Little do we know how popular cloud services such as Google, Yahoo, Bing, Twitter, Facebook, Pinterest, LinkedIn, Google+ and Web-based email have not only improved the way people interconnect, socialize and do business, they also help improve our search for information on the Web.
But they have, and significantly so. Because computers and networks remember everything we do when we punch a keyboard, click on a Website or touch a virtual keyboard, those massive logs of data saved by networks are now leading us into a new world of search: graph search.
Graph search, an open-source database project built on all the networking we do online every day, is the most far-reaching search IT to go mainstream since Google started storing up and ranking Websites more than a decade ago. Basically, a graph search database anonymously uses all the contacts in all the networks in which you work to help you find information. Anything you touch, any service you use and anything people in your networks touch eventually can help speed information back to you. It avoids anything non-relevant that would slow down the search.
How It Stands Apart From Standard Databases
Here's a typical use case: You might be looking for a particular type of restaurant in New York City--say Vietnamese. Using a graph search engine--let's deploy the one now powering Facebook's internal search--you enter a query (i.e., "Vietnamese restaurant in midtown Manhattan"); the graph search database then connects all the dots in your friends' accounts and identifies whether they've talked about, photographed or reviewed Vietnamese restaurants in midtown Manhattan. It looks at previous queries about Vietnamese restaurants you have made yourself in the past, it looks at the local geographic area where you currently are, and then it delivers those search results to you in microseconds.
The result is that you get a much more relevant type of search via people with whom you are connected--not a list of Web pages based on keywords that may or may not satisfy your query.
Rapidly Gaining Market Share
Graph databases are rapidly growing in usage, although most people are not aware of them. From Websites adding social-network features to telecoms providing personalized customer services to bioinformatics research, organizations are adopting graph databases as an efficient way to model and query already-connected data. Most of the growth of the genre thus far has been the result of word-of-mouth among database admins and CTOs.
Facebook put graph search on the mainstream IT map last January when Mark Zuckerberg announced it was going into early adopters' accounts. In July it went into general availability on the site. Other companies are now coming out with their own versions of the database based on the open-source model.
The world’s largest social network also revealed on Oct. 15 that it is planning to roll out Graph Search for its iOS application and for its Messenger app. That is expected to happen sometime in November.
Interestingly, although Facebook has made graph search widely known and freely available within its own environment, the social network did not invent the technology; it started as an open-source project in the late 1990s.
Pioneer of Graph Search Database: Neo Technology
Neo Technology researchers in Sweden have pioneered graph databases--they’ve been working on them since 2000--and have been instrumental in bringing them to a growing number of enterprises worldwide. Among those are Global 2000 companies such as Cisco Systems, Accenture, Deutsche Telekom and Telenor.
Neo Technology recently conducted its second annual GraphConnect conference in San Francisco (Oct. 3 and 4) to gather some of its developer community and customers and talk about continuing development of the platform.