Detecting Misinformation in Social Networks Using Provenance Data

Baeth, Mohamed Jehad; Aktaş, Mehmet S.

doi:10.1109/skg.2017.00022

Cited by 28 publications

(14 citation statements)

References 9 publications

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“…Hence, our workload‐aware and incremental approaches may also be applied in different multi‐dimensional domains, that is, temporal databases, spatio‐temporal databases, and even some use cases of data provenance management. For example, some studies 49,50 focus on managing provenance metadata, which is primarily time series‐based data, better to understand the history of the data and processes. Lastly, some application domains 51 using spatial big data frameworks are another candidate area that our approaches on bulk‐loading can be applied.…”

Section: Discussionmentioning

confidence: 99%

Novel approaches on bulk‐loading of large scale spatial datasets

Kalay

2021

Concurrency and Computation

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Index structures that are bulk-loaded adaptively based on the query workload have been the subject of many database research, primarily in conventional databases holding one-dimensional data. Similar problems are also valid for spatial databases, and novel approaches are needed in these systems. In this study, we first consider the adaptive indexing approaches in a broad sense and then specifically investigate some possible forms of adaptivity on bulk-loading of large spatial datasets while retaining some core mechanics of well-known spatial data structures. In our first design, we examined how the internal configuration of the R-tree spatial index could be better for various workload properties, such as the workload's average aspect ratio. Our experiments observed that Adaptive Sort-Tile-Recursive (STR)-tree performs better than the original STR-tree. Our second design, the main contribution of this paper, focused on incremental indexing, another form of adaptivity. We concentrate on the data-to-insight latency and loaded spatial data faster within this multi-stage process.We used the Partitioned B-tree loaded with Morton codes of spatial objects to achieve this improvement by compromising spatial proximity. Finally, the response time of the first query in our design has been better than using the ordinary STR-tree index by a factor of 34%.

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Section: Discussionmentioning

confidence: 99%

Novel approaches on bulk‐loading of large scale spatial datasets

Kalay

2021

Concurrency and Computation

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“…For this purpose, they extend the Open Provenance Model to satisfy the requirements for their case. There exist various other provenance-based systems utilizing the Karma tool [ 21 – 23 ]. In our study, the provenance representation and data model are fully compatible with the W3C consortium’s PROV specification, which defines a common provenance framework that is independent of a specific domain.…”

Section: Literature Reviewmentioning

confidence: 99%

On the Provenance Extraction Techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models

Tufek

Aktaş

2021

Euro-Par 2020: Parallel Processing Workshops

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Day by day, severe meteorological events increasingly highlight the importance of fast and accurate weather forecasting. There are various Numerical Weather Prediction (NWP) models worldwide that are run on either a local or a global scale to predict future weather. NWP models typically take hours to finish a complete run, however, depending on the input parameters and the size of the forecast domain. Provenance information is of central importance for detecting unexpected events that may develop during model execution, and also for taking necessary action as early as possible. Besides, the need to share scientific data and results between researchers or scientists also highlights the importance of data quality and reliability. In this study, we develop a framework for tracking The Weather Research and Forecasting (WRF) model and for generating, storing, and analyzing provenance data. We develop a machine-learning-based log parser to enable the proposed system to be dynamic and adaptive so that it can adapt to different data and rules. The proposed system enables easy management and understanding of numerical weather forecast workflows by providing provenance graphs. By analyzing these graphs, potential faulty situations that may occur during the execution of WRF can be traced to their root causes. Our proposed system has been evaluated and has been shown to perform well even in a high-frequency provenance information flow.

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“…In current studies, formulas (using variables such as maximal process length or Vapnik-Chervonenkis (VC) size of the data cluster) considering the data characteristics have appeared [4][5][6][7]. 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].…”

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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Detecting Misinformation in Social Networks Using Provenance Data

Cited by 28 publications

References 9 publications

Novel approaches on bulk‐loading of large scale spatial datasets

Novel approaches on bulk‐loading of large scale spatial datasets

On the Provenance Extraction Techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models

Association Rule Mining on Big Data Sets

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