Sometimes being tired of general search systems, our customers want to make something different or more specific. In this case, it would be a great idea to build a custom self-hosted search platform. It is not difficult to create a custom search engine with the existing open source technologies.
Sure, this process is not easy and is quite tricky in some moments. You also need to be prepared for a long-term run, because it takes not a single month to crawl all the data, as well as process and analyze it.
From our expertise, a complete beginner can develop a simple search engine for semi-structured data in several weeks or so. But each time the search engine development is a slightly different process, because of constant technology growth.
Hopefully, there are several common steps we usually face while developing a search engine, and these steps we uncover in this article. Our team hopes that this article helps you to understand the key phases and saves you several days on doing initial research.
Initial data analysis
Before the development starts, we need to analyze the initial data to understand what search algorithms suit your data best.
The data can be structured, unstructured, and semi-structured:
- For example, structured data is any data that contains a fixed field, specific file, or record. Matrices, structured tables, and a relational (SQL) databases can also be considered as structured data. During initial data analysis data scientist examines, cleans, and transforms data to find attributes.
- If we operate with structured data, we can categorize data in different groups using data attributes – unique properties that differentiate one record from another.
- If the data is unstructured – like photos, videos, images, documents – the easiest way to search thru this data is to convert it to a structured or semi-structured format using the various techniques. According to the data type, data scientist elaborates the way to handle this data to prevent false-positive results.
This important step allows us to move forward an essential result – from our expertise, it takes about 40% of all time.
User request parsing
The next step in search engine development is user request analysis.
During this step, data scientist analyzes:
- The way user forms incoming request
- How to extract parameters from it
- How these parameters are interconnected.
For complex data, it is not a good option to enter a simple query into the search input — you need to develop a specific query language that will help a customer to look up data by the combination of attributes quickly and efficiently.
If you are looking for an alternative for developing a particular query language, we suggest you try machine learning to extract data from search queries. Machine learning can be used to create a semantic search engine powered by the enhanced text analysis module.
The main feature of the semantic search engine — it helps you to process natural language, automatically extracting object attributes from search queries. It also finds relationships between different entry characteristics that are later used for efficient data retrieval.
Search algorithm development
Search algorithms are various: different algorithms are used to find different types of data. Applying the wrong algorithm to the specific data may lead to significant performance loss, and common data lookups may take much more time than expected.
Another fact that should be taken into consideration – the existing implementations of specific search algorithms. The most popular programming languages to build search engines are Python, Java, PHP, Ruby, and C#. You can easily find various implementations on GitHub.
But let’s look at a more particular example – Boyer–Moore string-search algorithm – it can be coded using various programming languages. But it is essential that the algorithm developed with C++ performs better than the same algorithm coded with PHP.
While developing a search platform, you need to understand the weak points of the programming language and algorithm you are planning to use. It is not probably a problem for a beginner, but it is a massive difficulty while developing a solution for a huge enterprise.
Let’s look at another example, textual search. Textual search is often based on so-called string matching – the technique of finding strings that match a specific pattern.
There are several types of string matching: the most common are strict and fuzzy (approximate string matching). Strict matching is a type of matching when data fully matches a pattern, while fuzzy matching — when only the part of a pattern matches the part of data.
If we dig deeper, we will find that the same rules work both for strings and complex objects. It’s excellent when the system detects an object that matches user query, but most often it can’t. In this situation, the engine scores the existing records and ranks them.
Machine learning can significantly improve this process – it can analyze not only user input but also score data that has similar attributes to the requested object. You can also use machine learning directly. It will provide a search system with an ability to learn the most relevant searches and improve continuously without being manually programmed.
Attribute scoring and tuning
The fourth step of the search engine development is the SERP setup. SERP stands for search engine results page – it is a page generated by a search engine, where all relevant results are displayed.
When a search engine finds several relevant results, it should put them in the right order to satisfy the user. The results are placed in the correct order because of attribute scoring. Every object found by a search engine has a set of attributes or parameters that describe the specific entry.
Each attribute has a numerical value called “weight“, and these values are summarized by a search engine to determine the right order of results. During this step, we usually analyze search engine behavior and tune attribute weights to achieve the result that satisfies the customer.
Machine learning can significantly improve attribute scoring. With advanced machine learning, we can analyze the search requests chain – the way how the user looks up for specific entry.
Taking into consideration search history, we can calculate the exact weights dynamically adjusting or decreasing values according to the results user already seen. With machine learning, it is easy to analyze the most searched entries and push them to the top automatically and without distorting a user or software engineer.
The last step of search engine development is SERP generation. We already mentioned that SERP is a search engine results page – a particular page, where a user can see relevant results to the search query. When a regular person thinks about how search engine results should look like he or she usually imagines Google or Yahoo.
Well, we must admit – Google SERP looks good and displays information in a simple manner. But while we are talking about more specific search engines, the user interface may not be simple at all.
The example of a search engine results pages from one of our latest projects
As every search engine provides data lookups thru various types of data, it is a typical situation when a result pages look different. Usually, it is a good practice to display a list of attributes extracted from the search query, but sometimes it may be challenging – as there can be hundreds of different interconnected attributes.
Industrial-grade search engines usually have a dynamic user interface built with popular front-end frameworks like React or Vue. These frameworks make it possible to explore the rich SERPs without page reloading, which decreases the load to the web server.
So, if you are thinking of building a search engine for complex data, you should consider how to visualize the results easily and what technologies to use.
The bottom line
We live in a fascinating world of data, so it’s impossible to imagine our life without modern search engines like Google or Yahoo. But there are also types of data general search engines cannot handle, and for this data, you will probably need something different.
If you want to build a search engine for complex structured or unstructured data, the points listed in this article are helpful to you – now you know where to start with and what issues you may face.
At Azati we’ve already built a dozen of different search engines for several customers in various industries, so we have an exciting experience to share. So, if you are developing your engine now, or only think about it – drop us a line, and we’ll have a chat about it.
If you started thinking about building a search engine – drop us a line and we will have a chat about that. We’ve already built several search engines and we can easily share our knowledge with you.