You call yourself a Database Expert? Know your History, Dude
A Guest Post by SOSA Startup Member, Kueri.Me
Occasionally, in a fast-paced world in which technology advances seemingly in the blink of an eye, it makes sense to step back and look at where a thing has been to figure out where it might be going. Take, for example, the evolution of the database and the search query.
Developers profit from the evolution of database management and search query language.
As long as there have been humans, there has been data. From ancient hieroglyphs to your mother’s recipe file, data storage has played a significant role in preserving and retaining important information throughout history.
The Rise of the Computer
Computer science began in earnest in the 1940s and COBOL was the first non-proprietary programming language. Once computer science truly got underway, it became apparent that data would need to be stored somewhere. Hence, the history of the database began.
The 1960s: In the early 1960s, computer use became more practical for private organizations and enterprises. In 1964, the first commercial database management system was designed. By the late 1960s, IBM and North American Aviation (later known as Rockwell International) developed a hierarchical database model known as IMS. By 1971, a network model known as CODASYL became popular. Both model types were accessible from COBOL using an interface.
These advances were notable because they enabled developers to create an application and maintain a database. However, the process was somewhat cumbersome and time-consuming for all involved.
The 1970s: Things changed considerably in the 1970s. Intuit’s “A Timeline of Database History” explains: “E.F. Codd published an important paper to propose the use of a relational database model, and his ideas changed the way people thought about databases. In his model, the database’s schema, or logical organization, is disconnected from physical information storage, and this became the standard principle for database systems.”
Once relational database systems became the norm, two database systems became prominent. The Ingres database system used a query language known as QUEL, which later led to query languages like Ingres Corp., MS SQL Server, Sybase, Wang’s PACE, and Britton-Lee. The System R database system used SEQUEL, which later led to the development of query languages like SQL/DS, DB2, Allbase, Oracle, and Non-Stop SQL.
The 1980s and 1990s: The evolution of search query languages continued unabated through the next two decades. In the 1980s, SQL became the standard query language. Personal computers became standard in most households, and developers scrambled to meet rising consumer demand for an easier user interface and a way to make search queries more user-friendly.
A New Millennium and a New Quest
Attempts at interfacing successfully between humans and their data hasn’t always yielded the most fluent of user experiences. Creating reports and dashboards, or even searching for a set of records is time consuming and can be difficult to formulate when it comes to non-tech-savvy people.
Most database interfaces include sliders and checkboxes where users need to be on boarded (pre-taught how to use) in order to extract answers efficiently. More often than not the sidebar approach tends to be cumbersome, a fact to which most Google Analytics or Salesforce users can attest.
Though developers have worked on the challenge of making search queries more accessible to the average person for many years, even in the new millennium progress has been somewhat slow. Microsoft’s “Natural Language Processing” explains the problem in this way: “It’s ironic that natural language, the symbol system that is easiest for humans to learn and use, is hardest for a computer to master. Long after machines have proven capable of inverting large matrices with speed and grace, they still fail to master the basics of our spoken and written languages.”
Human language is, by its nature, ambiguous. Computers, on the other hand, do not like ambiguity. The trick, then, was to develop a system whereby natural human language can be “translated” into a search query language like SQL. Can this really be done?
Enter Kueri, a more recent installment in the database interface history book, a system that acts as a bridge between SQL queries and people who speak natural language. Recently, Geektime explained Kueri’s unique system this way: “Kueri’s system enables developers to implant a unique search box within apps to take questions from end users in natural language and translate them into SQL queries in real time. The app can run the queries through the database and display the results to the user. In addition, in order to make it even easier for the end user, it facilitates automatic completion during typing, with completions of words and smart suggestions according to the context of the search and database.”
Kueri.me, the developers of Kueri, claim their solution has been proven as a real bridge of the Man-Database gap using their live online demo.
While solutions for mitigating the man-data-interface gap are developing and have reached maturity over the past 5 years, it seems that the accelerated abundance of data (BigData) is compounding the challenge in a pace almost hard to catch up with. Recent developments aim to cater the reduction in interface complexity by implementing a Zero UI approach comprised of voice recognition, touch screens, and the integration of artificial intelligence (AI) database interfaces.
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