In my last two posts, I spoke with the two team leaders in Exalead R&D’s Web Search & Analytics division: Jim Ferenczi (Web Data Mining) and Rémi Landais (Innovative User Experience). Today I’ll share my conversation with Sébastien Richard, who oversees the work of both teams.

Sébastien’s mission is to guide the development of Exalead’s WWW search engine (the world’s third largest), and all related projects that seek to leverage the innovations that spring from it to serve the needs of business, science and government.

This includes Internet (cloud-based) offerings as well as porting all appropriate innovations to the Exalead CloudView enterprise platform, which is built on the same core software as the WWW search platform.

While Sébastien relishes the challenge of working with Big Data in general, what really excites him is this fact that his Big Data challenge exists in the unique context of the Web: “The Web touches everyone, everywhere. It’s woven into every facet of our personal and professional lives. I love the centrality and the energy of it, and the fact that it gives me the chance to work simultaneously on dozens of technology fronts at a grand scale: networking, communications, distributed computing, crawling, advanced semantics, data visualization, user experience design, multimedia access, analytics, you name it.”

To learn more about what Sébastien and his team are up to, follow the happenings at WWW2012 in Lyon. There’ll be a follow-up post after that event with an inside look at a one of Exalead’s most exciting projects to date .

In my last post on the Web Search & Analytics division in Exalead R&D, I shared some highlights of my conversation with Jim Ferenczi, who leads the division’s Web Data Mining team. Today, I’ll replay some notes from my talk with Rémi Landais, head of the division’s Innovative User Experience team. His mission, as daunting as it is enticing, is literally to “invent and discover next-generation applications of Big Data.”

It’s hefty charge, but one to which Rémi is well accustomed. He has spent the last several years collaborating with Exalead’s technical and academic partners as well as Exalead colleagues and customers to imagine, develop and test a whole host of multimedia access and analytic applications. These include Chromatik, which lets users search and navigate images based on color; Voxalead, which leverages speech-to-text transcription technology to enable search inside videos; and MuMa, a music search engine that, among other capabilities, automatically classifies songs by moods (sad, romantic, happy, etc.) using acoustic analysis.

His new role in the Web Search & Analytics division is therefore the same in spirit, though the data universe he’s taking on is greatly expanded, with multimedia content being joined by unstructured, semi-structured and structured Big Data of all flavors, both inside the enterprise and out on the Internet.

Like Jim, he has to maintain a wide field of vision, pushing the frontiers of technology and advancing the core CloudView platform while maintaining a close intimacy with the changing ways in which users consume, generate and interact with information.

So what’s on his list of current projects? “One of the things I’m doing right now is working with scientific organizations, government agencies and start-ups to explore innovative, high-value uses of Open Data.” (For those not familiar with Open Data, it’s a movement dedicated to making the widest possible range of data – to date mainly scientific and governmental data – freely available to the public through the Internet.)

For Rémi, the search-based application (SBA) model is tailor-made for tapping into the potential of Open Data: “As nice as it’d be if every data set was published using standard formats like RDF/XML, the reality is organizations are pushing out data any way they can, with some collections relatively scrubbed and organized and others completely raw. Search-based applications are a great fit because they are natively designed to process and aggregate large volumes of heterogeneous, less-than-perfect data. And for me, the ‘aggregation’ part is going to drive the most interesting use cases, whether it’s integrating multiple Open Data sources or cross-referencing Open Data with Web and enterprise resources. Of course, in addition, there’s no other technology as adept as SBAs at making even complex information accessible and meaningful to non-specialist users, which an essential part of the Open Data mission.”

PS: To learn more about Open Data, the Wikipedia Open Data page offers a good starting point. See also Exalead’s Dataconnexions post for info about Exalead’s involvement in the French Government’s Open Data initiative.

I had the good fortune to share some one-on-one coffee breaks this week with the fellows who head up the Web Search & Analytics division in Exalead R&D: Jim Ferenczi, who is in charge of the Web Data Mining team dedicated to enhancing the Exalead Web platform; Rémi Landais, who leads the Innovative User Experience team, whose mission is to invent and discover next-generation applications of Big Data; and Sébastien Richard, who’s the big cheese of the division.

I met first with Jim, our platform guru. One of his charges is building the Exalead WWW search index. Though Exalead’s public WWW search engine is now the world’s third largest, behind Google and Microsoft’s Bing (now that Yahoo! has handed over the search reins to Bing), Jim’s mission is not, as one might think, to cast the widest possible net across the Web, but rather the smartest one.

For one thing, there is certainly no shortage of junk on the Web, and Jim relishes the challenge of developing an ever more discriminating crawl ecosystem. It’s not only an approach that yields higher quality results for users, it also helps to optimize the size of the index. And, as Exalead is not Microsoft or Google, this is a welcome economic advantage.

As Jim notes: “The financial realities of our start-up origins made the drive for the best, rather than simply the biggest, index a necessity. But the real economies come from our core focus on lean computing. From the beginning, we set out to process the largest possible volume of data at the fastest possible rate on the smallest possible number of commodity servers. As a result, our Web search engine operates at hardware economies-of-scale no other WWW engine can touch, and as our Web and enterprise platform are one and the same, it means we’ve been able to deliver massive linear scaling on a tiny, low-cost footprint to the enterprise as well.”

As if all this weren’t enough to keep Jim busy, he has to continually advance the robustness and sophistication of our semantic processing pipeline while still pushing the boundaries of lean computing. It a crazy mission, but then who cares if you can ‘handle’ Big Data if you can’t make sense of it?

Next up in my Big Data series: Man-with-Crazy-Mission #2, Rémi Landais.