Oğuz, Damla
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Oguz, D.
Oğuz, D
Oguz, D
Oguz,D.
Oguz, Damla
Oğuz, D.
Oğuz, D
Oguz, D
Oguz,D.
Oguz, Damla
Oğuz, D.
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damlaoguz@iyte.edu.tr
Main Affiliation
03.04. Department of Computer Engineering
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Current Staff
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Documents
11
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134
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5
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Scholarly Output
12
Articles
7
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6703/3721
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
23
Scopus Citation Count
61
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0
WoS Citations per Publication
1.92
Scopus Citations per Publication
5.08
Open Access Source
8
Supervised Theses
1
| Journal | Count |
|---|---|
| International Journal of Data Warehousing and Mining | 2 |
| 20th East European Conference on Advances in Databases and Information Systems, ADBIS 2016 | 1 |
| -- 9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 2025-09-06 through 2025-09-07 -- Malatya -- 215321 | 1 |
| ACM International Conference Proceeding Series -- 7th International Conference on Advances in Artificial Intelligence, ICAAI 2023 -- 13 October 2023 through 15 October 2023 -- Istanbul -- 196685 | 1 |
| Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi | 1 |
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12 results
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Master Thesis Dynamic Frequent Itemset Mining Based on Matrix Appriori Algorithm(Izmir Institute of Technology, 2012) Oğuz, Damla; Oğuz, Damla; Ergenç Bostanoğlu, Belgin; Ergenç, BelginThe frequent itemset mining algorithms discover the frequent itemsets from a database. When the database is updated, the frequent itemsets should be updated as well. However, running the frequent itemset mining algorithms with every update is inefficent. This is called the dynamic update problem of frequent itemsets and the solution is to devise an algorithm that can dynamically mine the frequent itemsets. In this study, a dynamic frequent itemset mining algorithm, which is called Dynamic Matrix Apriori, is proposed and explained. In addition, the proposed algorithm is compared using two datasets with the base algorithm Matrix Apriori which should be re-run when the database is updated.Conference Object Citation - Scopus: 12Incremental Itemset Mining Based on Matrix Apriori Algorithm(Springer Verlag, 2012) Oğuz, Damla; Ergenç, BelginDatabases are updated continuously with increments and re-running the frequent itemset mining algorithms with every update is inefficient. Studies addressing incremental update problem generally propose incremental itemset mining methods based on Apriori and FP-Growth algorithms. Besides inheriting the disadvantages of base algorithms, incremental itemset mining has challenges such as handling i) increments without re-running the algorithm, ii) support changes, iii) new items and iv) addition/deletions in increments. In this paper, we focus on the solution of incremental update problem by proposing the Incremental Matrix Apriori Algorithm. It scans only new transactions, allows the change of minimum support and handles new items in the increments. The base algorithm Matrix Apriori works without candidate generation, scans database only twice and brings additional advantages. Performance studies show that Incremental Matrix Apriori provides speed-up between 41% and 92% while increment size is varied between 5% and 100%.Article Citation - WoS: 1Citation - Scopus: 1Extended Adaptive Join Operator With Bind-Bloom Join for Federated Sparql Queries(IGI Global Publishing, 2017) Oğuz, Damla; Yin, Shaoyi; Ergenç, Belgin; Hameurlain, Abdelkader; Dikenelli, OğuzThe goal of query optimization in query federation over linked data is to minimize the response time and the completion time. Communication time has the highest impact on them both. Static query optimization can end up with inefficient execution plans due to unpredictable data arrival rates and missing statistics. This study is an extension of adaptive join operator which always begins with symmetric hash join to minimize the response time, and can change the join method to bind join to minimize the completion time. The authors extend adaptive join operator with bind-bloom join to further reduce the communication time and, consequently, to minimize the completion time. They compare the new operator with symmetric hash join, bind join, bind-bloom join, and adaptive join operator with respect to the response time and the completion time. Performance evaluation shows that the