Information Systems Engineering: Vol. 58, No. 3, 2016) Gorbacheva, Elena; Stein, Armin; Schmiedel, Theresa; Müller, Oliver
    While Business Process Management (BPM) was originally focused on Information Technology as a key factor driving the efficiency and effectiveness of organizational processes, there is now a growing consensus among practitioners and academics that BPM represents a holistic management approach that also takes such factors as corporate governance, human capital, and organizational culture into account. Studies show that the BPM practice faces a shortage of competence supply that stems from a shortage of qualified BPM professionals. At the same time, there is a distinct underrepresentation of women in technology-related fields; it has been suggested that gender stereotypes are one of the reasons for this underrepresentation. The goal of this research paper is, thus, to better understand the role of gender in the BPM competences supply. In this study 10,405 LinkedIn profiles of BPM professionals were analyzed using a text mining technique called Latent Semantic Analysis. Twelve distinct categories of supplied BPM competences were identified and it was investigated how far gender biases exist among BPM professionals. The nature of BPM-related competences is discussed, together with the differences in their presentation by male and female professionals, which indicate potential existence of gender stereotypes. Further, it is discussed how the apparent underrepresentation of women among BPM professionals can be addressed to close the competence gap in the field. The study contributes to both the call for research on human capital in the BPM field, and the calls for research on gender and gender stereotypes in technology-related fields.
  • Zeitschriftenartikel
    Vergleich von Kompetenzanforderungen an Business-Intelligence- und Big-Data-Spezialisten
    (Wirtschaftsinformatik: Vol. 56, No. 5, 2014) Debortoli, Stefan; Müller, Oliver; Brocke, Jan vom
    Während sich die meisten wissenschaftlichen Studien zum Thema „Big Data“ mit den technischen Möglichkeiten zur Bewältigung von riesigen Datenmengen beschäftigen, sind empirische Untersuchungen in Bezug auf die von Fachleuten verlangten Kompetenzen für das Management and die Analyse von Big Data bislang noch nicht durchgeführt worden. Gleichzeitig diskutiert man in Wissenschaft und Praxis heftig über die Unterschiede und Gemeinsamkeiten von Big Data (BD) einerseits und „traditionellem“ Business Intelligence (BI) andererseits. Der vorliegende Artikel beschreibt die Durchführung einer Latenten Semantischen Analyse (LSA) von Stellenanzeigen auf dem Online-Portal monster.com, um Informationen darüber zu gewinnen, welche Anforderungen Unternehmen an Fachkräfte in den Bereichen BD und BI stellen. Auf Basis einer Analyse und Interpretation der statistischen Ergebnisse der LSA wird eine Taxonomie von Kompetenzanforderungen für BD bzw. BI entwickelt. Die wichtigsten Ergebnisse der Untersuchung lauten: (1) für beide Bereiche, BD und BI, ist Businesswissen genauso wichtig wie technisches Wissen; (2) kompetent sein im Bereich BI bezieht sich vorwiegend auf Wissen und Fähigkeiten in Bezug auf die Produkte der großen kommerziellen Softwareanbieter, während im Bereich BD eher Wissen und die Fähigkeiten in Bezug auf die Entwicklung von Individualsoftware und die Anwendung statistischer Methoden im Vordergrund steht; (3) die Nachfrage nach Kompetenz im Bereich BI ist immer noch weitaus größer als die Nachfrage nach Kompetenz im Bereich BD; und (4) BD-Projekte sind gegenwärtig wesentlich humankapital-intensiver als BI-Projekte. Die Ergebnisse und Erkenntnisse der Studie können Praktikern, Unternehmen und wissenschaftlichen Einrichtungen dabei helfen, ihre BD- bzw. BI-Kompetenz zu bewerten und zu erweitern.AbstractWhile many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.