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  • × theme_ss:"Data Mining"
  1. Medien-Informationsmanagement : Archivarische, dokumentarische, betriebswirtschaftliche, rechtliche und Berufsbild-Aspekte ; [Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar und Folgetagung 2001 in Köln] (2003) 0.01
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    Abstract
    Als in den siebziger Jahren des vergangenen Jahrhunderts immer häufiger die Bezeichnung Informationsmanager für Leute propagiert wurde, die bis dahin als Dokumentare firmierten, wurde dies in den etablierten Kreisen der Archivare und Bibliothekare gelegentlich belächelt und als Zeichen einer Identitätskrise oder jedenfalls einer Verunsicherung des damit überschriebenen Berufsbilds gewertet. Für den Berufsstand der Medienarchivare/Mediendokumentare, die sich seit 1960 in der Fachgruppe 7 des Vereins, später Verbands deutscher Archivare (VdA) organisieren, gehörte diese Verortung im Zeichen neuer inhaltlicher Herausforderungen (Informationsflut) und Technologien (EDV) allerdings schon früh zu den Selbstverständlichkeiten des Berufsalltags. "Halt, ohne uns geht es nicht!" lautete die Überschrift eines Artikels im Verbandsorgan "Info 7", der sich mit der Einrichtung von immer mächtigeren Leitungsnetzen und immer schnelleren Datenautobahnen beschäftigte. Information, Informationsgesellschaft: diese Begriffe wurden damals fast nur im technischen Sinne verstanden. Die informatisierte, nicht die informierte Gesellschaft stand im Vordergrund - was wiederum Kritiker auf den Plan rief, von Joseph Weizenbaum in den USA bis hin zu den Informations-Ökologen in Bremen. Bei den nationalen, manchmal auch nur regionalen Projekten und Modellversuchen mit Datenautobahnen - auch beim frühen Btx - war nie so recht deutlich geworden, welche Inhalte in welcher Gestalt durch diese Netze und Straßen gejagt werden sollten und wer diese Inhalte eigentlich selektieren, portionieren, positionieren, kurz: managen sollte. Spätestens mit dem World Wide Web sind diese Projekte denn auch obsolet geworden, jedenfalls was die Hardware und Software anging. Geblieben ist das Thema Inhalte (neudeutsch: Content). Und - immer drängender im nicht nur technischen Verständnis - das Thema Informationsmanagement. MedienInformationsManagement war die Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar überschrieben, und auch die Folgetagung 2001 in Köln, die der multimedialen Produktion einen dokumentarischen Pragmatismus gegenüber stellte, handelte vom Geschäftsfeld Content und von Content-Management-Systemen. Die in diesem 6. Band der Reihe Beiträge zur Mediendokumentation versammelten Vorträge und Diskussionsbeiträge auf diesen beiden Tagungen beleuchten das Titel-Thema aus den verschiedensten Blickwinkeln: archivarischen, dokumentarischen, kaufmännischen, berufsständischen und juristischen. Deutlich wird dabei, daß die Berufsbezeichnung Medienarchivarln/Mediendokumentarln ziemlich genau für all das steht, was heute mit sog. alten wie neuen Medien im organisatorischen, d.h. ordnenden und vermittelnden Sinne geschieht. Im besonderen Maße trifft dies auf das Internet und die aus ihm geborenen Intranets zu. Beide bedürfen genauso der ordnenden Hand, die sich an den alten Medien, an Buch, Zeitung, Tonträger, Film etc. geschult hat, denn sie leben zu großen Teilen davon. Daß das Internet gleichwohl ein Medium sui generis ist und die alten Informationsberufe vor ganz neue Herausforderungen stellt - auch das durchzieht die Beiträge von Weimar und Köln.
    Date
    11. 5.2008 19:49:22
  2. Knowledge management in fuzzy databases (2000) 0.01
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    Abstract
    The volume presents recent developments in the introduction of fuzzy, probabilistic and rough elements into basic components of fuzzy databases, and their use (notably querying and information retrieval), from the point of view of data mining and knowledge discovery. The main novel aspect of the volume is that issues related to the use of fuzzy elements in databases, database querying, information retrieval, etc. are presented and discussed from the point of view, and for the purpose of data mining and knowledge discovery that are 'hot topics' in recent years
  3. Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003) 0.01
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    Abstract
    With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.
