Abstract: Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.

Abstract: Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.

Abstract: Methods and systems for recommending limited, personalized and relevant list of prospects to enterprises, in a configurable, automated, scalable and machine-learnt way. According to one embodiment, raw data about potential prospects across diverse areas is collected from various data sources. The raw data is transformed to variables containing values in a binary format, also known as interests, in accordance with a predetermined set of rules. An interest graph is created with the interests as nodes and affinity between them as edges and net affinities are calculated and stored in an interest table. The user's requirements are understood through user input and a set of user-relevant interests is captured and extended with additional similar interests from the interest table. Multiple scores are calculated for each of the potential prospects based on this set of interests. A net score for each potential prospect is calculated and highest potential prospects are finally recommended.