The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

Systems and methods for spilling data for hash joins are described. An example method includes determining an amount of available space in a first memory used by a set of relational queries is insufficient for a first relational join query. The first relational join query comprises a join operation. The method also includes determining a set of build memory sizes and a set of probe memory sizes for a set of partitions for the set of relational queries. The method further includes identifying a first partition of the set of partitions based on the set of probe memory sizes and the set of build memory sizes. The method further includes copying the first partition from the first memory to a second memory, wherein the first partition comprises a first build portion and a first probe portion.

In some embodiments of the present disclosure, techniques are utilized that allow answers to be provided to end users such as health care consumers, based on benefit book documents. The benefit book documents, which do not initially contain machine-readable structural or semantic information, are processed in order to detect structure and create semantic content based on the structure. This semantic content may then be added to a graph that represents the information contained in the benefit book document. A computing device may then use the nodes of this graph to answer questions received from consumers, where templates that provide answers to the questions reference the nodes of the graph.

In some embodiments of the present disclosure, techniques are utilized that allow answers to be provided to end users such as health care consumers, based on benefit book documents. The benefit book documents, which do not initially contain machine-readable structural or semantic information, are processed in order to detect structure and create semantic content based on the structure. This semantic content may then be added to a graph that represents the information contained in the benefit book document. A computing device may then use the nodes of this graph to answer questions received from consumers, where templates that provide answers to the questions reference the nodes of the graph.

Provided are methods and systems comprising receiving a plurality of data tables, wherein each data table comprises at least one row, generating a first handle for a first value in a table of the plurality of tables, wherein the first handle comprises an indication of whether a row in each of the plurality of tables is associated or not associated with the first value, and performing one or more operations on the first handle.

A method and system for eliminating the redundant allocation and deallocation of special data on disk, wherein the redundant allocation and deallocation of special data on disk is eliminated by providing an innovate technique for specially allocating special data of a storage system. Specially allocated data is data that is pre-allocated on disk and stored in memory of the storage system. “Special data” may include any pre-decided data, one or more portions of data that exceed a pre-defined sharing threshold, and/or one or more portions of data that have been identified by a user as special. For example, in some embodiments, a zero-filled data block is specially allocated by a storage system. As another example, in some embodiments, a data block whose contents correspond to a particular type document header is specially allocated.

A system and method for chatbot and search engine integration comprising chatbot crawler engine configured to detect all possible paths through a conversational flow between a chatbot and a user, and also comprising a chatbot search integration manager configured to receive a processed conversation flow from the chatbot crawler engine, parse the conversation flow to identify keywords and features, and build an indexable data structure which can be integrated into search engines in order to expose the information and data contained within the chatbot's knowledge base. This integration may allow search engine users to be redirected to a website hosting the chatbot when an indexed data structure comprises information relevant to a search engine query.

The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines, each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit to determine an amount of a resource for each of the machines to service the task requirement for each machine, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the forward resource market.

The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines, each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit to determine an amount of a resource for each of the machines to service the task requirement for each machine, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the forward resource market.