Technical analysis is a widely used method for forecasting the price direction on the financial time series data. This method requires the use of different number and types of analysis algorithms (technical indicators...
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ISBN:
(纸本)9781728104720
Technical analysis is a widely used method for forecasting the price direction on the financial time series data. This method requires the use of different number and types of analysis algorithms (technical indicators) together. Although these algorithms show successful performance on small-scale financial time series data, significant performance decreases are detected when the size of data increased. On the large-scale financial time series data, it is necessary to implement these algorithms based on the map-reduce programming model and examine the performance of the algorithms which are implemented based on this model comparatively. For this purpose, seven different indicators are studied within the scope of this study, new versions of these indicators are implemented using map-reduce parallel data processing model and performance comparisons are made with these algorithms. As a result of these comparisons on single-node and multi-node, significant performance gains have been obtained using map-reduce programming model.
The financial data analysis, which is the road map of the future and at the same time the mirror of today, is of vital importance for many institutions. Therefore, it is common to apply statistical analysis on financi...
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ISBN:
(纸本)9781728104720
The financial data analysis, which is the road map of the future and at the same time the mirror of today, is of vital importance for many institutions. Therefore, it is common to apply statistical analysis on financial data. In such cases, data size becomes very important when performing financial data analysis. While analyzing the financial data, as the size and variety of data and increase, one can achieve the most accurate financial data analysis outcome. However, the increase in data size also brings some disadvantages such as performance-loss due to processing large-scale data. These disadvantages occur in both query performance and various functions that are used in data analysis. In this respect, it is necessary to examine the data storage platforms comparatively, which will investigate the performance of query and statistical functions, used in financial data analysis, at the highest level for large-scale financial data sets. For this purpose, the first step of this study was to compare the performance of the query on the Relational and Non-SQL-based storage environments, and to compare the performance of the query in the single-node and double-node in-memory NoSQL data storage environment. To facilitate testing of these platforms;as the SQL database system, MSSQL was selected and as the distributed in-memory NoSQL database system, Hazelcast was selected. For different data sizes on these platforms, the run times of the query and statistical functions were measured. In order to examine the ability of the in-memory NoSQL data storage platforms, to manage and manipulate the data, map-reduce programming model was used. Performance tests on single nodes and multiple nodes show that in-memory NoSQL platforms are very successful compared to relational database systems. In addition, it has been found that in-memory NoSQL storage platforms provide higher performance gains when using the map-reduce programming model.
Access control is a fundamental component of any data management system, ensuring the prevention of unauthorized data access. Within the realm of data streams, it plays a crucial role in query processing by facilitati...
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Access control is a fundamental component of any data management system, ensuring the prevention of unauthorized data access. Within the realm of data streams, it plays a crucial role in query processing by facilitating authorized access to them. This paper introduces the StreamFilter framework, which focuses on securely processing queries with range filters over streaming data. Leveraging the Role-Based Access Control model, the StreamFilter framework enables the specification of fine-grained access policies at various levels of granularity, such as tuples and attributes, through the utilization of a bit string structure. To enhance the search operation during data stream query processing, the framework employs a distributed indexing method, constructing a set of smaller B + Tree indices rather than a single large B + Tree index. Furthermore, it seamlessly integrates access authorization evaluation with query processing, efficiently filtering unauthorized parts from the query results. The experimental results demonstrate an approximately 50% increase in efficiency for processing queries with range filters compared to the post-filtering strategy. This improvement is observed across all types of data distribution, including uniform, skew, and hyper skew.
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