Abstract: A system is disclosed. The system includes a processor configured to: receive a set of data structured in a schemaless data representation; automatically translate the set of data into a relational representation by: translating an array map value in the set of data into an ordered multi-map; and converting the ordered multi-map to the relational representation. The processor is further configured to store the translated set of data in a key-value data store for a query-based retrieval.

Abstract: An application network is monitored using a plurality of agents. Adaptive event aggregation is performed to determine retaining values for an aggregation dimension. A report of the application network is generated based on the aggregation dimension.

Abstract: A data object is received for storage in a key-value store. A partitioning token prefix is generated for the data object. A logical key for the data object is determined. A partitioning key is generated based at least in part on combining the partitioning token prefix and the logical key. Data associated with the data object is stored in the key-value store based on the partitioning key.

Abstract: A system is disclosed. The system includes a processor configured to: receive a set of data structured in a schemaless data representation; automatically translate the set of data into a relational representation by: translating an array map value in the set of data into an ordered multi-map; and converting the ordered multi-map to the relational representation. The processor is further configured to store the translated set of data in a key-value data store for a query-based retrieval.

Abstract: An application network is monitored using a plurality of agents. Adaptive event aggregation is performed to determine retaining values for an aggregation dimension. A report of the application network is generated based on the aggregation dimension.

Abstract: A set of data is received that includes a plurality of event data objects or a plurality of meta data objects, wherein a structure of the set of data is a schemaless data representation. The set of data is automatically translated into a relational representation. The translated set of data is stored in a key-value data store for a query-based retrieval.

Abstract: An application network is monitored using a plurality of agents. Adaptive event aggregation is performed to determine retaining values for an aggregation dimension. A report of the application network is generated based on the aggregation dimension.

Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Abstract: An application network is monitored using a plurality of agents. Adaptive event aggregation is performed to determine retaining values for an aggregation dimension. A report of the application network is generated based on the aggregation dimension.

Abstract: A method for processing a search query according to one embodiment includes receiving a search query containing terms; combining at least some consecutive terms in the search query to create biwords; looking up at least some of the terms and biwords in a search index for identifying sections of documents containing the at least some of the terms and/or biwords; generating a content score for each of the identified sections based at least in part on a number of the terms and biwords found in the sections of each document, wherein the biwords are given a higher priority than matched terms, wherein the priority affects the content score; and selecting and outputting an indicator of at least one of the sections, or portion thereof, based at least in part on the content score.

Abstract: A method for processing a search query according to one embodiment includes receiving a search query containing terms; combining at least some consecutive terms in the search query to create biwords; looking up at least some of the terms and biwords in a search index for identifying sections of documents containing the at least some of the terms and/or biwords; generating a content score for each of the identified sections based at least in part on a number of the terms and biwords found in the sections of each document, wherein the biwords are given a higher priority than matched terms, wherein the priority affects the content score; and selecting and outputting an indicator of at least one of the sections, or portion thereof, based at least in part on the content score.

Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Abstract: Systems and methods for programmatically causing a machine to classify and extract the meaning of text are discussed herein. Some embodiments may provide for a system including circuitry configured to identify topics associated with a block of text and one or more categories for each of the topics. Each unique category across the one or more categories may be further associated with one or more levels of parent and/or child categories to form an expanded category set of category nodes having parent-child relationships. Based on a number of category nodes connected to each unique category, the circuitry may be configured to determine one or more filtered categories from the unique categories and one or more filtered topics. Filtered topics or categories may be used to programmatically classify text with a more relevant data set than may be possible without the filtering.

Abstract: A method for processing a search query according to one embodiment includes receiving a search query containing terms; combining at least some consecutive terms in the search query to create biwords; looking up at least some of the terms and biwords in a search index for identifying sections of documents containing the at least some of the terms and/or biwords; generating a content score for each of the identified sections based at least in part on a number of the terms and biwords found in the sections of each document, wherein the biwords are given a higher priority than matched terms, wherein the priority affects the content score; and selecting and outputting an indicator of at least one of the sections, or portion thereof, based at least in part on the content score.

Abstract: A method for processing a search query according to one embodiment includes receiving a search query containing terms; combining at least some consecutive terms in the search query to create biwords; looking up at least some of the terms and biwords in a search index for identifying sections of documents containing the at least some of the terms and/or biwords; generating a content score for each of the identified sections based at least in part on a number of the terms and biwords found in the sections of each document, wherein the biwords are given a higher priority than matched terms, wherein the priority affects the content score; and selecting and outputting an indicator of at least one of the sections, or portion thereof, based at least in part on the content score.

Abstract: Systems and methods for programmatically causing a machine to classify and extract the meaning of text are discussed herein. Some embodiments may provide for a system including circuitry configured to identify topics associated with a block of text and one or more categories for each of the topics. Each unique category across the one or more categories may be further associated with one or more levels of parent and/or child categories to form an expanded category set of category nodes having parent-child relationships. Based on a number of category nodes connected to each unique category, the circuitry may be configured to determine one or more filtered categories from the unique categories and one or more filtered topics. Filtered topics or categories may be used to programmatically classify text with a more relevant data set than may be possible without the filtering.

Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Abstract: A method for processing a search query according to one embodiment includes receiving a search query containing terms; combining at least some consecutive terms in the search query to create biwords; looking up at least some of the terms and biwords in a search index for identifying sections of documents containing the at least some of the terms and/or biwords; generating a content score for each of the identified sections based at least in part on a number of the terms and biwords found in the sections of each document, wherein the biwords are given a higher priority than matched terms, wherein the priority affects the content score; and selecting and outputting an indicator of at least one of the sections, or portion thereof, based at least in part on the content score.

Abstract: A method for processing a search query according to one embodiment includes receiving a search query containing terms; combining at least some consecutive terms in the search query to create biwords; looking up at least some of the terms and biwords in a search index for identifying sections of documents containing the at least some of the terms and/or biwords; generating a content score for each of the identified sections based at least in part on a number of the terms and biwords found in the sections of each document, wherein the biwords are given a higher priority than matched terms, wherein the priority affects the content score; and selecting and outputting an indicator of at least one of the sections, or portion thereof, based at least in part on the content score.