Search (27 results, page 1 of 2)

  • × theme_ss:"Data Mining"
  1. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.04 
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    Date
    22.11.1998 18:57:22
    Source
    Online. 21(1997) no.6, S.87-92
  2. Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008) 0.02 
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    Content
    "Vor Google gibt es kein Entrinnen. Nun macht sich die größte Internetsuchmaschine daran, auch gefährliche Grippewellen in den USA vorauszusagen - und das schneller als die US-Gesundheitsbehörde. In den Regionen, in denen die Influenza grassiert, häufen sich erfahrungsgemäß auch die Online-Anfragen im Internet speziell zu diesem Thema. "Wir haben einen engen Zusammenhang feststellen können zwischen Personen, die nach themenbezogenen Informationen suchen, und Personen, die tatsächlich an der Grippe erkrankt sind", schreibt Google. Ein Webtool namens "Google Flu Trends" errechnet aus den Anfragen die Ausbreitung von Grippeviren. Auch wenn nicht jeder Nutzer erkrankt sei, spiegele die Zahl der Anfragen doch ziemlich genau die Entwicklung einer Grippewelle wider. Das belege ein Vergleich mit den Daten der US-Seuchenkontrollbehörde CDC, die in den meisten Fällen nahezu identisch seien. Die Internet-Suchmaschine könne anders als die Gesundheitsbehörde täglich auf aktuelle Daten zurückgreifen. Dadurch sei Google in der Lage, die Grippesaison ein bis zwei Wochen früher vorherzusagen. Und Zeit bedeutet Leben, wie Lyn Finelli sagt, Leiter der Abteilung Influenza der USSeuchenkontrollbehörde: "Je früher wir gewarnt werden, desto früher können wir handeln. Dies kann die Anzahl der Erkrankten erheblich minimieren." "Google Flu Trends" ist das erste Projekt, das Datenbanken einer Suchmaschine nutzt, um einen auftretenden Grippevirus zu lokalisieren - zurzeit nur in den USA, aber weltweite Prognosen wären ein folgerichtiger nächster Schritt. Philip M. Polgreen von der Universität von Iowa verspricht sich noch viel mehr: "Theoretisch können wir diese Flut an Informationen dazu nutzen, auch den Verlauf anderer Krankheiten besser zu studieren." Um das Grippe-Ausbreitungsmodell zu erstellen, hat Google mehrere hundert Milliarden Suchanfragen aus den vergangenen Jahren analysiert. Datenschützer haben den Internetgiganten bereits mehrfach als "datenschutzfeindlich" eingestuft. Die Anwender wüssten weder, was mit den gesammelten Daten passiere, noch wie lange gespeicherte Informationen verfügbar seien. Google versichert jedoch, dass "Flu Trends" die Privatsphäre wahre. Das Tool könne niemals dazu genutzt werden, einzelne Nutzer zu identifizieren, da wir bei der Erstellung der Statistiken lediglich anonyme Datenmaterialien nutzen. Die Muster, die wir in den Daten analysieren, ergeben erst in einem größeren Kontext Sinn." An einer echten Virus-Grippe - nicht zu verwechseln mit einer Erkältung - erkranken weltweit mehrere Millionen Menschen, mehr als 500 000 sterben daran."
    Date
    3. 5.1997 8:44:22
  3. Jäger, L.: Von Big Data zu Big Brother (2018) 0.02 
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    Abstract
    1983 bewegte ein einziges Thema die gesamte Bundesrepublik: die geplante Volkszählung. Jeder Haushalt in Westdeutschland sollte Fragebögen mit 36 Fragen zur Wohnsituation, den im Haushalt lebenden Personen und über ihre Einkommensverhältnisse ausfüllen. Es regte sich massiver Widerstand, hunderte Bürgerinitiativen formierten sich im ganzen Land gegen die Befragung. Man wollte nicht "erfasst" werden, die Privatsphäre war heilig. Es bestand die (berechtigte) Sorge, dass die Antworten auf den eigentlich anonymisierten Fragebögen Rückschlüsse auf die Identität der Befragten zulassen. Das Bundesverfassungsgericht gab den Klägern gegen den Zensus Recht: Die geplante Volkszählung verstieß gegen den Datenschutz und damit auch gegen das Grundgesetz. Sie wurde gestoppt. Nur eine Generation später geben wir sorglos jedes Mal beim Einkaufen die Bonuskarte der Supermarktkette heraus, um ein paar Punkte für ein Geschenk oder Rabatte beim nächsten Einkauf zu sammeln. Und dabei wissen wir sehr wohl, dass der Supermarkt damit unser Konsumverhalten bis ins letzte Detail erfährt. Was wir nicht wissen, ist, wer noch Zugang zu diesen Daten erhält. Deren Käufer bekommen nicht nur Zugriff auf unsere Einkäufe, sondern können über sie auch unsere Gewohnheiten, persönlichen Vorlieben und Einkommen ermitteln. Genauso unbeschwert surfen wir im Internet, googeln und shoppen, mailen und chatten. Google, Facebook und Microsoft schauen bei all dem nicht nur zu, sondern speichern auf alle Zeiten alles, was wir von uns geben, was wir einkaufen, was wir suchen, und verwenden es für ihre eigenen Zwecke. Sie durchstöbern unsere E-Mails, kennen unser persönliches Zeitmanagement, verfolgen unseren momentanen Standort, wissen um unsere politischen, religiösen und sexuellen Präferenzen (wer kennt ihn nicht, den Button "an Männern interessiert" oder "an Frauen interessiert"?), unsere engsten Freunde, mit denen wir online verbunden sind, unseren Beziehungsstatus, welche Schule wir besuchen oder besucht haben und vieles mehr.
    Date
    22. 1.2018 11:33:49
  4. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.02 
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    Date
    2. 4.2000 18:01:22
  5. Tunbridge, N.: Semiology put to data mining (1999) 0.02 
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    Source
    Online and CD-ROM review. 23(1999) no.5, S.303-305
  6. KDD : techniques and applications (1998) 0.02 
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  7. Fayyad, U.M.; Djorgovski, S.G.; Weir, N.: From digitized images to online catalogs : data ming a sky server (1996) 0.01 
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    Abstract
    Offers a data mining approach based on machine learning classification methods to the problem of automated cataloguing of online databases of digital images resulting from sky surveys. The SKICAT system automates the reduction and analysis of 3 terabytes of images expected to contain about 2 billion sky objects. It offers a solution to problems associated with the analysis of large data sets in science
  8. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.01 
