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Static Testing the Logical Design
Published in William E. Lewis, David Dobbs, Gunasekaran Veerapillai, Software Testing and Continuous Quality Improvement, 2017
William E. Lewis, David Dobbs, Gunasekaran Veerapillai
Each entity is a table divided horizontally into rows and columns. Each row is a specific occurrence of each entity, much like records in a file. Each column is an attribute that helps describe the entity. Examples of attributes include size, date, value, and address. Each entity in a data model does not exist by itself; it is linked to other entities by relationships. A relationship is an association between two or more entities of interest to the user, about which the application is to maintain and report data. There are three types of relationships: a one-to-one relationship links a single occurrence of an entity to zero or one occurrence of another entity; a one-to-many relationship links one occurrence of an entity to zero or more occurrences of an entity; and a many-to-many relationship links many occurrences of an entity to many occurrences of an entity. The type of relationship defines the cardinality of the entity relationships. See Appendix G10, “Database Testing,” for more details about data modeling.
Database administration
Published in Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Texts in Statistical Science, 2017
Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
PRIMARY KEY: a column or set of columns in a table that uniquely identifies each row. By convention, this column is often called id. A table can have at most one primary key, and in general it is considered good practice to define a primary key on every table (although there are exceptions to this rule). If the index spans k < p columns, then even though the primary key must by definition have n rows itself, it only requires nk pieces of data, rather than the np that the full table occupies. Thus, the primary key is always smaller than the table itself, and is thus faster to search. A second critically important role of the primary key is enforcement of non-duplication. If you try to insert a row into a table that would result in a duplicate entry for the primary key, you will get an error.
General introduction
Published in Adedeji B. Badiru, Handbook of Industrial and Systems Engineering, 2013
The idea that database systems should present the user with a view of data organized as tables called relations was originally proposed by Codd (1979). Each relation is made up of attributes. Attributes are values describing properties of an entity, a concrete object in its reality. Furthermore, the connections among two or more sets of entities are called relationships. The idea of a key on a table is central to the relational model. The purpose of a key is to identify each row uniquely. A primary key is the attribute (or combination of attributes) that uniquely identifies one row or record. On the other hand, a foreign key is the attribute (or combination of attributes) that appears as a primary key in another table. Foreign key relationships provide the basis for establishing relationships across tables in a relational database.
Dynamic inventory replenishment strategy for aerospace manufacturing supply chain: combining reinforcement learning and multi-agent simulation
Published in International Journal of Production Research, 2022
Hao Wang, Jiaqi Tao, Tao Peng, Alexandra Brintrup, Edward Elson Kosasih, Yuqian Lu, Renzhong Tang, Luoke Hu
In order to improve the SCP under the dynamic replenishment strategy, we employ RL to find the optimal performance level as mentioned in Section 4. In each episode, the OEM can choose any combination in Table 2 at decision time t and get a reward rt calculated by Equation (4). Then it will repeat this at the next decision time t + I (I = 7) and get a reward rt+I until this episode stops at time T (T = 30). In our case, there are v = 5 decision stages and p = 5 actions at each state, so the Q-table has 65 = 7776 rows and 5 columns. After each episode, the Q-table will be updated by Equation (20) and the optimal strategy π* that maximises the total reward can be found by inquiring the Q-table. For example, in one episode, the sequence of action at five decision stages is: [1→1→3→1→2], and the state transition is: [0,0,0,0,0] → [1,0,0,0,0] → [1,1,0,0,0] → [1,1,3,0,0] → [1,1,3,1,0] → [1,1,3,1,2], and reward at each decision point is: SCPt = 7 = 35883.491, SCPt = 14 = 68213.100, SCPt = 21 = 308892.879, SCPt = 28 = 499832.967, SCPt = 30 = 513593.037. Then the Q-table will update the value of (row: 1, column: 1), (row: 6480, column: 1), (row: 7128, column: 3), (row: 7308, column: 1) and (row: 7320, column: 2).
Comparison of Performance of Data Imputation Methods for Numeric Dataset
Published in Applied Artificial Intelligence, 2019
Anil Jadhav, Dhanya Pramod, Krishnan Ramanathan
Lower is value of Mean NRMSE; better is estimate of the missing values. The Mean NRMSE for each dataset for different percentage of imputed data using different imputation methods is calculated and given in the Tables 2–6. Each column in the table indicates percentage of imputed data and each row indicates method used for imputation of data. The value in bold indicates lowest Mean NRMSE. It means that bold value indicates the imputation method that gives better imputation result when applied on the given dataset. The plot of Imputation Method and corresponding Mean NRMSE for different percentages of the missing values for all datasets used in the study are shown in Figures 1–5. It is observed that as percentage of missing values increases Mean NRMSE also increases. It is also observed that Mean NRMSE for kNN Impute method is lowest across all datasets and all missing data percentages.
A scalable cloud-based cyberinfrastructure platform for bridge monitoring
Published in Structure and Infrastructure Engineering, 2019
Seongwoon Jeong, Rui Hou, Jerome P. Lynch, Hoon Sohn, Kincho H. Law
The Cassandra database is built upon a column family data model consisting of ‘keyspace,’ ‘column family’, ‘row’ and ‘column’, which are analogous to ‘database’, ‘table’, ‘tuple’ and ‘attribute’ of relational database, respectively. One important feature of the Casandra database schema is that it follows closely the BrIM schema for bridge monitoring applications. Figure 7 presents data mapping between the BrIM schema of the ‘FELine’ object and the corresponding column family schema ‘FELine’. The database schema contains the data entities of ‘FELine’, as well as ‘child’ and ‘parent’ entities to record the hierarchical relation between the objects. As such, bridge information stored in the column-oriented database can be mapped to hierarchical BrIM objects.