Queries

Sample Ontology: Objects represent key concepts within a business' datawarehouse. Queries are used to extract data from the ontology.

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Object vs. Metric Queries

1. Object Queries
2. Metric Queries

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Object Queries

• Which store has the most revenue?
  (You want a table where each row represents a unique store, possibly including a revenue metric.)
• How many orders were shipped to Canada?
  (You want a table where each row represents a unique order. If you have location data, you might also see attributes like “country” attached to each row.)

• The query returns rows corresponding to a single object—for example, “Store” or “Order.”
• Attributes from related objects can be joined in automatically. For example, if every store has a regional manager, then the name of the regional manager can be included when the store object is queried.
• Aggregated metrics from “downstream” objects can be included. (If each store has multiple orders, total profit from the “Order” object can be aggregated by store.)

Example: Querying the Location object returns a table where each row corresponds to a location, and collumns refer to the attributes of that location.

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Metric Queries

• Direct focus on numerical measurements, such as revenue, profit, or quantity.
• Rows represent slices or groups of these metrics across defined dimensions (e.g., time, geography, product category).
• Ideal for pivot-table-style breakdowns (e.g., total sales per city per month).

Example: Querying the Total Profit metric returns a table where each row coresponds to a partocular aggregation of profit, and where columns describe the dimensions that the metic can be further aggregated on.

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Technical Deep Dive

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Structure of an Ontology Query

1. Core Fact Object
   • Definition: The primary object (or noun) around which the query is centered.
   • Purpose: Determines the main data source and how other entities join.
   • Example: “Sale” as the core fact object in a sales analysis.
2. Metrics
   • Definition: The quantifiable measurements (e.g., sum, count, average) you want to analyze.
   • Purpose: Provide numerical insights, often aggregated.
   • Example: “Total Sales” or “Average Order Value.”
3. Dimensions
   • Definition: Attributes or fields that categorize your metrics.
   • Purpose: Break down metrics into slices (e.g., time, region, product category).
   • Example: “Store Location,” “Order Date,” or “Product Type.”
4. Filters
   • Definition: Criteria that include or exclude data from the query.
   • Purpose: Narrow down your dataset to the most relevant subset.
   • Example: Sales in the last quarter or orders above a certain dollar amount.
5. Orders
   • Definition: The sorting or ordering applied to the results.
   • Purpose: Make it easier to see trends, top performers, or chronological sequences.
   • Example: Sorting by descending “Total Sales” to identify best-selling products first.

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Object Queries

Coming Soon: A thorough exploration of the search algorithm for object queries, including how TextQL:

• Identifies the core fact object
• Joins relevant attributes
• Handles downstream metrics
• Optimizes performance across large datasets

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Metric Queries

Coming Soon: A deep dive into the technical underpinnings of metric queries, including:

• Defining and retrieving metrics from multiple objects
• Dimension-based slicing
• Aggregation functions and performance considerations
• Intelligent grouping and pivoting

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Next Steps