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Introduction Part 1/2

Introduction Part 2/2

how it works


"Search White Paper"


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 in the right amount 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 tardeoffs 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:


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 tardeoffs, 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 tardeoffs by understanding exactly what a user's preferences are, and additionally allows the user to store and share these tardeoffs 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 tardeoffs 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.




Retrieve highly relevant results that others miss:

Parametric search also known as constraint based or SQL-type searches, can be represented in an n dimensional space, where n is the number of attributes, by a box based on the search constraints (query conditions). The search retrieves the data that meet the query constraints in other words the elements that are in the box.

For example, the selection of a car that has a performance that exceeds 200 horsepower and priced under $25,000 would eliminate a vehicle with 250 Horsepower that costs $25,500. It only returns the cars within the constraints or "inside the box".

Optimization theory, teaches that ideal results are usually found close to the pareto optimal boundaries. These boundaries are typically the intersections of curves, planes and graphs representing constraints, utility functions, etc...

In the case of a parametric search, this intersection corresponds to the corner of the box. With SQL-type searches, data that falls outside the box is eliminated and does not show up in the result set. This is why parametric search often requires several trial and error attempts to locate the required data.

This means that a parametric or SQL-type search will miss highly relevant results that happen to "land just outside the box".

In comparison, Auguri’s search works by identifying an ideal result (often hypothetical) and measuring the distance of each element to the ideal in an n dimensional vector space. Auguri will retrieve ALL the elements that are close to the ideal result. As a result every relevant data will be retrieved.


Allow users to determine how to prioritize their own results

Until the advent of Auguri computers were really good at searching in a black or white environment (a condition is either met or it is not). But they were notoriously bad at dealing with tradeoffs and "gray zones". With Auguri this changes. Auguri introduces a new element to the search: the relative importance of the criteria. This additional dimension yields a highly more relevant result. For example if you are searching for a car with more than 200 Horsepower (Performance > 200HP) under the price of $25,000 (Price < $25K) traditional SQL-type queries would retrieve the same result irrespective of the affinity of the users to performance and their sensitivity to price.

On the other hand, Auguri will retrieve different results depending on whether the user is a "car buff" who is primarily interested in horsepower and would tradeoff price for a higher performance engine or if the user is price sensitive.

Auguri takes into account the relative importance of every criterion by weighing every criterion this translates in a stretch of the axis accordingly.

Leverage interactive aspect of the web to capture user needs

The challenge of the single field metaphor is that it makes it difficult to prioritize the results of the search to meet the specific needs of a user. This problem is compounded by the fact that (i) typical searches have very little context (i.e. all the Google engine knows are the words that are typed and there is very little else that is known about the user), and (ii) the amount of data on the web increases geometrically thus yielding in excess of 2,740,000 results for a search for “Large Screen Laptops”. GYM are focused on building smarter more intelligent ranking algorithms.

To address this challenge we believe that a paradigm shift is warranted. The new approach we propone, is based on leveraging the interactive aspect of the Internet. The best ranking algorithms developed by the most talented engineers of GYM in an attempt to guess the intent of a user will not yield as relevant a result as an interaction with the user to better understand what they are seeking and most importantly letting them decide how to rank, order and prioritize their results.
This new approach is particularly more attractive given that the associated revenues are highly dependent on relevance for click-through for example. In addition the interaction gives the chance to display more ads.

The new approach is predicated on building a platform that allows the user to select the way they want to prioritize their results instead of a relying on the guesswork of the sophisticated algorithms of GYM’s engineers. The Auguri patented technology and framework that was developed over the past 7 years is a perfect fit for this platform.
To illustrate our point, if someone walks into a video store and asks the clerk to recommend a movie, GYM’s approach would be to identify statistically the most popular movie. Our approach would be to engage the customer by asking about their mood, the genre they are interested in, if they have a preferred actor, whether they favor a new release or if they have a certain period in mind, etc… and accordingly match the movie that best meet their needs.




The Challenge: Implementing BI solutions or 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 can reverse engineer your queries. It takes a ranking of a set of possible alternatives (or result set) and determines the relative importance of various criteria accordingly (or the original query). 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.


Models query after decision process
Weighs importance of query criteria
Incorporates "Ideal Result" Concept
Enables trade-off
Offers flexible search capability
Queries incomplete data set
Infers query from result
Stores and shares intelligence



A decision (selection, search etc) is the selection of the optimal alternative(s) from a set of choices. The decision is typically made by performing tradeoffs (weighing the relative importance) over a set of factors or criteria. Tradeoffs become necessary in situations where various factors are conflicting, and alternatives are consequently less than perfect. To give a simple example, a consultant that often travels to deliver presentations is interested in a light weight laptop with a large screen. However, the criteria are conflicting; the larger the screen the heavier the laptop. Hence the ability to tradeoff between conflicting criteria is paramount.

The objective of a decision, a selection, a prioritization, or a triage is to select the best solution(s) from a set of alternatives. To be able to make a selection, the first component required is a set of data, or alternatives. This is the database of options which will be ranked according to their weighted proximity to a hypothetical ideal result.
A decision or selection is typically based on a set of criteria. These criteria are encapsulated by their criteria behavior (shown on the left side of Figure 1) and embodied by a function, such as a “utility” function, that captures the way we think about that criterion. For example, screen-size is a criterion when selecting a laptop, and typically, its behavior is captured by the notion that “larger is better”. Weight is another selection criterion, defined by a different function since we typically seek lighter laptops. But because laptop weight typically increases with screen-size, we have a conflict between these criteria. Humans know only one way to mitigate these conflicts: Tradeoffs.

The ideal laptop is lightest one that has the biggest screen. Unfortunately, such a laptop is hypothetical and does not exist. Criteria are almost always conflicting in real-world scenarios. This conflict is addressed via the notion of tradeoffs – relative prioritizations. Tradeoffs are a central component in the Auguri platform, just as they are in real decision-making situations. They play a pivotal role in determining how various options will score.