Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Abstract: 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.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: 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.

Abstract: 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.

Abstract: A system and method for processing data wherein one or more user selections of source data and an input defining one or more operations to be performed on the selected source data are received to generate processed data for display as a chart; the source data is retrieved from at least one data source, the source data is processed according to the defined one or more operations to generate processed data for output for display as a chart, the chart is stored as data defining the one or more operations and data identifying the source data operated on, a further user selection is received to redisplay the chart; retrieving the source data from the at least one data source; and the source data is processed according to the defined one or more operations to generate the processed data for output for redisplay as the chart.

Abstract: 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.