Time-series data indicating a temporal variation of an index, which indicates a usage state of each of resources that are used by multiple processes, is acquired, and an operation-data matrix including vectors is generated based on the time-series data such that each of the vectors indicates the time-series data at a predetermined time interval and includes as an element the index indicating the usage state of one of the resources at the predetermined time interval. A basis matrix including a predetermined number of basis vectors is generated by performing nonnegative matrix factorization on the operation-data matrix. Component values, which respectively correspond to the resources, indicated by each of the predetermined number of the basis vectors are extracted, and information on the extracted component values is output as usage states of the resources that are used by each of the multiple processes.

Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.

A data analysis system is proposed for providing fine-grained low latency access to high volume input data from possibly multiple heterogeneous input data sources. The input data is parsed, optionally transformed, indexed, and stored in a horizontally-scalable key-value data repository where it may be accessed using low latency searches. The input data may be compressed into blocks before being stored to minimize storage requirements. The results of searches present input data in its original form. The input data may include access logs, call data records (CDRs), e-mail messages, etc. The system allows a data analyst to efficiently identify information of interest in a very large dynamic data set up to multiple petabytes in size. Once information of interest has been identified, that subset of the large data set can be imported into a dedicated or specialized data analysis system for an additional in-depth investigation and contextual analysis.

Techniques are provided for managing objects within an object store. An object is maintained within an object store. In an embodiment, a rule is enforced for the object that in-use slots of the object are non-modifiable and unused slots of the object are modifiable. Metadata of additional information for a slot within the object is attached to the object header. A first application allowed to access user data within the slot is provided access to the user data without being provided access to the metadata. A second application allowed access to the user data and the additional information is provided with access to the user data and the metadata for identifying a location of additional information within the object.

A method, apparatus, system, and computer program product for processing a query received through a network. A computer system identifies a topic in the query. The computer system identifies a set of friends of a user from a set of social media networks in which the set of the friends have an expertise in the topic identified in the query. The computer system ranks the set of the friends based on a level of the expertise of the set of the friends for the topic and an availability of the set of the friends to form a ranked set of the friends. The computer system returns results that contain the ranked set of the friends for the topic.

Techniques for a neural network output layer for machine learning are disclosed. A plurality of processing elements within a reconfigurable fabric is configured to implement a data flow graph, where the data flow graph implements a neural network. The data flow graph can include machine learning or deep learning. A layer is implemented, within the neural network, that maps a first vector of real values to a second vector of real values bounded by zero and one, where the second vector sums to a value of one using fixed-point calculations. The layer can include a final layer within the neural network. The layer that maps the first vector includes a Softmax function. Results of the neural network are classified based on a value of the second vector. The classifying can include part of a machine learning or a deep learning process.

A method for traversing hierarchical data is provided. The method may include generating, based on a source table stored in a database, an index for traversing a graph corresponding to the source table. The source table may identify a parent node for each node in the graph. The generating of the index may include iterating over the source table to generate an inner node map. The inner node map may include at least one mapping identifying one or more children nodes descending from an inner node in the graph. The graph may be traversed based at least on the index. The index may enable the graph to be traversed depth first starting from a root node of the graph and continuing to a first child node descending from the root node of the graph. Related systems and articles of manufacture, including computer program products, are also provided.

A device configured to emulate a correlithm object processing system comprises a memory and one or more processors. The memory stores a mapping table that includes multiple context value entries, multiple corresponding source value entries, and multiple corresponding target value entries. Each context value entry includes a correlithm object. The one or more processors receive at least one input source value and a context input value. The one or more processors identify a context value entry from the mapping table that matches the context input value based at least in part upon n-dimensional distances between the context input value and each of the context value entries. The one or more processors identify a portion of the source value entries corresponding to the identified context value entry, and further identifies a source value entry that matches the input source value. The one or more processors identify a target value entry corresponding to the identified source value entry.

A correlithm object processing system that includes a trainer configured to send a node entry request to a node engine that triggers the node engine to generate an entry in a node table. The trainer is further configured to receive a source correlithm object and a target correlithm object in response to sending the node entry request. The trainer is further configured to send a real world input value and the source correlithm object to a sensor engine which triggers the sensor engine to generate an entry in a sensor table linking the real world input value and the source correlithm object. The trainer is further configured to send a real world output value and the target correlithm object to an actor engine which triggers the actor engine to generate an entry in an actor table linking the real world output value and the target correlithm object.

A data analysis system is proposed for providing fine-grained low latency access to high volume input data from possibly multiple heterogeneous input data sources. The input data is parsed, optionally transformed, indexed, and stored in a horizontally-scalable key-value data repository where it may be accessed using low latency searches. The input data may be compressed into blocks before being stored to minimize storage requirements. The results of searches present input data in its original form. The input data may include access logs, call data records (CDRs), e-mail messages, etc. The system allows a data analyst to efficiently identify information of interest in a very large dynamic data set up to multiple petabytes in size. Once information of interest has been identified, that subset of the large data set can be imported into a dedicated or specialized data analysis system for an additional in-depth investigation and contextual analysis.