Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Author: search.filters.author.Ayav, TolgaInstitution Author: search.filters.institutionAuthor.Leloğlu, Engin

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  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 12
    A Review of Cloud Deployment Models for E-Learning Systems
    (Institute of Electrical and Electronics Engineers Inc., 2013) Leloğlu, Engin; Ayav, Tolga; Aslan, Burak Galip
    With the significant growth in the cloud-based systems, many industries give their attention to cloud computing solutions. E-learning is a promising application area since its typical requirements such as dynamically allocation of computation and storage resources, coincide well with cloud characteristics. This paper presents some possible cloud solutions in e-learning environments by emphasizing its pros and cons. It is of paramount importance to choose the most suitable cloud model for an e-learning application or an educational organization in terms of scalability, portability and security. We distinguish various deployment alternatives of cloud computing and discuss their benefits against typical e-learning requirements.
  • Conference Object
    Coefficient-Based Exact Approach for Frequent Itemset Hiding
    (IARIA, 2014) Leloğlu, Engin; Ayav, Tolga; Ergenç, Belgin
    Concealing sensitive relationships before sharing a database is of utmost importance in many circumstances. This implies to hide the frequent itemsets corresponding to sensitive association rules by removing some items of the database. Research efforts generally aim at finding out more effectivemethods in terms of convenience, execution time and side-effect. This paper presents a practical approach for hiding sensitive patterns while allowing as much nonsensitive patterns as possible in the sanitized database. We model the itemset hiding problem as integer programming whereas the objective coefficients allow finding out a solution with minimum loss of nonsensitive itemsets. We evaluate our method using three real datasets and compared the results with a previous work. The results show that information loss is dramatically minimized without sacrificing the accuracy.