Abstract: An improved system and method for a distributed column chunk data store is provided. A distributed column chunk data store may be provided by multiple storage servers operably coupled to a network. A storage server may include a database engine for partitioning a data table into the column chunks for distributing across multiple storage servers, a storage shared memory for storing the column chunks during processing of semantic operations performed on the column chunks, and a storage services manager for striping column chunks of a partitioned data table across multiple storage servers. Any data table may be flexibly partitioned into column chunks using one or more columns as a key with various partitioning methods. There may also be a storage policy for specifying how to partition a data table for distributing column chunks across multiple servers and for specifying a level of redundancy for recovery from failure of storage servers.

Abstract: An improved system and method for compression in a distributed column chunk data store is provided. A distributed column chunk data store may be provided by multiple storage servers operably coupled to a network. A storage server provided may include a database engine for partitioning a data table into the column chunks for distributing across multiple storage servers, a storage shared memory for storing the column chunks during processing of semantic operations performed on the column chunks, and a storage services manager for striping column chunks of a partitioned data table across multiple storage servers. Any data table may be flexibly partitioned into column chunks using one or more columns with various partitioning methods. Domain specific compression may be applied to a column chunk to reduce storage requirements of column chunks and increase transmission speeds for sending column chunks between storage servers.

Abstract: An improved system and method for a distributed column chunk data store is provided. A distributed column chunk data store may be provided by multiple storage servers operably coupled to a network. A storage server may include a database engine for partitioning a data table into the column chunks for distributing across multiple storage servers, a storage shared memory for storing the column chunks during processing of semantic operations performed on the column chunks, and a storage services manager for striping column chunks of a partitioned data table across multiple storage servers. Any data table may be flexibly partitioned into column chunks using one or more columns as a key with various partitioning methods. There may also be a storage policy for specifying how to partition a data table for distributing column chunks across multiple servers and for specifying a level of redundancy for recovery from failure of storage servers.

Abstract: A facility for identifying groups of items that co-occur in more than a threshold number of instances is described. Each such group of items has a size reflecting the number of items in the group. The facility uses a data structure comprising, for each of a plurality of group sizes, a single map identifying groups of that group size that co-occur in more than a threshold number of instances.

Abstract: A method, system and computer-readable medium for analyzing interaction or usage data, such as for customers, is described. The interaction or usage data may be stored in log files and supplemented with data from other sources. Various data parsing information may be defined and used as part of the analysis, such as by using customer-specific information to identify various occurrences of interest. For example, when analyzing a customer's web site interaction data, the parser component can use data defining customer-specific types of web site events of interest. Such high-level types of occurrences can be specified in a variety of ways, such as by using a combination of a logical web site, one or more URIs corresponding to web pages, and/or one or more query strings. The data parsing information may also specify a mapping of actual web sites to one or more logical sites.

Abstract: A method, system and computer-readable medium for analyzing interaction or usage data, such as for customers, is described. Various data parsing information may be defined and used as part of the analysis, such as by using customer-specific information to identify various occurrences of interest. For example, the parser component can use data defining customer-specific categories of content set items and customer-specific types of events of interest. Such high-level types of occurrences can be specified in a variety of ways, such as by using a combination of a logical web site, one or more URIs corresponding to web pages, and/or one or more query strings. In addition, in order to associate the appropriate data parsing information with data to be processed, the data parsing information can also include version information that specifies when it is applicable. The data parsing information may also map actual web sites to logical sites.

Abstract: A method, system and computer-readable medium for analyzing interaction or usage data, such as for customers, is described. Various data parsing information may be defined and used as part of the analysis, such as by using customer-specific information to identify various occurrences of interest. For example, the parser component can use data defining customer-specific categories of content set items and customer-specific types of events of interest. Such high-level types of occurrences can be specified in a variety of ways, such as by using a combination of a logical web site, one or more URIs corresponding to web pages, and/or one or more query strings. In addition, in order to associate the appropriate data parsing information with data to be processed, the data parsing information can also include version information that specifies when it is applicable. The data parsing information may also map actual web sites to logical sites.

Abstract: A method and system for importing data into a data store in accordance with metadata. The import system provides metadata that specifies how the import data for various types of import sources is to be imported into the data store. The import sources may be categorized according to the type of data provided by the import sources. When the import system receives the import data from the import source, it identifies the type of import source and retrieves the metadata defined for that type of import source. The import system then imports the received import data into the data store in accordance with the retrieved metadata.

