Hybrid cloud computing monitoring software architecture

Aktaş, Mehmet S.

doi:10.1002/cpe.4694

Cited by 36 publications

(16 citation statements)

References 20 publications

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“…PostgreSQL 9.6 were used for the experiments. The cloud environment is capable of providing the necessary expansion that allows parallel processing procedures to be used where more computing power is required due to the larger dataset 26 …”

Section: Experimental Methodologymentioning

confidence: 99%

Optimizing bitmap index encoding for high performance queries

Yildiz

2020

Concurrency and Computation

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Summary Many sources such as historical archives, sensor readings, health systems, and machine records produce ever‐increasing but often unchanging data. These accumulating data create a need for faster processing. Bitmap index, which can take advantage of multi‐core and multiprocessor systems, is designed to process data that increase over time but do not change frequently. It has a well‐known advantage, especially in queries on data with low cardinality. However, bitmap index can handle high cardinality data efficiently because it can use its own compression algorithm. Bitmap index has many encoding schemes that affect query processing time. In this study, we developed an algorithm that improves query performance by using optimal encoding among bitmap encodings. With this optimization algorithm, we witnessed up to 40% performance increase in queries made with bitmap indexes created with different encodings. Furthermore, in comparison with a commonly used relational database, we found significant improvements in the number of query operations per second performed on optimized encoded bitmap indexes generated by the introduced algorithm.

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Section: Experimental Methodologymentioning

confidence: 99%

Optimizing bitmap index encoding for high performance queries

Yildiz

2020

Concurrency and Computation

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“…There exists a number of studies focusing on how the management of metadata of big data sets are provided in a distributed computing setting [8][9][10][11]. Moreover, there exists a number of studies that are conducted in the field of information systems for managing distributed data storage platforms [12][13][14][15][16]. Unlike these studies, this chapter focuses on extracting meaningful information, i.e.…”

Section: Related Workmentioning

confidence: 99%

Association Rule Mining on Big Data Sets

Celik¹,

Hasanbasoglu²,

Aktaş³

et al. 2021

Data Mining - Methods, Applications and Systems

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An accurate, complete, and rapid establishment of customer needs and existence of product recommendations are crucial points in terms of increasing customer satisfaction level in various different sectors such as the banking sector. Due to the significant increase in the number of transactions and customers, analyzing costs regarding time and consumption of memory becomes higher. In order to increase the performance of the product recommendation, we discuss an approach, a sample data creation process, to association rule mining. Thus instead of processing whole population, processing on a sample that represents the population is used to decrease time of analysis and consumption of memory. In this regard, sample composing methods, sample size determination techniques, the tests which measure the similarity between sample and population, and association rules (ARs) derived from the sample were examined. The mutual buying behavior of the customers was found using a well-known association rule mining algorithm. Techniques were compared according to the criteria of complete rule derivation and time consumption.

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“…We observe the use of complex event processing techniques in different domains. This technique is used to detect patterns in different domains such as social media [19][20][21][22], internet of things [23][24], cloud computing [25], ecommerce [26] and real time streaming based applications [27]. Our study focuses on detecting predefined patterns within the data collected from the clickstream data generated by user-system interactions.…”

Section: Literature Reviewmentioning

confidence: 99%

Learning from Student Browsing Data on E-Learning Platforms: Case Study

Cansiz¹,

Sudan²,

Ogretici³

et al. 2020

Position Papers of the 2020 Federated Conference on Computer Science and Information Systems

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Interpretation of the behaviors of students in elearning platforms with machine learning models has become an emerging need in recent years. Increase in the number of registered students on e-learning platforms is one of the reasons for choosing machine learning models. Tracking, modeling and understanding student activities gets more complex when the number of students is increased. This study is focusing modeling student activities on e-learning platforms with Complex Event Processing (CEP), Association Rule Mining (ARM) and Clustering methods based on distributed software architecture. Within the scope of this study, different modules that work real-time have been developed. An admin panel has been also developed in order to control all modules and track the student actions. Performance results of modules also obtained and evaluated on distributed system architecture.

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Hybrid cloud computing monitoring software architecture

Cited by 36 publications

References 20 publications

Optimizing bitmap index encoding for high performance queries

Optimizing bitmap index encoding for high performance queries

Association Rule Mining on Big Data Sets

Learning from Student Browsing Data on E-Learning Platforms: Case Study

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