Why graph-databases are better for y(our) use case?

We all know that “Data” plays a crucial role when understanding the use case. According to [2], 70% of the time before training an AI model is spent on data preprocessing and it is necessary to store the data in a format that is efficient for each use case. In this article, we explore such database technologies for storing and processing data.?

Two widely used database storage techniques are relational and non-relational.

Relational databases like SQL needs planning of table structures, lack scalability, are inflexible, and require a plethora of operational and maintenance overhead.? In most of these issues, non-relational databases perform efficiently. However, we need a different type of non-relational database structure to represent connected data where graph databases are gaining popularity. In this article, we explore the advantages and provide a summary of graph databases at various levels.?

Why chose graph databases for traceability??

According to the most common definition [9], “traceability is the ability to follow relations between several artifacts of a project lifecycle”. These artifacts start from requirements and maps to documents, test cases, code, etc.

Since the artefacts in display relationships between them in a straightforward graph form, a graph database is a suitable choice for storing them. In other words, it works well for describing data with intricate dependencies. In order to discover connections between the graph's points, it may swiftly traverse these interconnections. They are therefore perfect for even intricate applications like fraud detection, network security and recommendation engines in addition to traceability.

Further, these standout features of using a graph database make it suitable for Traceability graphs

Handle highly interconnected data

A graph database would provide a platform for storing, managing, and querying densely linked data and would easily integrate new heterogeneous data. They derive powerful insights from this huge interconnected data. Traceability graphs are well connected and the logical flow of the software and systems development process is related to different types of traceability. Three primitive and fundamental traceability techniques include forward traceability, backward traceability, and bi-directional traceability which portray connections among the artifacts.?

Model flexibility

Being schema-less graph databases improve model flexibility. Traceability graphs evolve over time and hence the associated meta-models. Thus, the flexibility of the models aids data representation, storage, and retrieval that can be enhanced by storing them as graph databases.?

Deducing patterns

It makes sense to think of a graph database for complicated data with numerous connections whose pattern you wish to watch and understand. Traceability graphs in general have deducible patterns among the artefacts. These patterns are called chained, hourglass, one-one and fan-out patterns. Hence, representing them as graphs help us to observe these prominently and make informed decisions.

Improved querying capabilities

A graph database not only allows you to incorporate multiple types of items and different types of relationships in the graph but also maintains the relationships between objects in a natural fashion, making searches concerning relationships quick and simple. Also in traceability graphs, we need to retrieve data often, you should choose a graph database because they are designed for this task.?

Easy tracking

As mentioned before, the traceability graphs grow in size and complexity as the project progresses. Graph databases help us to deal with heterogeneous connections over a period of time by maintaining a level of dynamicity using techniques like time-stamping, and statistics monitoring. Thus, it provides capabilities that aid in automated maintenance and storage.

Summary:

In a nutshell, if you have highly complex interconnected data on which you run queries constantly and analyse it, a graph database is preferred over a relational database. For our use case in research projects: impakt, and cooperants we use a graph database for exploring traceable data from space-software and mechanical-software intensive systems engineering data for multiple use cases like link prediction, similarity analysis etc.