What is a Time Series Graph?
The world is composed of trillions of people, places, and things, each with interconnecting timelines. The digital world is even larger.
This illustration shows a person, a place, and a thing, each with intersecting timelines. Alice's timeline consists of waking up, ordering coffee, picking up coffee, and ordering another coffee. Alice and the coffee shop interacted when Alice placed the order and then the coffee shop delivered the cup of coffee to her. The coffee shop interacted with the cup of coffee's timeline when it created and delivered the cup of coffee. Eventually, Alice disposed of the cup of coffee, ending the cup of coffee's timeline.
A data record is information about one of these people, places, and things. Each record has a time frame, whether implicitly or explicitly specified. Explicitly specifying a time frame makes the data multi-dimensional. Today's algorithms for organizing data simply do not perform well with multi-dimensional data.
This is where Craxel's patented technology comes in. It can determine exactly where a multi-dimensional data value should be stored or found in what's called constant time, or O(1). This allows extraordinarily fast query times over multi-dimensional data. A natural way then to organize data for many problems is as a time series or multi-dimensional graph. This allows capturing the changes (time series) as well as the relationships (graph) in the data over time.
Cybersecurity Time Series Graph Example
This illustration is a simple example of a time series graph for cybersecurity. There are four hosts, each with a timeline of events.
Conversations between these computers are captured via flow records. When these conversations happen, the timelines intersect and a relationship between the computers exists in the graph. DNS lookups also form connections between computers in the graph.
The ability to capture all of the different types of events for a computer and all of the relationships with other computers is incredibly powerful. All of the information is available in a single place, without asking multiple data repositories for different types of information about each computer.
Craxel's breakthrough technology allows time series graphs for cybersecurity to be created as the data comes in and at massive scale.
Geospatial Time Series Graph Example
Imagine if time series graphs supported all data types including geospatial. This means you could build a timeline and relationships between places, or between places and things, or between places and people.
This illustration shows the relationships between a sensor, an area on the earth, a ground system, and a resulting product from a collection. With geospatial types in a time series graph, places could be specific point locations or arbitrary areas. The timeline of everything that is known about a huge number of locations and all of their relationships to other entities and things can be captured in a time series graph.
Time Series Graph at Any Scale
Building a time series graph with just a few timelines and relationships could be done with just about any database.
Building very large time series graphs for trillions of people, places, and things at line speed as the data arrives is beyond the capabilities of traditional indexing algorithms. Black Forest can:
- Build time series graphs for millions or billions of people, places, and things
- Build massive time series graphs at the speed the data arrives
- Support very large time series graphs with multiple years of data
Time series graphs at these scales and with these capabilities are completely infeasible without Craxel's incredible advancement in multi-dimensional indexing.