Abstract: A method, system and computer-readable medium for analyzing interaction or usage data, such as for customers, is described. The interaction or usage data may be stored in log files and supplemented with data from other sources. Various data parsing information may be defined and used as part of the analysis, such as by using customer-specific information to identify various occurrences of interest. For example, when analyzing a customer's web site interaction data, the parser component can use data defining customer-specific categories of web pages. Such high-level types of occurrences can be specified in a variety of ways, such as by using a combination of a logical web site, one or more URIs corresponding to web pages, and/or one or more query strings. The data parsing information may also specify a mapping of actual web sites to one or more logical sites.

Abstract: A method and system for providing customers with access to and analysis of event data is provided. The event data may be stored in log files and supplemented with data from other sources, such as product databases and customer invoices. A data warehouse system collects customer data from the customer web sites and stores the data at a data warehouse server. The data warehouse server interacts with the customer servers to collect to the customer data on a periodic basis. The data warehouse server may provide instructions to the customer servers identifying the customer data that is to be uploaded to the data warehouse server. When the data warehouse server receives customer data, it converts the customer data into a format that is more conducive to processing by decision support system applications by which customers can analyze their data.

Abstract: A method and system for providing customers with access to and analysis of event data is provided. The event data may be stored in log files and supplemented with data from other sources, such as product databases and customer invoices. A data warehouse system collects customer data from the customer web sites and stores the data at a data warehouse server. The data warehouse server interacts with the customer servers to collect to the customer data on a periodic basis. The data warehouse server may provide instructions to the customer servers identifying the customer data that is to be uploaded to the data warehouse server. When the data warehouse server receives customer data, it converts the customer data into a format that is more conducive to processing by decision support system applications by which customers can analyze their data.

Abstract: A method and system for providing customers with access to and analysis of the navigation data collected at customer web sites is provided. In one embodiment, a data warehouse system collects customer data from the customer web sites and stores the data at a data warehouse server. The customer data may include navigation data (e.g., click stream log files), user attribute data of users of the customer web site (e.g., name, age, and gender), product data (e.g., catalog of products offered for sale by the customer), shopping cart data (i.e., identification of the products currently in a user's shopping call), and so on. When the data warehouse server receives customer data, it converts the customer data into a format that is more conducive to processing by decision support system applications by which customers can analyze their data. For example, the data warehouse server may analyze low-level navigation events (e.g.

Abstract: A method and system for providing customers with access to and analysis of the navigation data collected at customer web sites is provided. In one embodiment, a data warehouse system collects customer data from the customer web sites and stores the data at a data warehouse server. The customer data may include navigation data (e.g., click stream log files), user attribute data of users of the customer web site (e.g., name, age, and gender), product data (e.g., catalog of products offered for sale by the customer), shopping cart data (i.e., identification of the products currently in a user's shopping cart), and so on. When the data warehouse server receives customer data, it converts the customer data into a format that is more conducive to processing by decision support system applications by which customers can analyze their data. For example, the data warehouse server may analyze low-level navigation events (e.g.

Abstract: A method and system for providing customers with access to and analysis of the navigation data collected at customer web sites is provided. In one embodiment, a data warehouse system collects customer data from the customer web sites and stores the data at a data warehouse server. The customer data may include navigation data (e.g., click stream log files), user attribute data of users of the customer web site (e.g., name, age, and gender), product data (e.g., catalog of products offered for sale by the customer), shopping call data (i.e., identification of the products currently in a user's shopping call), and so on. When the data warehouse server receives customer data, it converts the customer data into a format that is more conducive to processing by decision support system applications by which customers can analyze their data. For example, the data warehouse server may analyze low-level navigation events (e.g.

Abstract: A method and system for providing customers with access to and analysis of the navigation data collected at customer web sites is provided. In one embodiment, a data warehouse system collects customer data from the customer web sites and stores the data at a data warehouse server. The customer data may include navigation data (e.g., click stream log files), user attribute data of users of the customer web site (e.g., name, age, and gender), product data (e.g., catalog of products offered for sale by the customer), shopping cart data (i.e., identification of the products currently in a user's shopping cart), and so on. When the data warehouse server receives customer data, it converts the customer data into a format that is more conducive to processing by decision support system applications by which customers can analyze their data. For example, the data warehouse server may analyze low-level navigation events (e.g.

Abstract: An architecture for managing query friendly hierarchical values contains a data structure having node value entries for node values that make up the hierarchical values, hierarchical value entries for the hierarchical values expressed in terms of node value identifiers found in the node value entries, and hierarchy parent entries for parent-child pairs of hierarchy values. A node value entry contains a node value, a node hash value generated from the node value by a first hashing algorithm, and the node value identifier. The node hash value defines the node value entry in which the corresponding node value is stored. The hierarchical value entry contains a hierarchical value represented by the node value identifiers that correspond to the node values that make up the hierarchical value. The hierarchical value entry also contains a hierarchical value hash value derived from the node value identifier representation of the hierarchical value using a second hashing algorithm and a hierarchical value identifier.