Much of KnowledgeKube is driven by data. Often, this data is stored as individual, discrete values using virtual containers such as variables. However, to perform particularly advanced calculations, more complex data may be required.
KnowledgeKube provides a number of options for storing complex data, which allows you to design applications that make best use of the data and resources at your disposal. This section will introduce one of these options, namely how to use a Data Source to store and retrieve data.
| Topic | Description | Further Reading |
|---|---|---|
| Geolocation | Web browsers and mobile devices are capable of obtaining data about the active user's current location. KnowledgeKube can tap into that data, making it available for use within web and mobile applications. | Geolocation |
| Simple Lists | A simple method of storing multiple independent values together - for example, an e-mail recipient list. Lists have a dedicated set of functions that allow you to modify them, but due to their simple nature they cannot be used with the majority of data source functionality, and do not feature as part of the Data Sources interface introduced in Data Connections. | Simple Lists |
| Data Connections | A connection between a KnowledgeKube model and a table database. | Data Connections |
| Model Data Sources | A repository of data accessed by KnowledgeKube applications. | Model Data Sources |
| Data Source Fields | Limit which values are retrieved by a data source and connect keywords to specific data source fields by assigning aliases. | Data Source Fields and Aliases |
| Data Source Filters | Control which values are returned when a data source is queried. | Data Source Filters |
| In-Memory Data Sources | Create local in-memory data sources that only persist for the duration of the current session. These can be used in much the same way as any other data source, except they are created and managed entirely with expressions. | In-Memory Data Sources |
| Using Expressions to Query a Data Source | Use a special interface to generate Expressions that interact with a selected data source, or choose to write them manually. | Using Expressions to Query a Data Source |
| Implementing a Data-driven Response List | Special types of questions can be used to automatically populate a response list from data source fields. | Implementing a Data-driven Response List |
| Data Grids | Use a Data Source Grid to pull down information from a data source, and determine how to present data. | Data Grids |
| Connecting a Question to a Data Source | Map data source values to questions in a model. | Connecting a Question to a Data Source |
| Refreshing a Data Source | Specific methods of refreshing data in a data source can be used to replace the stored memory known as Cached Items with new data, whenever a data source is queried. | Refreshing a Data Source |
| Indexing a Data Source |
Specify fields in a data source using a tool that will index, test and analyse the data. |
Indexing a Data Source |
| Natural Language Processing | KnowledgeKube has the ability to translate human language as it is spoken into logical instructions, by breaking it down into machine-readable statements, within your model. | Natural Language Processing |
This section also includes the following appendices:
- Appendix: Data Source Transaction Scoping
- Appendix: Data Grid Column Types
- Appendix: Data Grid Expression Types
- Appendix: Filter Operator Types
- Appendix: Filtering Geolocation Data
- Appendix: Filter Condition Types
- Appendix: Data Source Cache
- Appendix: Data Source Index Mask
- Appendix: Full Text Search Results Mapped Attributes
- Appendix: NLP Expressions
- Appendix: Derived Column and Filter Expression Syntax