Some use cases for Cozo

As Cozo is a general-purpose database, it can be used in situations where traditional databases such as PostgreSQL and SQLite are used. However, Cozo is designed to overcome several shortcomings of traditional databases, and hence fares especially well in specific situations:

Interconnected relations

You have a lot of interconnected relations and the usual queries need to relate many relations together. In other words, you need to query a complex graph.

An example is a system granting permissions to users for specific tasks. In this case, users may have roles, belong to an organization hierarchy, and tasks similarly have organizations and special provisions associated with them. The granting process itself may also be a complicated rule encoded as data within the database.

With a traditional database, the corresponding SQL tend to become an entangled web of nested queries, with many tables joined together, and maybe even with some recursive CTE thrown in. This is hard to maintain, and worse, the performance is unpredictable since query optimizers in general fail when you have over twenty tables joined together.

With Cozo, on the other hand, Horn clauses make it easy to break the logic into smaller pieces and write clear, easily testable queries. Furthermore, the deterministic evaluation order makes identifying and solving performance problems easier.

Just a graph

Your data may be simple, even a single table, but it is inherently a graph.

We have seen an example in the Tutorial: the air route dataset, where the key relation contains the routes connecting airports.

In traditional databases, when you are given a new relation, you try to understand it by running aggregations on it to collect statistics: what is the distribution of values, how are the columns correlated, etc.

In Cozo you can do the same exploratory analysis, except now you also have graph algorithms that you can easily apply to understand things such as: what is the most connected entity, how are the nodes connected, and what are the communities structure within the nodes.

Hidden structures

Your data contains hidden structures that only become apparent when you identify the scales of the relevant structures.

Examples are most real networks, such as social networks, which have a very rich hierarchy of structures.

In a traditional database, you are limited to doing nested aggregations and filtering, i.e. a form of multifaceted data analysis. For example, you can analyze by gender, geography, job or combinations of them. For structures hidden in other ways, or if such categorizing tags are not already present in your data, you are out of luck.

With Cozo, you can now deal with emergent and fuzzy structures by using e.g. community detection algorithms, and collapse the original graph into a coarse-grained graph consisting of super-nodes and super-edges. The process can be iterated to gain insights into even higher-order emergent structures. This is possible in a social network with only edges and no categorizing tags associated with nodes at all, and the discovered structures almost always have meanings correlated to real-world events and organizations, for example, forms of collusion and crime rings. Also, from a performance perspective, coarse-graining is a required step in analyzing the so-called big data, since many graph algorithms have high complexity and are only applicable to the coarse-grained small or medium networks.

Knowledge augmentation

You want to understand your live business data better by augmenting it into a knowledge graph.

For example, your sales database contains product, buyer, inventory, and invoice tables. The augmentation is external data about the entities in your data in the form of taxonomies and ontologies in layers.

This is inherently a graph-theoretic undertaking and traditional databases are not suitable. Usually, a dedicated graph processing engine is used, separate from the main database.

With Cozo, it is possible to keep your live data and knowledge graph analysis together, and importing new external data and doing analysis is just a few lines of code away. This ease of use means that you will do the analysis much more often, with a perhaps much wider scope.