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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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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.: Digital image representation and access (1994)
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- Abstract
- State of the art review of techniques used to generate, store and retrieval digital images. Explains basic terms and concepts related to image representation and describes the differences between bilevel, greyscale, and colour images. Introduces additional image related data, specifically colour standards, correction values, resolution parameters and lookup tables. Illustrates the use of data compression techniques and various image data formats that have been used. Identifies 4 branches of imaging research related to dtaa indexing and modelling: verbal indexing; visual surrogates; image indexing; and data structures. Concludes with a discussion of the state of the art in networking technology with consideration of image distribution, local system requirements and data integrity
- Source
- Annual review of information science and technology. 29(1994), S.91-135
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Mukhopadhyay, S.; Peng, S.; Raje, R.; Mostafa, J.; Palakal, M.: Distributed multi-agent information filtering : a comparative study (2005)
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- Abstract
- Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large-scale information infrastructure, e.g., digital libraries. To provide large-scale Information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single-agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium-scale applications. In this article, we discuss two distributed (multiagent) information filtering approaches, that are distributed with respect to knowledge or functionality, to overcome the limitations of single-agent centralized information filtering. Large-scale experimental studies involving the weIl-known TREC data set are also presented to illustrate the advantages of distributed filtering as weIl as to compare the different distributed approaches.
- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.8, S.834-842
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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
-
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
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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
-
Quiroga, L.M.; Mostafa, J.: ¬An experiment in building profiles in information filtering : the role of context of user relevance feedback (2002)
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- Abstract
- An experiment was conducted to see how relevance feedback could be used to build and adjust profiles to improve the performance of filtering systems. Data was collected during the system interaction of 18 graduate students with SIFTER (Smart Information Filtering Technology for Electronic Resources), a filtering system that ranks incoming information based on users' profiles. The data set came from a collection of 6000 records concerning consumer health. In the first phase of the study, three different modes of profile acquisition were compared. The explicit mode allowed users to directly specify the profile; the implicit mode utilized relevance feedback to create and refine the profile; and the combined mode allowed users to initialize the profile and to continuously refine it using relevance feedback. Filtering performance, measured in terms of Normalized Precision, showed that the three approaches were significantly different ( [small alpha, Greek] =0.05 and p =0.012). The explicit mode of profile acquisition consistently produced superior results. Exclusive reliance on relevance feedback in the implicit mode resulted in inferior performance. The low performance obtained by the implicit acquisition mode motivated the second phase of the study, which aimed to clarify the role of context in relevance feedback judgments. An inductive content analysis of thinking aloud protocols showed dimensions that were highly situational, establishing the importance context plays in feedback relevance assessments. Results suggest the need for better representation of documents, profiles, and relevance feedback mechanisms that incorporate dimensions identified in this research.
-
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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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.6, S.641-662
-
Mostafa, J.: Bessere Suchmaschinen für das Web (2006)
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- Date
- 22. 1.2006 18:34:49
