As more and more people are creating their profiles on social networking sites, it is getting even harder for the organizations behind these social media platforms to uncover connections. Concepts like Big Data and Vector Search help provide these organizations with the functionalities to do the same hassle-free. In this article, we are going to look at what big data is, and how it increases the ease of searching for connections in these complex social networks via the use of the concept of vector search.
What is Big Data?
Before we take a deeper dive into understanding the concept of vector search, let’s understand what Big Data is since we apply vector search to Big Data.
Big Data is exactly what we are talking about in this article. The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. The increasing number of users on social media platforms is increasing, and that is exactly what is forming the Big Data for these organizations.
Now for searching for connections in these large and complex datasets, we can’t use the traditional and simple search algorithms, as that would simply not deliver what we want them to deliver. And that is exactly where Vector Search comes into the picture.
What is Vector Search?
Vector search is a way to find related objects that have similar characteristics using machine learning models that detect semantic relationships between objects in an index. That sounds just as complex as the data sets we are talking about. Simply put, vector search is a searching algorithm that works efficiently for searching between complex and large datasets like the ones we are interested in. These vector search algorithms are getting more and more prominent among these big organizations since they make the computations and logical work behind the screen so much faster and more efficient.
How does vector search work?
Now that we have an idea of what big data and vector search is, let us see how it exactly works.
Vector search engines — known as vector database, semantic, or cosine search — find the nearest neighbors to a given (vectorized) query.
There are basically three methods to the vector search algorithm, let us discuss each of them one by one.
Wouldn’t it be simple to store data in simply one form? Thinking about it, a database having data points in one fixed form will make it so much easier and more efficient to carry out operations and computations on the database. In vector search, vector embedding is how one can do so. Vector embeddings are the numeric representation of data and related context, stored in high dimensional (dense) vectors.
Another method under vector search that simplifies comparing two datasets is the similarity score. The idea of similarity score is that if two data points are similar their vector representation will be similar as well. By indexing both queries and documents with vector embeddings, you find similar documents as the nearest neighbors of your query.
The ANN algorithm is yet another method to account for the similarity between two datasets. The reason why the ANN algorithm is efficient is because it sacrifices perfect accuracy in exchange for executing efficiently in high dimensional embedding spaces, at scale. This proves to be effective relative to the traditional nearest neighbor algorithms like the k-nearest neighbor algorithm (kNN) which leads to excessive execution times and zaps computational resources.
There’s so much that can be done with the help of vector search algorithms. Vector search can specifically prove to be of extreme use for organizations whose customer is increasing at an exponential rate. For such organizations, there is no better way to digitize their customer base with the help of vector databases.
Welcome to our blog! My name is Yuvraj Kore, and I am a blogger who has been exploring the world of blogging since 2017. It all started back in 2014 when I attended a digital marketing program at college and learned about the intriguing world of blogging.