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    Date
    17. 7.2002 19:22:06
  9. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01 
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  10. O'Brien, H.L.; Lebow, M.: Mixed-methods approach to measuring user experience in online news interactions (2013) 0.01 
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    Abstract
    When it comes to evaluating online information experiences, what metrics matter? We conducted a study in which 30 people browsed and selected content within an online news website. Data collected included psychometric scales (User Engagement, Cognitive Absorption, System Usability Scales), self-reported interest in news content, and performance metrics (i.e., reading time, browsing time, total time, number of pages visited, and use of recommended links); a subset of the participants had their physiological responses recorded during the interaction (i.e., heart rate, electrodermal activity, electrocmytogram). Findings demonstrated the concurrent validity of the psychometric scales and interest ratings and revealed that increased time on tasks, number of pages visited, and use of recommended links were not necessarily indicative of greater self-reported engagement, cognitive absorption, or perceived usability. Positive ratings of news content were associated with lower physiological activity. The implications of this research are twofold. First, we propose that user experience is a useful framework for studying online information interactions and will result in a broader conceptualization of information interaction and its evaluation. Second, we advocate a mixed-methods approach to measurement that employs a suite of metrics capable of capturing the pragmatic (e.g., usability) and hedonic (e.g., fun, engagement) aspects of information interactions. We underscore the importance of using multiple measures in information research, because our results emphasize that performance and physiological data must be interpreted in the context of users' subjective experiences.
  11. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.01 
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  12. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.01 
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    Date
    22. 3.2008 14:46:06
  13. Liu, W.; Weichselbraun, A.; Scharl, A.; Chang, E.: Semi-automatic ontology extension using spreading activation (2005) 0.01 
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    Abstract
    This paper describes a system to semi-automatically extend and refine ontologies by mining textual data from the Web sites of international online media. Expanding a seed ontology creates a semantic network through co-occurrence analysis, trigger phrase analysis, and disambiguation based on the WordNet lexical dictionary. Spreading activation then processes this semantic network to find the most probable candidates for inclusion in an extended ontology. Approaches to identifying hierarchical relationships such as subsumption, head noun analysis and WordNet consultation are used to confirm and classify the found relationships. Using a seed ontology on "climate change" as an example, this paper demonstrates how spreading activation improves the result by naturally integrating the mentioned methods.
  14. Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010) 0.01 
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    Abstract
    With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine-grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature-opinion tuples are generated. Experimental results on real-world datasets show that the proposed approach is effective.
  15. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01 
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    Date
    22. 3.2013 19:43:01
  16. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01 
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  17. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01 
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    Date
    7. 3.2019 16:32:22
  18. Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003) 0.01 
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    Date
    22. 4.2003 11:45:36
  19. Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014) 0.01 
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    Abstract
    An organization performing environmental scanning generally monitors or tracks various events concerning its external environment. One of the major resources for environmental scanning is online news documents, which are readily accessible on news websites or infomediaries. However, the proliferation of the World Wide Web, which increases information sources and improves information circulation, has vastly expanded the amount of information to be scanned. Thus, it is essential to develop an effective event episode discovery mechanism to organize news documents pertaining to an event of interest. In this study, we propose two new metrics, Term Frequency × Inverse Document FrequencyTempo (TF×IDFTempo) and TF×Enhanced-IDFTempo, and develop a temporal-based event episode discovery (TEED) technique that uses the proposed metrics for feature selection and document representation. Using a traditional TF×IDF-based hierarchical agglomerative clustering technique as a performance benchmark, our empirical evaluation reveals that the proposed TEED technique outperforms its benchmark, as measured by cluster recall and cluster precision. In addition, the use of TF×Enhanced-IDFTempo significantly improves the effectiveness of event episode discovery when compared with the use of TF×IDFTempo.
  20. Jones, K.M.L.; Rubel, A.; LeClere, E.: ¬A matter of trust : higher education institutions as information fiduciaries in an age of educational data mining and learning analytics (2020) 0.01 
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    Abstract
    Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of "learning analytics," this work can-and often does-surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.

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  • e 19
  • d 8

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