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Hua Zhu Song - One of the best experts on this subject based on the ideXlab platform.

  • loading rdf owl file into oracle nosql Database using a bulk loading and parallelization techniques
    Applied Mechanics and Materials, 2014
    Co-Authors: Khamis Abdul Latif Khamis, Luo Zhong, Hua Zhu Song
    Abstract:

    Processing and loading large RDF/OWL Data files often involves the transfer of large amounts of Data from source operational systems to the Target Data Store. It requires a proper loading techniques and more expressive methods to ensure the RDF/OWL Data files are correctly loaded into the Database, or the loading process will end up finishing all memory space which resulted in performance deterioration in the entire application. The Bulk loading is one of the predominant scientific techniques and with fastest loading method to load large amounts of Data into the Database; however, this study has found out that the bulk loading alone, cannot handle triples files containing object values with more than 4000 bytes. To solve these tidy problems, we propose to implement bulk loading techniques concurrently with parallelization methods to load RDF/OWL Data file into the Database. To accomplish this goal, the Oracle NOSQL Database is chosen as the backend persistent storage for the RDF/OWL Data files. In addition, loading model and techniques are provided to utilize the loading process, and finally algorithms, methods and parallelization techniques are given with the aim of increasing loading performance of RDF/OWL within the Oracle NOSQL Database.

  • Loading RDF/OWL File into Oracle NoSQL Database Using a Bulk Loading and Parallelization Techniques
    Applied Mechanics and Materials, 2014
    Co-Authors: Khamis Abdul Latif Khamis, Luo Zhong, Hua Zhu Song
    Abstract:

    Processing and loading large RDF/OWL Data files often involves the transfer of large amounts of Data from source operational systems to the Target Data Store. It requires a proper loading techniques and more expressive methods to ensure the RDF/OWL Data files are correctly loaded into the Database, or the loading process will end up finishing all memory space which resulted in performance deterioration in the entire application. The Bulk loading is one of the predominant scientific techniques and with fastest loading method to load large amounts of Data into the Database; however, this study has found out that the bulk loading alone, cannot handle triples files containing object values with more than 4000 bytes. To solve these tidy problems, we propose to implement bulk loading techniques concurrently with parallelization methods to load RDF/OWL Data file into the Database. To accomplish this goal, the Oracle NOSQL Database is chosen as the backend persistent storage for the RDF/OWL Data files. In addition, loading model and techniques are provided to utilize the loading process, and finally algorithms, methods and parallelization techniques are given with the aim of increasing loading performance of RDF/OWL within the Oracle NOSQL Database.

Khamis Abdul Latif Khamis - One of the best experts on this subject based on the ideXlab platform.

  • loading rdf owl file into oracle nosql Database using a bulk loading and parallelization techniques
    Applied Mechanics and Materials, 2014
    Co-Authors: Khamis Abdul Latif Khamis, Luo Zhong, Hua Zhu Song
    Abstract:

    Processing and loading large RDF/OWL Data files often involves the transfer of large amounts of Data from source operational systems to the Target Data Store. It requires a proper loading techniques and more expressive methods to ensure the RDF/OWL Data files are correctly loaded into the Database, or the loading process will end up finishing all memory space which resulted in performance deterioration in the entire application. The Bulk loading is one of the predominant scientific techniques and with fastest loading method to load large amounts of Data into the Database; however, this study has found out that the bulk loading alone, cannot handle triples files containing object values with more than 4000 bytes. To solve these tidy problems, we propose to implement bulk loading techniques concurrently with parallelization methods to load RDF/OWL Data file into the Database. To accomplish this goal, the Oracle NOSQL Database is chosen as the backend persistent storage for the RDF/OWL Data files. In addition, loading model and techniques are provided to utilize the loading process, and finally algorithms, methods and parallelization techniques are given with the aim of increasing loading performance of RDF/OWL within the Oracle NOSQL Database.

  • Loading RDF/OWL File into Oracle NoSQL Database Using a Bulk Loading and Parallelization Techniques
    Applied Mechanics and Materials, 2014
    Co-Authors: Khamis Abdul Latif Khamis, Luo Zhong, Hua Zhu Song
    Abstract:

    Processing and loading large RDF/OWL Data files often involves the transfer of large amounts of Data from source operational systems to the Target Data Store. It requires a proper loading techniques and more expressive methods to ensure the RDF/OWL Data files are correctly loaded into the Database, or the loading process will end up finishing all memory space which resulted in performance deterioration in the entire application. The Bulk loading is one of the predominant scientific techniques and with fastest loading method to load large amounts of Data into the Database; however, this study has found out that the bulk loading alone, cannot handle triples files containing object values with more than 4000 bytes. To solve these tidy problems, we propose to implement bulk loading techniques concurrently with parallelization methods to load RDF/OWL Data file into the Database. To accomplish this goal, the Oracle NOSQL Database is chosen as the backend persistent storage for the RDF/OWL Data files. In addition, loading model and techniques are provided to utilize the loading process, and finally algorithms, methods and parallelization techniques are given with the aim of increasing loading performance of RDF/OWL within the Oracle NOSQL Database.

Luo Zhong - One of the best experts on this subject based on the ideXlab platform.

