There are few enterprise applications in which the wheel is reinvented more often than in deployed search engines. In a typical enterprise, employees repeat the same search process over and over in different locations, divisions, and product groups. Gigantic amounts of professional labor are wasted, and worse, varying degrees of success are obtained by users with different levels of skill and domain expertise. Better than asking a search engine a question is asking a person that knows the answer. Search applications are evolving from “find me a list of documents to read” to “find me a person that knows the answer.”  David Seuss will share Northern Light’s experiences developing and deploying social search solutions for strategic business research in large enterprises.

While hyperlinking was a true innovation more than a decade ago, its original intent and purpose has become increasingly irrelevant as the amount of linkable digital information continues to grow exponentially. Hyperlinks, by nature, are manually implemented, relying on the content creator or author to define meaningful connections to known and related information. The real challenge is this: Only the user, at an exact point in time, knows which word or phrase within a document is important. And it is different for every single user, every single time. In this presentation, a new model for hyperlinking is discussed and demonstrated, a method that has two key components—the interface, in which users can turn any or many words on the screen in front of them into hyperlinks, and a semantic/statistic analytics engine that analyzes and disambiguates surrounding content.

The technology of information retrieval and search is based on retrieving documents in response to a query entered inside the ubiquitous search box. But a different model of queries, those launched by clicking on the “more results like this” or the “find similar documents” links, avoids using the search box. Indeed, it may be impossible to enter such a query in a search box. In this talk we extend the idea of launching a nonsearch-box query by clicking on an “object” in a document to retrieve more documents that discuss or contain this object. Such an object can be a phrase, a sentence, a chart, or a graphic. Called in-document, demonstrative (IDD) queries, IDDs are fundamentally different from search-box queries because their linguistic context needs to be taken into account. The same phrase (or chart) occurring in two documents can express two different topics (or two different sets of data) depending on the context. Queries launched by clicking on these two occurrences of the phrase should return different sets of documents.

Business success is often dependent on making timely decisions based on the best information available. Typically, for text information, this has meant using document search. However, the process can be accelerated by using agile text mining to provide decision-makers directly with answers rather than sets of documents. This presentation will review the challenges faced in bringing together diverse and extensive information resources to answer business-critical R&D questions in the pharmaceutical domain. In particular, it will outline how an agile NLPbased approach for discovering facts and relationships from free text can be used to leverage scientific knowledge and move beyond search to  automated profiling and hypothesis generation from millions of documents in real time.

Search has become a commodity tool for helping users find diverse information from heterogeneous sources. But corporate users are now asking for knowledge-centric applications, they demand accuracy, findability and real-time interactivity. Text Analytics turns information into smarter content and dramatically enhances the way people interact with information to get unique business insights.

Search is often contrasted with database retrieval. Much research in language processing and in databases concerns extracting structure from unstructured data and loading this structured data into databases. Sometimes there are advantages to moving in the other direction. This session presents the application of database offloading, moving data from structured databases into the world of free-text search. Search engines provide ease of use (since no query language need be learned) and maintain traceability (since results are linked back to their original formats). Real case studies are given of exploiting search engine connectors to replace database search with faceted text search over largescale, real-time operational data. As deeper semantics are correctly treated by search engines, database offloading becomes an important hidden application of search engine technology.

Correctly making sense of sentiment, opinion, and mood—subjective, attitudinal information—poses special challenges for search. First is properly indexing sentiment. Next is understanding searcher intent (which is itself contextual and subjective) in order to respond to search queries. Finally, there are issues with presenting sentiment-search findings. This session will describe applications and business benefits in areas including customer support and satisfaction, brand and reputation management, and marketing and product management. It will describe the characteristics of sentiment and opinion, outlining technical approaches and challenges. It will provide examples of online sentiment analysis via the application of search technologies to attitudinal information found in traditional and social media.

When you need information and analysis, you should not have to start by figuring out where that information resides and whether the dots are connected between pieces of relevant information. Rapidly gaining a single view into all the relevant information you need, regardless of format, is now possible with Unified Information Access (UIA), a new class of information-centric infrastructure that helps you quickly access information from any source, including both core applications and databases and external content from the web. UIA is optimized to work across both structured and unstructured data sources, enriching the information for better insight and decision-making. This session will present UIA as a key enabler of competitive advantage. By bringing together all relevant information sources, it is possible to correlate what users say (in text) and what they do (in transactions). Such analysis can have a dramatic impact on user experience, conversion rate, internal decision-making processes, etc.

The Evvie Award is given in honor of Ev Brenner, one of the leaders in online information systems and functions. The award was established after Brenner’s death in 2006. Brenner served on the program committee for Search Engine Meeting since its inception. Everett Brenner is generally regarded as one of the “fathers” of commercial online databases.  He worked for the American Petroleum Institute and served as a mentor to many of the innovators who created the commercial online industry.