Abstract: A crowdsourcing data structuring system and method for capturing unstructured data from the Web and adding structure by placing the data in a document that is accessible by others in a cloud computing environment. Using crowdsourcing, the unstructured data is annotated, amended, and verified to add structure to the unstructured data. An anchor and update module convert the data to a pointer that links the document to the data at an information source and stores the pointer in the document rather than the data itself. The data displayed in the document is updated whenever the information source is updated. A contribution module allows users to add data to the document, a validation module allows users to determine the validity of the data linked to in the document, and an expert ranking module allows users to rank the expert or contributor of the data in the document.

Abstract: A “code splitting tool” provides various techniques for automatically analyzing and rewriting existing browser-based applications to introduce dynamic code loading into those applications thereby improving perceived application responsiveness. Structural elements of application code (including functions, classes, etc.) are broken into small “stubs” with corresponding bodies. Rewritten applications then initially transfer only the portion of the code (including some combination of stubs and bodies) to the client as necessary for initial application execution. Additional stubs and/or bodies are then transferred either on-demand at runtime or in the background. Automated code rewriting proceeds without requiring any application-specific knowledge or changes to existing code prior to code rewriting. Further, the code splitting tool can tailor code rewriting to specific computing devices (computers, PDA's, cell phones, etc.

Abstract: A classifier that disambiguates among entities based on a dictionary, such as corpus of documents about those entities, is built by incorporating probabilities that an entity exists that is not in the dictionary. Given a document it is associated by the classifier with an entity. By incorporating out of collection probabilities into the classifier, a higher level of confidence in the match between an entity and a document is achieved.

Abstract: A classifier that disambiguates among entities based on a dictionary, such as corpus of documents about those entities, is built by incorporating probabilities that an entity exists that is not in the dictionary. Given a document it is associated by the classifier with an entity. By incorporating out of collection probabilities into the classifier, a higher level of confidence in the match between an entity and a document is achieved.

Abstract: A crowdsourcing data structuring system and method for capturing unstructured data from the Web and adding structure by placing the data in a document that is accessible by others in a cloud computing environment. Using crowdsourcing, the unstructured data is annotated, amended, and verified to add structure to the unstructured data. An anchor and update module convert the data to a pointer that links the document to the data at an information source and stores the pointer in the document rather than the data itself. The data displayed in the document is updated whenever the information source is updated. A contribution module allows users to add data to the document, a validation module allows users to determine the validity of the data linked to in the document, and an expert ranking module allows users to rank the expert or contributor of the data in the document.

Abstract: A “code splitting tool” provides various techniques for automatically analyzing and rewriting existing browser-based applications to introduce dynamic code loading into those applications thereby improving perceived application responsiveness. Structural elements of application code (including functions, classes, etc.) are broken into small “stubs” with corresponding bodies. Rewritten applications then initially transfer only the portion of the code (including some combination of stubs and bodies) to the client as necessary for initial application execution. Additional stubs and/or bodies are then transferred either on-demand at runtime or in the background. Automated code rewriting proceeds without requiring any application-specific knowledge or changes to existing code prior to code rewriting. Further, the code splitting tool can tailor code rewriting to specific computing devices (computers, PDA's, cell phones, etc.

Abstract: Systems and methods establish groups among numerous indications of failure in order to infer a cause of failure common to each group. In one implementation, a system computes the groups such that each group has the maximum likelihood of resulting from a common failure. Indications of failure are grouped by probability, even when a group's inferred cause of failure is not directly observable in the system. In one implementation, related matrices provide a system for receiving numerous health indications from each of numerous autonomous systems connected with the Internet. A correlational matrix links input (failure symptoms) and output (known or unknown root causes) through probability-based hypothetical groupings of the failure indications. The matrices are iteratively refined according to self-consistency and parsimony metrics to provide most likely groupings of indicators and most likely causes of failure.

Abstract: Systems and methods establish groups among numerous indications of failure in order to infer a cause of failure common to each group. In one implementation, a system computes the groups such that each group has the maximum likelihood of resulting from a common failure. Indications of failure are grouped by probability, even when a group's inferred cause of failure is not directly observable in the system. In one implementation, related matrices provide a system for receiving numerous health indications from each of numerous autonomous systems connected with the Internet. A correlational matrix links input (failure symptoms) and output (known or unknown root causes) through probability-based hypothetical groupings of the failure indications. The matrices are iteratively refined according to self-consistency and parsimony metrics to provide most likely groupings of indicators and most likely causes of failure.