  • loading rdf owl file into oracle nosql Database using a bulk loading and parallelization techniques
    Applied Mechanics and Materials, 2014
    Co-Authors: Khamis Abdul Latif Khamis, Luo Zhong, Hua Zhu Song
    Abstract:

    Processing and loading large RDF/OWL Data files often involves the transfer of large amounts of Data from source operational systems to the Target Data Store. It requires a proper loading techniques and more expressive methods to ensure the RDF/OWL Data files are correctly loaded into the Database, or the loading process will end up finishing all memory space which resulted in performance deterioration in the entire application. The Bulk loading is one of the predominant scientific techniques and with fastest loading method to load large amounts of Data into the Database; however, this study has found out that the bulk loading alone, cannot handle triples files containing object values with more than 4000 bytes. To solve these tidy problems, we propose to implement bulk loading techniques concurrently with parallelization methods to load RDF/OWL Data file into the Database. To accomplish this goal, the Oracle NOSQL Database is chosen as the backend persistent storage for the RDF/OWL Data files. In addition, loading model and techniques are provided to utilize the loading process, and finally algorithms, methods and parallelization techniques are given with the aim of increasing loading performance of RDF/OWL within the Oracle NOSQL Database.

  • Loading RDF/OWL File into Oracle NoSQL Database Using a Bulk Loading and Parallelization Techniques
    Applied Mechanics and Materials, 2014
    Co-Authors: Khamis Abdul Latif Khamis, Luo Zhong, Hua Zhu Song
    Abstract:

    Processing and loading large RDF/OWL Data files often involves the transfer of large amounts of Data from source operational systems to the Target Data Store. It requires a proper loading techniques and more expressive methods to ensure the RDF/OWL Data files are correctly loaded into the Database, or the loading process will end up finishing all memory space which resulted in performance deterioration in the entire application. The Bulk loading is one of the predominant scientific techniques and with fastest loading method to load large amounts of Data into the Database; however, this study has found out that the bulk loading alone, cannot handle triples files containing object values with more than 4000 bytes. To solve these tidy problems, we propose to implement bulk loading techniques concurrently with parallelization methods to load RDF/OWL Data file into the Database. To accomplish this goal, the Oracle NOSQL Database is chosen as the backend persistent storage for the RDF/OWL Data files. In addition, loading model and techniques are provided to utilize the loading process, and finally algorithms, methods and parallelization techniques are given with the aim of increasing loading performance of RDF/OWL within the Oracle NOSQL Database.

Charles C. Tappert - One of the best experts on this subject based on the ideXlab platform.

  • Data Virtualization for Decision Making in Big Data
    International Journal of Software Engineering & Applications, 2019
    Co-Authors: Manoj Muniswamaiah, Tilak Agerwala, Charles C. Tappert
    Abstract:

    Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze Data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between Data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past Data. Business Intelligence solutions are among the most valued Data management tools whose main objective is to enable interactive access to real-time Data, manipulation of Data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw Data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting Data from different sources, transforming and aggregating and finally loading large volume of Data into warehouses. Recently Data virtualization has been used to speed up the Data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass Data transformation and cleansing operations, and bulk loading the Data into a Target Data Store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.

  • Data Virtualization for Analytics and Business Intelligence in Big Data
    9th International Conference on Computer Science Engineering and Applications (CCSEA 2019), 2019
    Co-Authors: Manoj Muniswamaiah, Tilak Agerwala, Charles C. Tappert
    Abstract:

    Data analytics and Business Intelligence (BI) is essential for strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between Data offering insights. The major difference between BI and analytics is that analytics has predictive competence whereas Business Intelligence helps in informed decision-making built on the analysis of past Data. Business Intelligence solutions are among the most valued Data management tools available. Business Intelligence solutions gather and examine current, actionable Data with the determination of providing insights into refining business operations. Data needs to be integrated from disparate sources in order to derive insights. Traditionally organizations employ Data warehouses and ETL process to obtain integrated Data. Recently Data virtualization has been used to speed up the Data integration process. Data virtualization and ETL are often complementary technologies performing complex, multi-pass Data transformation and cleansing operations, and bulk loading the Data into a Target Data Store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.

Manoj Muniswamaiah - One of the best experts on this subject based on the ideXlab platform.

  • Data Virtualization for Decision Making in Big Data
    International Journal of Software Engineering & Applications, 2019
    Co-Authors: Manoj Muniswamaiah, Tilak Agerwala, Charles C. Tappert
    Abstract:

    Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze Data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between Data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past Data. Business Intelligence solutions are among the most valued Data management tools whose main objective is to enable interactive access to real-time Data, manipulation of Data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw Data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting Data from different sources, transforming and aggregating and finally loading large volume of Data into warehouses. Recently Data virtualization has been used to speed up the Data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass Data transformation and cleansing operations, and bulk loading the Data into a Target Data Store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.

  • Data Virtualization for Analytics and Business Intelligence in Big Data
    9th International Conference on Computer Science Engineering and Applications (CCSEA 2019), 2019
    Co-Authors: Manoj Muniswamaiah, Tilak Agerwala, Charles C. Tappert
    Abstract:

    Data analytics and Business Intelligence (BI) is essential for strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between Data offering insights. The major difference between BI and analytics is that analytics has predictive competence whereas Business Intelligence helps in informed decision-making built on the analysis of past Data. Business Intelligence solutions are among the most valued Data management tools available. Business Intelligence solutions gather and examine current, actionable Data with the determination of providing insights into refining business operations. Data needs to be integrated from disparate sources in order to derive insights. Traditionally organizations employ Data warehouses and ETL process to obtain integrated Data. Recently Data virtualization has been used to speed up the Data integration process. Data virtualization and ETL are often complementary technologies performing complex, multi-pass Data transformation and cleansing operations, and bulk loading the Data into a Target Data Store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.