Detecting misinformation in social networks using provenance data

Baeth, Mohamed Jehad; Aktaş, Mehmet S.

doi:10.1002/cpe.4793

Cited by 44 publications

(22 citation statements)

References 42 publications

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“…They concluded that the bitmap indexing technique was the appropriate choice for the data warehouse query operation 20 . There are also studies on provenance data suitable for the encoded bitmap index due to its often unchanging feature 21 . Abdulhadi et al measured the performance of bitmap indexes by comparing it with the B‐tree indexes using the Oracle environment that utilizes B‐tree as the default indexing.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

Optimizing bitmap index encoding for high performance queries

Yildiz

2020

Concurrency and Computation

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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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“…CDR data has been impactful in the analysis of varying models and its application is growing exceedingly. Among these usages, urban sensing and planning [8], [9], traffic engineering [10], [11], predicting energy consumption [12], improved churn prediction using both CDR data and community detection [13] [14]- [22]. Provenance graph is mainly used to understand the data lineage.…”

Section: Literature Reviewmentioning

confidence: 99%

On the Community Discovery Methods for Complex Networks: A Case Study

Afrassa

Cosgun

Gursoy

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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The inherent knowledge discovery problem regarding networks that represent complex real world phenomenon is a popular research topic. Specifically, in social network analysis (SNA), several community discovery techniques with various approaches have been put forward to distinguish closely related entities. Identifying the relevant techniques to utilize based on the context of the application is a key difficulty researchers face.In this study we propose a methodology for classifying these techniques, visualize a prototype, and analyze the performance and quality of selected approaches over a real world call detail record (CDR) data set.

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Detecting misinformation in social networks using provenance data

Cited by 44 publications

References 42 publications

Optimizing bitmap index encoding for high performance queries

Optimizing bitmap index encoding for high performance queries

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

On the Community Discovery Methods for Complex Networks: A Case Study

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