A system, that when operated, creates an index for a database arrangement, wherein the system includes a support server and an indexing server communicably coupled to the database arrangement, wherein the support server: receives a document, metadata pertaining to the document, and a document identifier associated with the document; extracts keywords from the document and store the keywords in a meta-document; stores the document identifier corresponding to the metadata in the database arrangement; and provides, to the indexing server, the meta-document and the document identifier; and wherein the indexing server: receives the meta-document and the document identifier from the support server; extracts keywords from the meta-document; and creates the index, wherein the index includes document identifier listed corresponding to the extracted keywords.

Embodiments include systems, methods, articles of manufacture, and computer-readable media configured process data in a structured format and an unstructured format and applying one or more algorithms to detect elements and links between the elements in the data. Embodiments are further configured to generate a graph model comprising nodes comprising the elements and edges comprising the links.

A method for maintaining links is described. A document selection is received, including a first destination document containing a first link to linked content. A first source document containing the linked content and not contained within the document selection is identified using the first link. A copy mode is selected, using an intrinsic property associated with the first link, from: a first mode where a second destination document that is a copy of the first destination document is generated and includes a second link to the linked content within the first source document, without copying the first source document, and a second copy mode where i) a second source document that is a copy of the first source document is generated, ii) the second destination document is generated and includes a second link to the linked content within the second source document. The selected copy mode is performed.

Processor-implemented systems and methods are provided for generating clusters of technical documents. A method includes analyzing degrees of similarity among the technical documents using a hierarchical taxonomy code similarity model and a text clustering model. Clusters of the technical documents are generated based upon the analyzed degrees of similarity from the models.

An approach is provided to analyze posts included in threads of one or more threaded discussions. The analysis identifies child posts that refer to a parent post that was selected a user of a user interface. A statement in the parent post is identified and that statement is analyzed using agreement data found in the child posts that refer to the parent post. The parent post and the analysis of the statement is displayed to the user at the user interface.

Techniques are described herein for creating an algorithm for batch mode processing against big data. The techniques involve receiving one or more user commands from a set number of commands that correspond one-to-one with a set number of low-level database operations. In a preferred embodiment, the set of database operations includes only FILTERS, SORTS, AGREGGATES, and JOINS. In the algorithm formation process, database operations are performed on a sample population of records. The user drills down to a set of useful records by performing database operations against the results of the previous database operations. While the database cluster is receiving operations, the system is tracking the operations in a dependency graph. The chains selected within the dependency graph indicate which operations are used to create the algorithm. To generate the algorithm, the database cluster reverse engineers the logic for performing those operations against big data.

Dependency trees are generated for questions and answers of a question answering (QA) corpus in which the answers are associated with the questions. Generating the dependency trees includes identifying root nodes. A word near an identified root node of one of the questions is compared to words of answers associated with the one of the questions. The word is determined to be in less than a threshold number of the associated answers. The word is identified as a stop word.

The invention provides decision tree based systems and methods for estimating the risk of acute coronary syndrome (ACS) in subjects suspect of having ACS. In particular, systems and methods are provided that employ additive decision tree based algorithms to process a subject's initial cardiac troponin I or T (cTnI or cTnT) concentration, a subject's cTnI or cTnT rate of change, and at least one of the following: the subject's age, the subject's gender, the subject's ECG value, the subject's hematology parameter value, to generate an estimate risk of ACS. Such risk stratification allows, for example, patients to be ruled in or rule out with regard to needing urgent treatment.

Systems and methods for content provisioning are disclosed herein. The system can include memory having a content database, a task database, and a user profile database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include a server that can: receive a user identifier from the user device; retrieve user information from the user profile database, which user information identifies one or several attributes of the user; retrieve user task data from the task database, which user task data identifies a plurality of tasks for completion by the user; automatically generate prioritization data for the plurality of tasks identified by the user task data; select a task based on the prioritization data; and send content relating to the selected task to the user device.

An accumulated information data store may include topic nodes, each having a text description of limited length and (in some cases) one or more attributes. A particular topic node may be associated as a parent topic node other child topic nodes such that the topic nodes form at least one data tree. An information processing engine may access information in the accumulated information data store and determine that a plurality of topic node text descriptions are similar and classify them as similar topic nodes. At least a part of the text description associated with one of the similar topic nodes may be selected as a favorable text description for the similar topic nodes. The system may also unify the similar topic nodes as identical topic nodes when they are currently grouped together as having the same upper tree hierarchy.