  4. Whittle, M.; Eaglestone, B.; Ford, N.; Gillet, V.J.; Madden, A.: Data mining of search engine logs (2007) 0.01
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    Abstract
    This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of query transformation. It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web-based searching by the general public.
  5. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.01
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    Date
    2. 4.2000 18:01:22
  6. Zhang, Z.; Li, Q.; Zeng, D.; Ga, H.: Extracting evolutionary communities in community question answering (2014) 0.01
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    Abstract
    With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision-making basis for business.
  7. KDD : techniques and applications (1998) 0.00
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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
  8. Chakrabarti, S.: Mining the Web : discovering knowledge from hypertext data (2003) 0.00
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    Footnote
    Rez. in: JASIST 55(2004) no.3, S.275-276 (C. Chen): "This is a book about finding significant statistical patterns on the Web - in particular, patterns that are associated with hypertext documents, topics, hyperlinks, and queries. The term pattern in this book refers to dependencies among such items. On the one hand, the Web contains useful information an just about every topic under the sun. On the other hand, just like searching for a needle in a haystack, one would need powerful tools to locate useful information an the vast land of the Web. Soumen Chakrabarti's book focuses an a wide range of techniques for machine learning and data mining an the Web. The goal of the book is to provide both the technical Background and tools and tricks of the trade of Web content mining. Much of the technical content reflects the state of the art between 1995 and 2002. The targeted audience is researchers and innovative developers in this area, as well as newcomers who intend to enter this area. The book begins with an introduction chapter. The introduction chapter explains fundamental concepts such as crawling and indexing as well as clustering and classification. The remaining eight chapters are organized into three parts: i) infrastructure, ii) learning and iii) applications.
  9. Cohen, D.J.: From Babel to knowledge : data mining large digital collections (2006) 0.00
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    Abstract
    In Jorge Luis Borges's curious short story The Library of Babel, the narrator describes an endless collection of books stored from floor to ceiling in a labyrinth of countless hexagonal rooms. The pages of the library's books seem to contain random sequences of letters and spaces; occasionally a few intelligible words emerge in the sea of paper and ink. Nevertheless, readers diligently, and exasperatingly, scan the shelves for coherent passages. The narrator himself has wandered numerous rooms in search of enlightenment, but with resignation he simply awaits his death and burial - which Borges explains (with signature dark humor) consists of being tossed unceremoniously over the library's banister. Borges's nightmare, of course, is a cursed vision of the research methods of disciplines such as literature, history, and philosophy, where the careful reading of books, one after the other, is supposed to lead inexorably to knowledge and understanding. Computer scientists would approach Borges's library far differently. Employing the information theory that forms the basis for search engines and other computerized techniques for assessing in one fell swoop large masses of documents, they would quickly realize the collection's incoherence though sampling and statistical methods - and wisely start looking for the library's exit. These computational methods, which allow us to find patterns, determine relationships, categorize documents, and extract information from massive corpuses, will form the basis for new tools for research in the humanities and other disciplines in the coming decade. For the past three years I have been experimenting with how to provide such end-user tools - that is, tools that harness the power of vast electronic collections while hiding much of their complicated technical plumbing. In particular, I have made extensive use of the application programming interfaces (APIs) the leading search engines provide for programmers to query their databases directly (from server to server without using their web interfaces). In addition, I have explored how one might extract information from large digital collections, from the well-curated lexicographic database WordNet to the democratic (and poorly curated) online reference work Wikipedia. While processing these digital corpuses is currently an imperfect science, even now useful tools can be created by combining various collections and methods for searching and analyzing them. And more importantly, these nascent services suggest a future in which information can be gleaned from, and sense can be made out of, even imperfect digital libraries of enormous scale. A brief examination of two approaches to data mining large digital collections hints at this future, while also providing some lessons about how to get there.
  10. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.00
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    Date
    22.11.1998 18:57:22
  11. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.00
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    Date
    17. 7.2002 19:22:06
  12. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.00
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  13. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.00
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  14. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.00
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    Date
    22. 3.2008 14:46:06
  15. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.00
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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) 0.00
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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) 0.00
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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) 0.00
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