Abstract: Systems and methods are described for use in higher-order-knowledge-based searching of content available from a network of data-storage devices. In various embodiments, at least one computational expression representative of a relational framework for content is identified and provided to an information retrieval system for use in searching for content desired by a user. The relational framework for content may include rules, expressions, equations, and/or constraints, which bind, relate, or associate certain content with other content. A computational expression may be determined from processing structured data. The structured data may be identified during crawling of a network or may be expressly provided to an extractor. Use of a computational expression by an information retrieval system may more efficiently and accurately return desired content to a user than is possible with traditional information searching methods.

Abstract: A data search and retrieval system that, in response to a search query, applies a model to search results to generate information to be returned to a user. A user may modify the model via a set of controls displayed to the user. The modified model may then be applied to search results to generate information in compliance with the modified model. Modified models may be stored locally on a client system or stored in the search system and made available to other users so that the modified models can be applied to subsequent requests for information. The modified models and the results of a search may be stored on a client machine such that, as the model is modified, new information may be generated based on the original search, reducing communication bandwidth, which can improve the speed and cost of providing user interaction with data in a mobile environment.

Abstract: A data search and retrieval system that, in response to a search query, dynamically selects and applies a model of information to be returned to a user. The model may be selected based on the search query directly, or indirectly based on data returned by a search engine applying the query. For this purpose, the system may include an index of models, similar to a search index. Models may include expressions that may be descriptions of computations, equations, constraints or rules to define information to be returned. These elements of a selected model may be executed within to the search stack based on data identified in a search of web pages by executing the query or other dynamically obtained data. Execution of a model may lead to collecting new information, such as through further searching or performing computations, or may result in ordering or other formatting of data.

Abstract: A data search and retrieval system that, in response to a search query, dynamically selects and applies a model of information to be returned to a user. The model may be selected based on the search query directly or indirectly based on data returned by a search engine applying the query. For this purpose, the system may include an index of models, similar to a search index. Models may be authored and contributed to the search and retrieval system by third parties, and an association between each such contributed model and characteristics of a search query, such as specific search query terms, may be stored in the index of models. A user of the search and retrieval system may provide feedback on a model that was used to generate information in response to the user's search query, and such feedback may be used to update the index of models.

Abstract: A potential user query that can be subsequently received from a user is identified. An interface exposed by a data service is invoked, which includes providing the potential user query to the interface. Search results in response to the potential user query are received from the interface and maintained in a complex data set store. If a user query is subsequently received that maps to a normalized query that is the potential user query, then search results for the normalized query are obtained from the complex data set store and returned as the search results for the user query.

Abstract: System(s) and method(s) are provided to trade compensation credit(s) in a customer price incentive scheme funded through advertisement. A secondary market for rewards is established through advertisement spend from a set of disparate category advertisers (e.g., brand advertisers and product advertisers). Market liquidity is injected through an advertisement component that directs a portion of advertisement spend to issue tradable compensation credit(s) at a initial offering price. A trade platform facilitates commercialization of compensation credit(s) among a set of registered advertisers: A regulatory component provides governance and establishes pricing of traded compensation credit(s), while a brokerage component facilitates negotiation among traders. Disparate advertisers can be joined for trading based at least in part on advertiser intelligence.

Abstract: System(s) and method(s) are provided that facilitate intrinsically targeted, dependable peer-to-peer referral and compensation within an intent-compensation scheme. The referral system synergistically exploits trust mechanisms existing among a referring agent and a referred agent in order to generate high-quality referrals based on a determination of commercial intent from the referred agent. In exchange of conveyed intent at a time of a transaction with a referred agent, system platform directly compensates both the referred agent and the referring agent. Compensation of the referred agent is ensured via tracking mechanism that can identify a referral originating device. Information associated with referral(s) is scoped through privacy profiles, and referral/compensation integrity is maintained via an antifraud component and a tracking component that can identify referring and referred agents. Compensation can be funded trough advertisement spend.