Publications ⋆ Kawa Nazemi

Publications


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2019
2.
 Nazemi, Kawa
Visual Trend Analytics in Digital Libraries Miscellaneous 
Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval., 2019.
Abstract | Links | BibTeX | Tags: Information visualization, Trend analysis, Trend Analytics, Visual analytics
@misc{Naz19ASIST,

title = {Visual Trend Analytics in Digital Libraries},

author = {Kawa Nazemi},

url = {https://zenodo.org/record/3264801#.XSBcMo_gpaR, Zenodo Open Access},

doi = {10.5281/zenodo.3264801},

year  = {2019},

date = {2019-04-26},

abstract = {The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies’ reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3].},

howpublished = {Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval.},

keywords = {Information visualization, Trend analysis, Trend Analytics, Visual analytics},

pubstate = {published},

tppubtype = {misc}

}

Close
The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies’ reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3].
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Zenodo Open Access
doi:10.5281/zenodo.3264801
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2015
1.
 Nazemi, Kawa;  Retz, Reimond;  Burkhardt, Dirk;  Kuijper, Arjan;  Kohlhammer, Jörn;  Fellner, Dieter W
Visual Trend Analysis with Digital Libraries Proceedings Article 
In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business., pp. 14:1–14:8, ACM, Graz, Austria, 2015, ISBN: 978-1-4503-3721-2.
Abstract | Links | BibTeX | Tags: Data Analytics, datamining, Information extraction, Information visualization, Trend analysis, Visual analytics
@inproceedings{Nazemi2015b,

title = {Visual Trend Analysis with Digital Libraries},

author = {Kawa Nazemi and Reimond Retz and Dirk Burkhardt and Arjan Kuijper and Jörn Kohlhammer and Dieter W Fellner},

url = {https://doi.acm.org/10.1145/2809563.2809569},

doi = {10.1145/2809563.2809569},

isbn = {978-1-4503-3721-2},

year  = {2015},

date = {2015-10-01},

booktitle = {Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business.},

pages = {14:1--14:8},

publisher = {ACM},

address = {Graz, Austria},

series = {i-KNOW '15},

abstract = {The early awareness of new technologies and upcoming trends is essential for making strategic decisions in enterprises and research. Trends may signal that technologies or related topics might be of great interest in the future or obsolete for future directions. The identification of such trends premises analytical skills that can be supported through trend mining and visual analytics. Thus the earliest trends or signals commonly appear in science, the investigation of digital libraries in this context is inevitable. However, digital libraries do not provide sufficient information for analyzing trends. It is necessary to integrate data, extract information from the integrated data and provide effective interactive visual analysis tools. We introduce in this paper a model that investigates all stages from data integration to interactive visualization for identifying trends and analyzing the market situation through our visual trend analysis environment. Our approach improves the visual analysis of trends by investigating the entire transformation steps from raw and structured data to visual representations.},

keywords = {Data Analytics, datamining, Information extraction, Information visualization, Trend analysis, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
The early awareness of new technologies and upcoming trends is essential for making strategic decisions in enterprises and research. Trends may signal that technologies or related topics might be of great interest in the future or obsolete for future directions. The identification of such trends premises analytical skills that can be supported through trend mining and visual analytics. Thus the earliest trends or signals commonly appear in science, the investigation of digital libraries in this context is inevitable. However, digital libraries do not provide sufficient information for analyzing trends. It is necessary to integrate data, extract information from the integrated data and provide effective interactive visual analysis tools. We introduce in this paper a model that investigates all stages from data integration to interactive visualization for identifying trends and analyzing the market situation through our visual trend analysis environment. Our approach improves the visual analysis of trends by investigating the entire transformation steps from raw and structured data to visual representations.
Close
https://doi.acm.org/10.1145/2809563.2809569
doi:10.1145/2809563.2809569
Close