Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Department: search.filters.department.Thesis (Master)--İzmir Institute of Technology, Computer EngineeringSubject: search.filters.subject.Information technology

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  • Master Thesis
    Tag Based Storage and Retrieval System for Organization Related News
    (Izmir Institute of Technology, 2019) Parkın, Kübra; Tuğlular, Tuğkan
    For corporate organizations, it becomes more and more important to gather information about opponents or partners, or any kind of information that can be related to the organization. In a rapidly changing world, ensuring competitiveness for organizations and making consistent strategic decisions are becoming increasingly difficult. Gathering news about the business has an undeniable effect on the decisions of companies. It is essential to keep up with this race in order not to get out of the race. Therefore, what corporate companies need is to have a retrieval system that collects and evaluates information that is relevant to the organization. However, it can be difficult to make use of large amounts of information. What needed is to store that information based on a pattern and make it easy to analyses.
  • Master Thesis
    Finding Out Subject-Matter Experts and Research Trends Using Bibliographic Data
    (Izmir Institute of Technology, 2015) Karataş, Arzum; Tekir, Selma
    With the prevalent use of information technology, it is very easy to reach nearly any information. However, if it is desired to be specialized in an area, the first thing to do is to know who are the experts in that area. Since experts have valuable knowledge, it is important to find these experts. Also, it is vital to be aware of trends for researchers who want to be expert in a topic or who want to enter into a new area. This work includes an empirical study for finding experts and research trends in academic world. We created a citation network from KDD proceedings and an author-keyword bipartite graph from bibliographic data of the same set of proceedings. Then, we applied link analysis algorithms HITS and PageRank, respectively. The results show that it is possible to detect two expert types (one that works intensively on a single subject and another having high level knowledge of various subtopics of a subject-matter). Moreover, topical trends are identified as doing peak, periodic, and having the same shape rather than showing absolute increase, decrease or stationary pose.