Abstract: This disclosure describes a system for managing inventory as it transitions into a materials handling facility, as it transitions between locations within a materials handling facility and/or as it transitions out of a materials handling facility. In some instances, a user (e.g., picker or picking agent) may retrieve an item from an inventory location and place the item into a tote. The systems described herein detect the item when it is added to or removed from the tote.

Abstract: An analytical database server and a method for enabling investigative data analytics have been disclosed. The database server comprises a data loader that receives data from a plurality of data sources, and loads the received source data tables onto a Hadoop Distributed File System (HDFS). A processor processes the source data tables loaded onto the HDFS and assigns a unique turf row (TR) identifier to each of the records present in the source data tables. The source data tables and the corresponding metadata are organized into a first data table, a second data table and a third data table. The first table comprises a record for each of the received source data tables. The second data table stores, in the form of an inverted list, the data originally contained in the received source data tables. The third data table stores the unidirectional relationships between the source data tables.

Abstract: A self learning system and a method for categorizing input data have been disclosed. The system includes a generator that generates an initial training set comprising a plurality of words linked to scores/ratings which are based on the sentiments conveyed by the words. The words and corresponding ratings and sentiments are inter-linked and stored in a repository. A rule based classifier segregates the input data into individual words, and compares the words with the entries in the repository, and subsequently determines a first score corresponding to the input data. The input data is also provided to a machine-learning based classifier that generates a plurality of features corresponding to the input data and subsequently generates a second score corresponding to the input data. The first score and the second score are further aggregated by an ensemble classifier which further generates a classification score which enables the data to be classified into a plurality of predetermined categories.

Abstract: An analytical database server and a method for enabling investigative data analytics have been disclosed. The database server comprises a data loader that receives data from a plurality of data sources, and loads the received source data tables onto a Hadoop Distributed File System (HDFS). A processor processes the source data tables loaded onto the HDFS and assigns a unique turf row (TR) identifier to each of the records present in the source data tables. The source data tables and the corresponding metadata are organized into a first data table, a second data table and a third data table. The first table comprises a record for each of the received source data tables. The second data table stores, in the form of an inverted list, the data originally contained in the received source data tables. The third data table stores the unidirectional relationships between the source data tables.

Abstract: The various embodiments herein provide a method and system for building a relationship hierarchy from big data. The method comprises extracting a plurality of relationships defined between entities from a big data, building relationship recognition models adapted to identify different forms of generic relationships, resolving the relationships by grouping the similar relationships together and separating the relationships which are syntactically and semantically dissimilar and reconciling the resolved relationships to build the relationship hierarchy. The relationship hierarchy comprises groups and subgroups of relationships created based on generic relationship similarity based on a contextual aspect and a specialization aspect using a Language and Domain model. The method of extraction of plurality of relationships from the unstructured data is a self-learning process which uses open information extraction techniques for learning new relationships.

Abstract: The various embodiments herein provide a method and system for modeling a data. The method for modeling data comprises steps of extracting the data from a plurality of data sources, identifying a plurality of entities from the plurality of data, defining occurrence of a relationship between the plurality of entities, capturing recurrences of the relationship between the plurality of entities based on one or more common interactions between the plurality of entities and creating a data model indicating the occurrences and recurrences of the relationship between the plurality of the entities. The data model is adapted to store data corresponding to the plurality of entities, the relationship between the plurality of entities and the common interactions between the plurality of entities. The plurality of entities includes contents of a digital data artifact.

Abstract: The various embodiments herein provide a method for providing a personalization solution based on a multi-dimensional data. The method comprises of identifying a target event for personalization, profiling a plurality of entities associated with the target event, identifying a plurality of attributes adapted for predicting the target event, identifying one or more relevant attributes from the plurality of attributes, determining a personalization context associated with the target event, identifying at least one analysis algorithm for processing the identified target event and creating a predictive analytical model for building an optimal personalization solution. The target event is a personalization task which is formulated by analyzing an interaction between the plurality of entities. The plurality of entities are explanatory factors adapted for predicting an outcome of the target event.

Abstract: The various embodiments herein provide a system and method for mining frequent patterns in relationship space from a plurality of relationship sequences extracted from a big data The system comprises a data repository for collecting and storing the big data. An Entity Store for collecting and storing a plurality of entities from the big data, an Entity Hierarchy for representing a hierarchical structure of entities, a Relationship Store for collecting and storing relationship instances between the pluralities of entities, a Relationship Hierarchy for representing a hierarchical structure of relationship, a language/domain model for organizing entities and relationships in a hierarchical manner, a pattern query Processing Module (PQPM) for processing, a pattern query related to finding patterns in relationships and entities, and a Pattern Generation Module (PGM) to generate frequent patterns and a Frequent Pattern Display Module (FPDM) to provide a visual presentation of the mined patterns.

Abstract: The various embodiments herein provide a method and system for building a relationship hierarchy from big data. The method comprises extracting a plurality of relationships defined between entities from a big data, building relationship recognition models adapted to identify different forms of generic relationships, resolving the relationships by grouping the similar relationships together and separating the relationships which are syntactically and semantically dissimilar and reconciling the resolved relationships to build the relationship hierarchy. The relationship hierarchy comprises groups and subgroups of relationships created based on generic relationship similarity based on a contextual aspect and a specialization aspect using a Language and Domain model. The method of extraction of plurality of relationships from the unstructured data is a self-learning process which uses open information extraction techniques for learning new relationships.

