One of the most common problem for anime watchers is filler episode. The anime series of One Piece is produced by Toei Animation and has reached more than 800 episodes. With pirate world setting, there are many One Piece characters, including pirates crew, marine, world government and other characters. Luffy, a young pirate captain and his crew in Strawhat Pirates that explore Grand Line sea to find world’s ultimate treasure known as ‘One Piece’ in order to become the next Pirate King.įirst One Piece manga published in 1997 and since then there are hundreds One Piece chapters and volumes. Its version of anime also popular around the world and often regarded as the best Japanese anime series. PostgreSQL: The World’s Most Advanced Open Source Relational Database.Fillers in One Piece – One Piece is a popular manga adapted into an anime. Swinbank, R.: Design patterns for metadata-first ETL process control. Shankaranarayanan, G., Even, A.: The metadata enigma. Zyl, J.V., Vincent, M., Mohania, M.: Representation of metadata in a data warehouses. Vetterli, T., Vaduva, A., Staudt, M.: Metadata standards for data warehousing: open information model vs. Rahman, N., Marz, J., Akhter, S.: An ETL metadata model for data warehousing. In: Proceedings of the 6th ACM International Workshop on Data Warehousing and OLAP, New Orleans, Louisiana, USA, pp. 2172–2179 (2015)įan, H., Poulovassilis, A.: Using AutoMed metadata in data warehousing environments. Faculty of engineering and information sciences, January 2015, pp. Narendra, N., Ponnalagu, K., Tamilselvam, S., Ghose, A.: Goal-driven context-aware data filtering in IoT-based systems. Jain, T., Rajasree, S., Saluja, S.: Refreshing datawarehouse in near real-time. Nath, R.P.D., Hose, K., Pedersen, T.B., Romero, O.: SETL: a programmable semantic extract-transform-load framework for semantic data warehouses. Wiley, New York (2010)īansal, S.K., Kagemann, S.: Integrating big data: a semantic extract-transform-load framework. Kimball, R., Ross, M.: The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence. ![]() ![]() In: 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), Langkawi, 2017, pp. ![]() Sabtu, A., et al.: The challenges of extract, transform and loading (ETL) system implementation for near real-time environment. In: 2015 International Seminar on Intelligent Technology and its Applications (ISITIA), pp. Wibowo, A.: Problems and available solutions on the stage of extract transform and loading in near real-time data warehousing (a literature study). Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. A test execution of an experimental Airflow Directed Acyclic Graph (DAG) with randomly selected data is performed to evaluate the proposed framework. We present a metadata framework implementation which is based on open-source Big Data technologies, describing its architecture and interconnections with external systems, data model, functions, quality metrics, and templates. In this work, we focus on ETL metadata and its use in driving process execution and present a proprietary approach to the design of the metadata-based process control that can reduce complexity, enhance resilience of ETL processes and allow their adaptive self-reorganization. In order to mitigate this impact and provide resilience of the ETL process, a special Metadata Framework is needed that can manage the design of new data pipelines and processes. In essence, ETL- processes are becoming bottlenecks in such environments due to complexity growth, number of steps in data transformations, number of machines used for data processing and finally, increasing impact of human factors on development of new ETL-processes. Extract-transform-load (ETL) processes play a crucial role in data analysis in real-time data warehouse environments which demand low latency and high availability features for functionality.
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