extended operator provides optimal response time and further reduces the completion time. Moreover, it has the adaptation ability to different data arrival rates.Conference Object Citation - Scopus: 11An Analysis of Large Language Models and Langchain in Mathematics Education(Association for Computing Machinery, 2023) Soygazi,F.; Oğuz, DamlaThe development of large language models (LLMs) has led to the consideration of new approaches, particularly in education. Word problems, especially in subjects like mathematics, and the need to solve these problems by collectively addressing specific stages of reasoning, have raised the question of whether LLMs can be successful in this area as well. In our study, we conducted analyses by asking mathematics questions especially related to word problems using ChatGPT, which is based on the latest language models like Generative Pretrained Transformer (GPT). Additionally, we compared the correct and incorrect answers by posing the same questions to LLMMathChain, a mathematics-specific LLM based on the latest language models like LangChain. It was observed that the answers obtained were more successful with ChatGPT (GPT 3.5), particularly in the field of mathematics. However, both language models were found to be below expectations, particularly in word problems, and suggestions for improvement were provided. © 2023 ACM.Article Performance Analysis of K-Degree Anonymization on Barabási-Albert Graph(2023) Soygazi, Fatih; Oğuz, DamlaAnonymity is one the most important problems that emerged with the increasing number of graph-based social networks. It is not straightforward to ensure anonymity by adding or removing some nodes from the graph. Therefore, a more sophisticated approach is required. The consideration of the degree of the nodes in a graph may facilitate having knowledge about specific nodes. To handle this problem, one of the prominent solutions is k-degree anonymization where some nodes involving particular degree values are anonymized by masking its information from the attackers. Our objective is to evaluate the achievement of k-degree anonymization with a well-known graph structure, namely, Barabási-Albert graph, which is similar to the graphs on social networks. Hence, we generate multiple synthetic Barabási-Albert graphs and evaluate the k-degree anonymization performance on these graphs. According to experimental results, the success of k-degree anonymity approximately proportional to the number of edges or nodes.Article Citation - WoS: 1Citation - Scopus: 3Ignoring Internal Utilities in High-Utility Itemset Mining(MDPI, 2022) Oğuz, DamlaHigh-utility itemset mining discovers a set of items that are sold together and have utility values higher than a given minimum utility threshold. The utilities of these itemsets are calculated by considering their internal and external utility values, which correspond, respectively, to the quantity sold of each item in each transaction and profit units. Therefore, internal and external utilities have symmetric effects on deciding whether an itemset is high-utility. The symmetric contributions of both utilities cause two major related challenges. First, itemsets with low external utility values can easily exceed the minimum utility threshold if they are sold extensively. In this case, such itemsets can be found more efficiently using frequent itemset mining. Second, a large number of high-utility itemsets are generated, which can result in interesting or important high-utility itemsets that are overlooked. This study presents an asymmetric approach in which the internal utility values are ignored when finding high-utility itemsets with high external utility values. The experimental results of two real datasets reveal that the external utility values have fundamental effects on the high-utility itemsets. The results of this study also show that this effect tends to increase for high values of the minimum utility threshold. Moreover, the proposed approach reduces the execution time.Article Citation - WoS: 17Citation - Scopus: 26Federated Query Processing on Linked Data: a Qualitative Survey and Open Challenges(Cambridge University Press, 2015) Oğuz, Damla; Ergenç, Belgin; Yin, Shaoyi; Dikenelli, Oğuz; Hameurlain, AbdelkaderA large number of data providers publish and connect their structured data on the Web as linked data. Thus, the Web of data becomes a global data space. In this paper, we initially give an overview of query processing approaches used in this interlinked and distributed environment, and then focus on federated query processing on linked data. We provide a detailed and clear insight on data source selection, join methods and query optimization methods of existing query federation engines. Furthermore, we present a qualitative comparison