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RFID Data Warehousing and Analysis
Published in Lu Yan, Yan Zhang, Laurence T. Yang, Huansheng Ning, The Internet of Things, 2008
The rollup operation performs aggregation on a data cube, either by climbing up a concept hierarchy for a dimension or by dimension reduction. For example, assume that the location has the concept hierarchy location → locale → location category, time has the hierarchy second → hour → day, and product has the hierarchy EPC → SKU. Now, assume that one is looking at the stay table at the level of 〈locale, hour〉 and at the info table at the level of EPC, i.e., for each EPC, one aggregates stay with the same time_in, and time_out at the hour level, and the same location at the locale level. One can rollup to a higher abstraction level, such as, 〈location, category, day〉 and SKU where each stay and info perform coarser aggregation.
Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy
Published in International Journal of Production Research, 2022
Kai Zhang, Ting Qu, Yongheng Zhang, Ray Y. Zhong, George Huang
When the production system is functioning normally, the various states remain stable according to the established plan until the system is disturbed, such as machine quality, order change. At this time, complete data related to the UFs are collected for the evaluation of the impact of the system. As shown in Figure 2(a), all UF-related data is stored in a raw data cube. The raw data cube integrates data in four dimensions: tuple, operation, time, and information. Tuple dimensions include orders, electronic product code (EPC), storage, machine, cloud-resource, and timestamp. In the operation dimension, location, operation sequences and scheduling rules and other data representing the execution process and operation are recorded. The Time dimension records the time information of system state changes or other key event information, such as the timestamp of the occurrence of UFs. In the Information dimension, the attributes of each tuple are converted into valid information and displayed at the top of the data cube.
Design pragmatic method to low-carbon economy visualisation in enterprise systems based on big data
Published in Enterprise Information Systems, 2021
Zhigui Guan, Yuanjun Zhao, Xingdong Wang
The star model is selected for data warehouse design. The star model is mainly composed of a fact table and multiple dimension tables, and each dimension table is only connected with a unique fact table (Xiong et al. 2020). The data organisation of the star model is intuitive, and it pre-processes the data of each dimension during the construction of the data mart, so it has a fast data access speed (Drzadzewski and Tompa 2016). The logical model of data warehouse is a multidimensional database. OLAP is also based on multidimensional analysis. In BI intelligent data analysis, data cubes are undertaken to achieve multidimensional analysis. The data cube is composed of two elements: dimension and measure. The dimension is the angle from which the user observes the data, and the measure is the actual data value. The data cube is pre-stored in the data warehouse. When the OLAP engine receives a data query request, it converts the MDX statement into an SQL statement, retrieves the target data set from the data warehouse, stores it in online analytical processing memory, and finally passes it to the view layer for reading by the foreground analysis tool.