Abstract: Processes, machines, and stored machine instructions are provided for approximating value densities in data. While generating a resulting density model to approximate value densities in a set of data, density modeling logic selects a functional component of a first model to vary based at least in part on how much the functional component contributes to how well the first model approximates the value densities. The density modeling logic then uses at least the functional component and a variation of the functional component as seed components to determine adjusted functional components of a second model by iteratively determining, in an expectation step, how much the seed components contribute to how well the second model explains the values, and, in a maximization step, new seed components, optionally to be used in further iterations, based at least in part on how much of the values are attributable to the seed components.

Abstract: Methods, machines, and stored instructions are provided for partitioning a graph of nodes into clusters of nodes by iteratively excluding edges in the graph. For each node of at least a subset of nodes in the graph, a graph partitioning module determines whether to exclude edges for the node and, if so, selects for exclusion edge(s) to at least a subset of the node's neighbor(s). The module selects edge(s) to the node's neighbor(s) for exclusion based at least in part on a degree of overlap between the node's neighbor(s) and neighbor(s) of the node's neighbor(s). For any subset(s) that are yet not sufficiently partitioned into clusters, the module repeats the step of determining whether to exclude edges and, if so, selecting nodes for exclusion, and determining whether or not the nodes are sufficiently partitioned. Subset(s) of nodes that are already sufficiently partitioned may be skipped during the repeated steps.

Abstract: Processes, machines, and stored machine instructions are provided for grouping interdependent fields. Field grouping logic may include specially configured machines and/or stored instructions that identify group(s) of interdependent fields of a data set. The field grouping logic may receive, from a client on a customizable interface, a request for interdependent fields in a data set and, in response, cause generation of an output object that identifies the similar fields in the data set. The field grouping logic may exclude field(s) of the data set that are not interdependent, are not frequently accessed, or do not consume much space in storage, even though the request may not identify which fields are interdependent. The output object identifies the similar fields in set(s) or list(s) of fields, or in a hierarchy or hierarchies of groups and sub-groups.

Abstract: Processes, machines, and stored machine instructions are provided for estimating how many rows would be processed by a query. In one embodiment, model aggregating logic may generate a global model based on separate models for separate portions of a set of data. For example, the separate portions may be assigned to separate partitions of the set of data based at least in part on key values in the separate portions. Generating the global model may include generating an uncompressed model that includes components of the separate models before generating a compressed model to estimate the uncompressed model. Query analysis logic may use the global model to estimate how many rows would be processed by queries that, when executed, would access portion(s) of the set of data.

Abstract: Methods, machines, and stored instructions are provided for partitioning a graph of nodes into clusters of nodes by iteratively excluding edges in the graph. For each node of at least a subset of nodes in the graph, a graph partitioning module determines whether to exclude edges for the node and, if so, selects for exclusion edge(s) to at least a subset of the node's neighbor(s). The module selects edge(s) to the node's neighbor(s) for exclusion based at least in part on a degree of overlap between the node's neighbor(s) and neighbor(s) of the node's neighbor(s). For any subset(s) that are yet not sufficiently partitioned into clusters, the module repeats the step of determining whether to exclude edges and, if so, selecting nodes for exclusion, and determining whether or not the nodes are sufficiently partitioned. Subset(s) of nodes that are already sufficiently partitioned may be skipped during the repeated steps.

Abstract: Processes, machines, and stored machine instructions are provided for estimating how many rows would be processed by a query. In one embodiment, model aggregating logic may generate a global model based on separate models for separate portions of a set of data. For example, the separate portions may be assigned to separate partitions of the set of data based at least in part on key values in the separate portions. Generating the global model may include generating an uncompressed model that includes components of the separate models before generating a compressed model to estimate the uncompressed model. Query analysis logic may use the global model to estimate how many rows would be processed by queries that, when executed, would access portion(s) of the set of data.

Abstract: Processes, machines, and stored machine instructions are provided for approximating value densities in data. While generating a resulting density model to approximate value densities in a set of data, density modeling logic selects a functional component of a first model to vary based at least in part on how much the functional component contributes to how well the first model approximates the value densities. The density modeling logic then uses at least the functional component and a variation of the functional component as seed components to determine adjusted functional components of a second model by iteratively determining, in an expectation step, how much the seed components contribute to how well the second model explains the values, and, in a maximization step, new seed components, optionally to be used in further iterations, based at least in part on how much of the values are attributable to the seed components.

Abstract: Processes, machines, and stored machine instructions are provided for grouping interdependent fields. Field grouping logic may include specially configured machines and/or stored instructions that identify group(s) of interdependent fields of a data set. The field grouping logic may receive, from a client on a customizable interface, a request for interdependent fields in a data set and, in response, cause generation of an output object that identifies the similar fields in the data set. The field grouping logic may exclude field(s) of the data set that are not interdependent, are not frequently accessed, or do not consume much space in storage, even though the request may not identify which fields are interdependent. The output object identifies the similar fields in set(s) or list(s) of fields, or in a hierarchy or hierarchies of groups and sub-groups.