of these engines and give a complementary comparison of the measured metrics of each engine with the idea of pointing out the major strengths of each one. Finally, we discuss the major challenges of federated query processing on linked data. © 2015 Cambridge University Press.Article Citation - Scopus: 1An Interestingness Measure for Knowledge Bases(Elsevier, 2023) Oğuz, Damla; Soygazi, FatihAssociation rule mining and logical rule mining both aim to discover interesting relationships in data or knowledge. In association rule mining, relationships are identified based on the occurrence of items in a dataset, while in logical rule mining, relationships are determined based on logical relationships between atoms in a knowledge base. Association rule mining has been widely studied in transactional databases, mainly for market basket analysis. Confidence has become the most widely used interesting measure to assess the strength of a rule. Many other interestingness measures have been proposed since confidence can be insufficient to filter negatively associated relationships. Recently, logical rule mining has become an important area of research, as new facts can be inferred by applying discovered logical rules. They can be used for reasoning, identifying potential errors in knowledge bases, and to better understand data. However, there are currently only a few measures for logical rule mining. Furthermore, current measures do not consider relations that can have several objects, called quasi-functions, which can dramatically alter the interestingness of the rule. In this paper, we focus on effectively assessing the strength of logical rules. We propose a new interestingness measure that takes into account two categories of relations, functions and quasi-functions, to assess the degree of certainty of logical rules. We compare our proposed measure with a widely used measure on both synthetic test data and real knowledge bases. We show that it is more effective in indicating rule quality, making it an appropriate interestingness measure for logical rule evaluation. & COPY; 2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Conference Object Stability of Low-Cost High-Utility Patterns Under Uncertain Cost Assignments(Institute of Electrical and Electronics Engineers Inc., 2025) Oguz, D.Low-Cost High-Utility Itemset Mining (LCHUIM) is a recent extension of utility-based pattern mining that aims to identify itemsets with high utility and low associated cost. In many real-world applications, especially in domains like education or healthcare, explicit cost information may be unavailable or difficult to measure. This study investigates the stability of LCHUIM under uncertain cost settings by applying it to a real-world educational dataset where cost values are not explicitly provided. We generate three different cost assignment strategies using interpretable mappings and evaluate the impact of cost differences under various utility and support thresholds. Experimental results show that under stricter thresholds, LCHUIM yields highly stable results. As thresholds are relaxed, more patterns emerge, and the sensitivity to cost differences increases. Nevertheless, a considerable number of patterns remain consistent, indicating that LCHUIM is capable of producing reliable insights even when cost values are estimated. This work contributes to understanding the robustness of utility-based pattern mining in behavior-driven domains with incomplete or estimated cost values. © 2025 IEEE.Article Medical Text Classification Using Semisupervised Learning and Bert-Based Models(2025) Soygazı, Fatıh; Oguz, DamlaTıbbi metin sınıflandırması, yetersiz eğitim verisi gibi zorluklarla karşılaşarak karmaşık tıbbi metinleri düzenlemektedir. Bu çalışma, sağlık sorunları özetleri ve etiketleri içeren bir veri setine dayanarak tıbbi metinleri sınıflandırmak için yeni bir yöntem önermektedir. Etiketli veri setimize veri temsil teknikleri uyguladık ve metin sınıflandırması için çeşitli makine öğrenmesi algoritmaları kullandık. İlk sonuçlar, sınırlı etiketli veriler nedeniyle yeterli bulunmamıştır. Bunu geliştirmek için, etiketli verileri zenginleştirmek amacıyla etiketlenmemiş bir veri seti kullanarak veri artırma teknikleri uyguladık; bu süreçte BERT tabanlı modeller (BioBERT, ClinicalBERT) kullanılmıştır. Yeni etiketli kayıtları doğrulamak ve veri setine eklemek için çoğunluk oylama ve ağırlıklı çoğunluk oylama gibi farklı oylama mekanizmaları kullanılmıştır. Etiketli verileri artırdıktan sonra, makine öğrenmesi algoritmalarını yeniden uygulanmıştır. Sonuçlar, yaklaşımımızın tıbbi metin sınıflandırmasının performansını önemli ölçüde artırdığını, sınırlı etiketli verilerin getirdiği zorlukları etkili bir şekilde ele aldığını ve genel doğruluğu artırdığını göstermiştir.