Yazar "Can, Ali Burak" seçeneğine göre listele

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    Big data testing framework for recommendation systems in e-science and e-commerce domains
    (IEEE, 2021) Uzun-Per, Meryem; Can, Ali Burak; Gürel, Ahmet Volkan; Aktaş, Mehmet S.
    Software testing is an important process to evaluate whether the developed software applications meet the required specifications. There is an emerging need for testing frameworks for big data software projects to ensure the quality of the big data applications and satisfy the user requirements. In this study, we propose a software testing framework that can be utilized in big data projects both in e-science and e-commerce. In particular, we design the proposed framework to test big data-based recommendation applications. To show the usability of the proposed framework, we provide a reference prototype implementation and use the prototype to test a big data recommendation application. We apply the prototype implementation to test both functional and non-functional methods of the recommendation application. The results indicate that the proposed testing framework is usable and efficient for testing the recommendation systems that use big data processing techniques.
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    A novel sequential pattern mining algorithm for large scale data sequences
    (Springer, 2022) Can, Ali Burak; Uzun-Per, Meryem; Aktaş, Mehmet Sıddık
    Sequential pattern mining algorithms are unsupervised machine learning algorithms that allow finding sequential patterns on data sequences that have been put together based on a particular order. These algorithms are mostly optimized for finding sequential data sequences containing more than one element. Hence, we argue that there is a need for algorithms that are particularly optimized for data sequences that contain only one element. Within the scope of this research, we study the design and development of a novel algorithm that is optimized for data sets containing data sequences with single elements and that can detect sequential patterns with high performance. The time and memory requirements of the proposed algorithm are examined experimentally. The results show that the proposed algorithm has low running times, while it has the same accuracy results as the algorithms in the similar category in the literature. The obtained results are promising.
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    On the big data processing algorithms for finding frequent sequences
    (Wiley, 2023) Can, Ali Burak; Zaval, Mounes; Uzun-Per, Meryem; Aktaş, Mehmet Sıddık
    Sequential pattern mining algorithms extract trendy sequence appearances insideordered transactional datasets such as market basket datasets. There is a lack ofresearch employing big data processing techniques to locate frequent sequences onlarge-scale datasets. Furthermore, there is a need for optimized sequential patternmining algorithms that run on ordered one-dimensional sequences. We also observe alack of sequential pattern search studies in the literature, where the focus is centeredaround multi-dimensional data sequences. Existing approaches that deal with orderedone-dimensional datasets suffer from scalability issues as the amount of data to beanalyzed is enormous. This research investigates the big data processing techniquesused to find frequent sequences in large-scale datasets. It also proposes a scalablesequence pattern mining algorithm called Sequential Pattern Acquisition by ReducingSearch Space (SPARSS) designed for distributed data processing systems that effi-ciently handle large datasets containing sequential one-element data. It introducesa prototype implementation of SPARSS and provides information on the SPARSS’smemory and time requirements, which were calculated as part of experimental stud-ies on a real-world dataset. The results confirm our expectations and demonstrateSPARSS’s superior scalability and run-time efficiency compared to other distributedalgorithms.
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    Scalable recommendation systems based on finding similar items and sequences
    (Wiley, 2022) Uzun-Per, Meryem; Gürel, Ahmet Volkan; Can, Ali Burak; Aktaş, Mehmet S.
    The rapid growth in the airline industry, which started in 2009, continued until the COVID-19 era, with the annual number of passengers almost doubling in 10 years. This situation has led to increased competition between airline companies, whose profitability has decreased considerably. They aimed to increase their profitability by making services like seat selection, excess baggage, Wi-Fi access optional under the name of ancillary services. To the best of our knowledge, there is no recommendation system for recommending ancillary services for airline companies. Also, to the best of our knowledge, there is no testing framework to compare recommendation algorithms considering their scalabilities and running times. In this paper, we propose a framework based on Lambda architecture for recommendation systems that run on a big data processing platform. The proposed method utilizes association rule and sequential pattern mining algorithms that are designed for big data processing platforms. To facilitate testing of the proposed method, we implement a prototype application.We conduct an experimental study on the prototype to investigate the performance of the proposed methodology using accuracy, scalability, and latency related performance metrics. The results indicate that the proposed method proves to be useful and has negligible processing overheads.