Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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/).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: 4Citation - Scopus: 5Dma: Matrix Based Dynamic Itemset Mining Algorithm(IGI Global Publishing, 2013) Oğuz, Damla; Yıldız, Baroş; Ergenç, BelginUpdates on an operational database bring forth the challenge of keeping the frequent itemsets up-to-date without re-running the itemset mining algorithms. Studies on dynamic itemset mining, which is the solution to such an update problem, have to address some challenges as handling i) updates without re-running the base algorithm, ii) changes in the support threshold, iii) new items and iv) additions/deletions in updates. The study in this paper is the extension of the Incremental Matrix Apriori Algorithm which proposes solutions to the first three challenges besides inheriting the advantages of the base algorithm which works without candidate generation. In the authors' current work, the authors have improved a former algorithm as to handle updates that are composed of additions and deletions. The authors have also carried out a detailed performance evaluation study on a real and two benchmark datasets.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.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.