Final answer:
To get a multi-series table, the underlying search needs to support complex queries, join or correlate data across multiple datasets, and handle aggregation functions. Structured and clean data is also essential to facilitate this process. Examples include using JOIN in SQL databases or specific query structures in NoSQL systems like MongoDB or Elasticsearch.
Step-by-step explanation:
To create a multi-series table as part of a search query, you need to make sure that your underlying search is capable of parsing multiple datasets and correlating them accordingly. Generally, this involves using a database or search engine that supports complex queries, such as SQL for relational databases or specific query languages for NoSQL databases.
For instance, in SQL, you can utilize JOIN clauses to combine rows from two or more tables based on a related column between them. This allows you to create a multi-series table that displays combined information from these tables. Additionally, the search would need to handle aggregation functions, like COUNT, AVG, SUM, etc., which can give you statistical insights across different series.
Meanwhile, NoSQL databases might require different query structures like aggregations in MongoDB or multi-get searches in Elasticsearch. The essential requirement is the ability to handle complex data structures and to perform inter-table or inter-document relations.
Finally, it's not just about the search capability but also ensuring that your data is structured and standardized in a way that supports multi-series analysis. This means that data must be clean, correctly formatted, and consistent in its