Abstract: The various embodiments herein describe a method for providing information visualization comprising identifying a plurality of events from a big data, calculating a temporal distance between at least two events, calculating a semantic distance between the at least two events, storing the calculated semantic distance and the temporal distance with respect to the at least two events in a data structure, providing the semantic distance and the temporal distances calculated between the at least two events on a visual representation, identifying one or more relevant events with respect to the target event based on the semantic distance and the temporal distance through visualization, selecting a plurality of events that influence the target event and examining a pattern of the plurality of events to realize possible collinearity of the events to further reduce the influencing events in order to facilitate feature selection through visualization.

Abstract: The various embodiments herein provide a method and a system for building an entity hierarchy. The method comprises extracting a plurality of entities from a bin data, determining a parent entity by understanding a context in which the entity is used, resolving the entities by bringing the synonymous entities together and holding the polysemous entities apart based on a semantic context and a syntactic context and building a hierarchical structure of entities using knowledge repositories, ontologies and language repositories along with natural language processing techniques. The method of extracting entities from the structured data comprises identifying each data point as an entity and identifying entities based on a relationship defined with other entities. The method of extracting entities from unstructured data includes a self-learning process and training based learning process to learn new parent entities from domain specific documents using new entity recognition models.

Abstract: The various embodiments herein provide a method and system for modeling a data. The method for modeling data comprises steps of extracting the data from a plurality of data sources, identifying a plurality of entities from the plurality of data, defining occurrence of a relationship between the plurality of entities, capturing recurrences of the relationship between the plurality of entities based on one or more common interactions between the plurality of entities and creating a data model indicating the occurrences and recurrences of the relationship between the plurality of the entities. The data model is adapted to store data corresponding to the plurality of entities, the relationship between the plurality of entities and the common interactions between the plurality of entities. The plurality of entities includes contents of a digital data artifact.

Abstract: The various embodiments herein provide a method and system for personalizing information retrieval in a communication network. The method involves receiving one or more inputs from several sources and processing the information present in the received inputs. A contextual and personalized search is performed based on the received inputs and the results are displayed in support of the information in the communication network. The system has an application server for receiving the inputs from the sources and a processor for processing the information in the received inputs. One or more virtual documents are generated based on the processed information to perform a search and the results are displayed.

Abstract: The various embodiments herein provide a method and system for personalizing information retrieval in a communication network. The method comprising receiving one or more inputs from a plurality of sources, processing information present in the one or more inputs received from the plurality of sources, performing a contextual and personalized search based on the inputs received and displaying one or more results in support of the information in the communication network. The system herein comprising an application server for receiving one or more inputs from a plurality of sources, a processor for performing at least one of processing an information present in the inputs received from the plurality of sources, generating one or more virtual documents based on the processing of the information present in the one or more inputs, performing a search based on the virtual documents generated and a display unit for displaying one or more results.

Abstract: The present invention is generally related towards enhancing the yield and/or cold-flow properties of certain hydrocarbon products, increasing the degree of isomerization in a diesel product and/or increasing the production rate of a diesel product. The embodiments generally include reducing the residence time of lighter hydrocarbon fractions during hydrocracking, thereby decreasing secondary cracking, by various configurations of introducing at least two hydrocarbon feedstreams of different boiling ranges at different entry points in a hydrocracking unit. A method further includes forming a hydrocarbons stream comprising primarily C5+ Fischer-Tropsch hydrocarbon products; fractionating hydrocarbons stream to form at least a wax fraction and an intermediate fraction which serve as separate feedstreams to a hydrocracking unit comprising at least two hydroconversion zones.

Abstract: The present invention is generally related towards enhancing the yield and/or cold-flow properties of certain hydrocarbon products, increasing the degree of isomerization in a diesel product and/or increasing the production rate of a diesel product. The embodiments generally include reducing the residence time of lighter hydrocarbon fractions during hydrocracking, thereby decreasing secondary cracking, by various configurations of introducing at least two hydrocarbon feedstreams of different boiling ranges at different entry points in a hydrocracking unit. A method further includes forming a hydrocarbons stream comprising primarily C5+ Fischer-Tropsch hydrocarbon products; fractionating hydrocarbons stream to form at least a wax fraction and an intermediate fraction which serve as separate feedstreams to a hydrocracking unit comprising at least two hydroconversion zones.

Abstract: Embodiments include processes for producing streams containing organic molecules (for example, diesel fuels and diesel fuel blending agents) including ultra-low severity hydrotreatment of at least a portion of a hydrocarbon synthesis product stream. Also, streams containing organic molecules (for example, diesel fuels and diesel fuel blending agents) produced by the processes are described.

Abstract: Embodiments include processes for producing streams containing organic molecules (for example, diesel fuels and diesel fuel blending agents) including ultra-low severity hydrotreatment of at least a portion of a hydrocarbon synthesis product stream. Also, streams containing organic molecules (for example, diesel fuels and diesel fuel blending agents) produced by the processes are described.