| |
In today's "Information Age", solution providers face the increasingly difficult challenge of harnessing a vast universe of electronic information. In particular, query technologies have not kept up with the enormous progress of data storage technologies. This has resulted in "information overload," where organizations can store large amounts of data, but lack the appropriate tools to extract the right information from it with a reasonable amount of effort.
To address this problem, Auguri has developed a next generation query technology that moves to the data layer some of the intelligence and processes traditionally found in the application layer. This is accomplished by enhancing and extending SQL with the introduction of a new dimension that patterns data access after the way humans think, performing tradeoffs and comparisons among alternatives. As a result, the constraints imposed by the SQL metaphor are broken, thereby enabling development of applications richer in functionality. A more subtle, yet much more far-reaching, consequence is that Auguri-enabled applications acquire the ability to understand, collect, store, and process intelligence about application users, preferences, and needs. This leads to better and more relevant data, and, most importantly, intelligence about that data that can easily be made available throughout the organization. Auguri-based applications inaugurate the era of intelligence interchange.
Auguri's framework enables the rapid development of query-rich, search-intensive or rapid intelligence-gathering applications. When the result of a search influences critical decisions, Auguri is your ideal platform. The framework is powered by two innovative breakthrough technologies:
TRADEOFF-BASED SEARCH
The Challenge: Traditional constraint-based SQL search techniques only have the capability to return results that exactly match the search criteria. This causes several problems. For one, many applications miss the opportunity to collect critical data. Secondly, using a constraint-based approach to searches makes it impossible to develop applications that model their data access after the processes used by users to think, make decisions, search or analyze data. For example, current employee-relationship-management systems make it difficult for an HR manager to quickly focus in on the best person for an assignment. In the decision process, one might be willing to trade a couple of years of experience for a degree from a top-tier university. With SQL-style searches, you are only able to specify exact cut-off values and not preferences or tradeoffs, often causing you to unintentionally eliminate the best candidate. SQL queries often return either scores of possible matches or "no results found". When confronted with too many results, users often define arbitrary screening constraints just to make their options manageable. The system then eliminates viable options as a result of these artificial screening constraints. In addition, SQL-style searches do not offer a good way to grasp accumulated enterprise knowledge about the processes leading to the best decisions. This knowledge is kept with individuals and is lost when they leave the enterprise. What enterprises need is an intelligent screening system that is capable of conducting searches in the way that humans think - one that enables the user to specify tradeoffs by understanding exactly what a user's preferences are, and additionally allows the user to store and share these tradeoffs with the entire organization.
The Auguri Solution:
Auguri has developed WISE (Weighted Intelligence Search Engine), a next-generation query and match technology that augments SQL by introducing a new dimension to queries - moving intelligence from the application to the data access level where it can be efficiently stored, manipulated and shared. Unlike traditional query methods, Auguri's engine is able to incorporate tradeoffs and the concept of an ideal result through its unique ability to weigh search criteria. For example, corporate buyers typically take into consideration a series of criteria and weigh them according to their importance in addressing enterprise needs when making their purchase decision. They may be willing to compromise on the performance of a laptop for its availability, and select a 1.2GHz machine today over a 4GHz delivered in a couple of months, or they may be willing to forgo a DVD drive in exchange for a lighter laptop. WISE is able to understand exactly what the application user is seeking through its need assessment capability and delivers a ranked list of best matches. These matches are ranked according to how closely they match the ideal search result. Furthermore, in order to search, the user only needs to specify the relative importance of criteria and not exact values, enabling even those with a novice understanding of a topic to quickly pinpoint exactly what it is they are searching for. WISE can then store this intelligence, allowing the knowledge of the most competent employees to be shared with and used by the others. WISE takes queries to a new level. Beyond searching, it enables matching. |
|
|
|
The
Challenge:
Implementing rule-based engines to analyze alternatives in light of a user's preferences can be resource intensive both initially and during ongoing maintenance. Statistics-based engines may not contain enough detailed information to enable calculations at the level of individual user preference. More problematic is the reality that SQL engines are unable to infer the issuing query based on search results which means pragmatically an inability to understand the rationale or reasoning behind particular results.
The Auguri Solution:
The Auguri Inference Engine takes a ranking of a set of possible alternatives and determines the relative importance of various criteria accordingly. Based on the same technology as WISE, the Auguri Inference Engine can not only tell you what criteria are important, but how important each criterion is on an individual basis. Inferring query parameters from the results of a query provides a particularly useful tool for analyzing application and user behavior and preferences. The inference capability opens up the door to a whole new suite of applications. For example, by viewing a user's click-stream, one can infer the not only the nature of the visit but also the motivations behind it. Auguri's state-of-the-art inference technology provides applications, in addition to what data is searched, intelligence about why it is sought.
Using rule-based engines to implement functionality that analyzes available
alternatives ranked by a user's preferences can be resource intensive
both at the initial implementation stage and during ongoing maintenance.
Statistics-based engines may not contain enough detailed information to
enable calculations at the level of individual user preference.
The Auguri Inference Engine analyzes a set of available alternatives,
and a user-defined ranking of the alternatives based on a number of criteria,
to determine the user’s perceived relative importance of these criteria.
Based on the same orientation as WISE, the Auguri Inference Engine is
ideally suited for applications that need to determine user preferences
at the individual level. |
|
