-
Sugimoto, C.R.; Mostafa, J.: ¬A note of concern and context : on careful use of terminologies (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.3, S.347-348
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Zhang, Y.; Wu, D.; Hagen, L.; Song, I.-Y.; Mostafa, J.; Oh, S.; Anderson, T.; Shah, C.; Bishop, B.W.; Hopfgartner, F.; Eckert, K.; Federer, L.; Saltz, J.S.: Data science curriculum in the iField (2023)
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- Abstract
- Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.
- Footnote
- Beitrag in einem Special issue on "Data Science in the iField".
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.6, S.641-662
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Mostafa, J.; Hall, D.M.: ¬A proposed quantitative methodology to characterize the corporate library universe : the role of personal research interests (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.5, S.760
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Mostafa, J.: Bessere Suchmaschinen für das Web (2006)
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- Content
- An der Wurzel des Indexbaums Im ersten Schritt werden potenziell interessante Inhalte identifiziert und fortlaufend gesammelt. Spezielle Programme vom Typ so genannter Webtrawler können im Internet publizierte Seiten ausfindig machen, durchsuchen (inklusive darauf befindlicher Links) und die Seiten an einem Ort gesammelt speichern. Im zweiten Schritt erfasst das System die relevanten Wörter auf diesen Seiten und bestimmt mit statistischen Methoden deren Wichtigkeit. Drittens wird aus den relevanten Begriffen eine hocheffiziente baumartige Datenstruktur erzeugt, die diese Begriffe bestimmten Webseiten zuordnet. Gibt ein Nutzer eine Anfrage ein, wird nur der gesamte Baum - auch Index genannt - durchsucht und nicht jede einzelne Webseite. Die Suche beginnt an der Wurzel des Indexbaums, und bei jedem Suchschritt wird eine Verzweigung des Baums (die jeweils viele Begriffe und zugehörige Webseiten beinhaltet) entweder weiter verfolgt oder als irrelevant verworfen. Dies verkürzt die Suchzeiten dramatisch. Um die relevanten Fundstellen (oder Links) an den Anfang der Ergebnisliste zu stellen, greift der Suchalgorithmus auf verschiedene Sortierstrategien zurück. Eine verbreitete Methode - die Begriffshäufigkeit - untersucht das Vorkommen der Wörter und errechnet daraus numerische Gewichte, welche die Bedeutung der Wörter in den einzelnen Dokumenten repräsentieren. Häufige Wörter (wie »oder«, »zu«, »mit«), die in vielen Dokumenten auftauchen, erhalten deutlich niedrigere Gewichte als Wörter, die eine höhere semantische Relevanz aufweisen und nur in vergleichsweise wenigen Dokumenten zu finden sind. Webseiten können aber auch nach anderen Strategien indiziert werden. Die Linkanalyse beispielsweise untersucht Webseiten nach dem Kriterium, mit welchen anderen Seiten sie verknüpft sind. Dabei wird analysiert, wie viele Links auf eine Seite verweisen und von dieser Seite selbst ausgehen. Google etwa verwendet zur Optimierung der Suchresultate diese Linkanalyse. Sechs Jahre benötigte Google, um sich als führende Suchmaschine zu etablieren. Zum Erfolg trugen vor allem zwei Vorzüge gegenüber der Konkurrenz bei: Zum einen kann Google extrem große Weberawling-Operationen durchführen. Zum anderen liefern seine Indizierungsund Gewichtungsmethoden überragende Ergebnisse. In letzter Zeit jedoch haben andere Suchmaschinen-Entwickler einige neue, ähnlich leistungsfähige oder gar punktuell bessere Systeme entwickelt.
- Date
- 22. 1.2006 18:34:49
-
Mongin, L.; Fu, Y.Y.; Mostafa, J.: Open Archives data Service prototype and automated subject indexing using D-Lib archive content as a testbed (2003)
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- Abstract
- The Indiana University School of Library and Information Science opened a new research laboratory in January 2003; The Indiana University School of Library and Information Science Information Processing Laboratory [IU IP Lab]. The purpose of the new laboratory is to facilitate collaboration between scientists in the department in the areas of information retrieval (IR) and information visualization (IV) research. The lab has several areas of focus. These include grid and cluster computing, and a standard Java-based software platform to support plug and play research datasets, a selection of standard IR modules and standard IV algorithms. Future development includes software to enable researchers to contribute datasets, IR algorithms, and visualization algorithms into the standard environment. We decided early on to use OAI-PMH as a resource discovery tool because it is consistent with our mission.
-
Heffron, J.K.; Dillon, A.; Mostafa, J.: Landmarks in the World Wide Web : a preliminary study (1996)
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- Source
- Global complexity: information, chaos and control. Proceedings of the 59th Annual Meeting of the American Society for Information Science, ASIS'96, Baltimore, Maryland, 21-24 Oct 1996. Ed.: S. Hardin
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Mostafa, J.: Digital image representation and access (1994)
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- Source
- Annual review of information science and technology. 29(1994), S.91-135
-
Lam, W.; Mostafa, J.: Modeling user interest shift using a Baysian approach (2001)
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- Source
- Journal of the American Society for Information Science and technology. 52(2001) no.5, S.416-429
-
Mostafa, J.; Dillon, A.: Design and evaluation of a user interface supporting multiple image query models (1996)
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- Source
- Global complexity: information, chaos and control. Proceedings of the 59th Annual Meeting of the American Society for Information Science, ASIS'96, Baltimore, Maryland, 21-24 Oct 1996. Ed.: S. Hardin
-
Mostafa, J.; Quiroga, L.M.; Palakal, M.: Filtering medical documents using automated and human classification methods (1998)
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- Source
- Journal of the American Society for Information Science. 49(1998) no.14, S.1304-1318
-
Mukhopadhyay, S.; Peng, S.; Raje, R.; Palakal, M.; Mostafa, J.: Multi-agent information classification using dynamic acquaintance lists (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.10, S.966-975
-
Mostafa, J.: Document search interface design : background and introduction to special topic section (2004)
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- Source
- Journal of the American Society for Information Science and Technology. 55(2004) no.10, S.869-872
-
Mukhopadhyay, S.; Peng, S.; Raje, R.; Mostafa, J.; Palakal, M.: Distributed multi-agent information filtering : a comparative study (2005)
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- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.8, S.834-842
-
Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002)
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- Abstract
- From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
