Abstract: In an embodiment, a method comprises dividing collected data into data clusters based on proximity of the data and adjusting the clusters based on density of data in individual clusters. Based on first data points in a first cluster, a first average point in the first cluster is determined. Based on second data points in a second cluster, a second average point in the second cluster is determined. Aggregate data, comprising the first average point and the second average point, are stored in storage. Upon receiving a request to provide data for a particular coordinate, the reconstructed data point is determined by interpolating between the first average point and the second average point at the particular coordinate. Accordingly, aggregated data may be stored and when a request specifies data that was not actually stored, a reconstructed data point with an approximated data value may be provided as a substitute.

Abstract: In an embodiment, a method comprises dividing collected data into data clusters based on proximity of the data and adjusting the clusters based on density of data in individual clusters. Based on first data points in a first cluster, a first average point in the first cluster is determined. Based on second data points in a second cluster, a second average point in the second cluster is determined. Aggregate data, comprising the first average point and the second average point, are stored in storage. Upon receiving a request to provide data for a particular coordinate, the reconstructed data point is determined by interpolating between the first average point and the second average point at the particular coordinate. Accordingly, aggregated data may be stored and when a request specifies data that was not actually stored, a reconstructed data point with an approximated data value may be provided as a substitute.

Abstract: In an embodiment, a method comprises dividing collected data into data clusters based on proximity of the data and adjusting the clusters based on density of data in individual clusters. Based on first data points in a first cluster, a first average point in the first cluster is determined. Based on second data points in a second cluster, a second average point in the second cluster is determined. Aggregate data, comprising the first average point and the second average point, are stored in storage. Upon receiving a request to provide data for a particular coordinate, the reconstructed data point is determined by interpolating between the first average point and the second average point at the particular coordinate. Accordingly, aggregated data may be stored and when a request specifies data that was not actually stored, a reconstructed data point with an approximated data value may be provided as a substitute.

Abstract: Techniques for concurrent access to a resource object, such as a database object, include generating a lock data structure for a particular resource object. The lock data structure includes data values for a resource object identification, a lock type, and a version number. The version number is related to a number of changes to the resource object since the lock data structure was generated. By carrying a lock version number in a lock data structure managed by a lock manager, improved optimistic locking is provided in a database. In particular, the approach enables introduction of optimistic locking to a legacy database without requiring burdensome changes to a database table schema.

Abstract: In an embodiment, a method comprises dividing collected data into data clusters based on proximity of the data and adjusting the clusters based on density of data in individual clusters. Based on first data points in a first cluster, a first average point in the first cluster is determined. Based on second data points in a second cluster, a second average point in the second cluster is determined. Aggregate data, comprising the first average point and the second average point, are stored in storage. Upon receiving a request to provide data for a particular coordinate, the reconstructed data point is determined by interpolating between the first average point and the second average point at the particular coordinate. Accordingly, aggregated data may be stored and when a request specifies data that was not actually stored, a reconstructed data point with an approximated data value may be provided as a substitute.

Abstract: In an embodiment, a method comprises dividing collected data into data clusters based on proximity of the data and adjusting the clusters based on density of data in individual clusters. Based on first data points in a first cluster, a first average point in the first cluster is determined. Based on second data points in a second cluster, a second average point in the second cluster is determined. Aggregate data, comprising the first average point and the second average point, are stored in storage. Upon receiving a request to provide data for a particular coordinate, the reconstructed data point is determined by interpolating between the first average point and the second average point at the particular coordinate. Accordingly, aggregated data may be stored and when a request specifies data that was not actually stored, a reconstructed data point with an approximated data value may be provided as a substitute.

Abstract: In an embodiment, a method comprises dividing collected data into data clusters based on proximity of the data and adjusting the clusters based on density of data in individual clusters. Based on first data points in a first cluster, a first average point in the first cluster is determined. Based on second data points in a second cluster, a second average point in the second cluster is determined. Aggregate data, comprising the first average point and the second average point, are stored in storage. Upon receiving a request to provide data for a particular coordinate, the reconstructed data point is determined by interpolating between the first average point and the second average point at the particular coordinate. Accordingly, aggregated data may be stored and when a request specifies data that was not actually stored, a reconstructed data point with an approximated data value may be provided as a substitute.

Abstract: A method of controlling concurrent users of a distributed resource on a network is disclosed. In one aspect, there are one or more local lock managers executing on corresponding hosts and cooperating as a distributed lock manager. The resource is limited to a maximum number of concurrent users. A user identification for each user is associated with one host. In response to a request associated with a particular user associated with a first host, a lock is requested from a first local lock manager process executing on the first host. A related method of handling a request for a count-limited resource includes receiving a request from a client process for the computer resource. If it is determined that the request exceeds a maximum count for the resource, then it is determined whether a current time is within a retry time period of the client's first request.

Abstract: In one embodiment, a method for persistent data authorization includes receiving a query at a management device and identifying authorization constraints at the management device utilizing an authorization model. The authorization model includes at least one group hierarchy defining authorization based on a relationship between levels in the hierarchy. The query is modified based on the authorization constraints and the modified query is sent to the database. Data is received in response to the modified query and is configured for transmission from the management device without further filtering. The query received at the management device and the modified query are both configured for communication directly with the database with no modification to the database required for the modified query. A system and apparatus for persistent data authorization are also disclosed.

Abstract: A method of controlling concurrent users of a distributed resource on a network is disclosed. In one aspect, there are one or more local lock managers executing on corresponding hosts and cooperating as a distributed lock manager. The resource is limited to a maximum number of concurrent users. A user identification for each user is associated with one host. In response to a request associated with a particular user associated with a first host, a lock is requested from a first local lock manager process executing on the first host. A related method of handling a request for a count-limited resource includes receiving a request from a client process for the computer resource. If it is determined that the request exceeds a maximum count for the resource, then it is determined whether a current time is within a retry time period of the client's first request.

Abstract: In one embodiment, a method for persistent data authorization includes receiving a query at a management device and identifying authorization constraints at the management device utilizing an authorization model. The authorization model includes at least one group hierarchy defining authorization based on a relationship between levels in the hierarchy. The query is modified based on the authorization constraints and the modified query is sent to the database. Data is received in response to the modified query and is configured for transmission from the management device without further filtering. The query received at the management device and the modified query are both configured for communication directly with the database with no modification to the database required for the modified query. A system and apparatus for persistent data authorization are also disclosed.

Abstract: A method of controlling concurrent users of a distributed resource on a network is disclosed. In one aspect, there are one or more local lock managers executing on corresponding hosts and cooperating as a distributed lock manager. The resource is limited to a maximum number of concurrent users. A user identification for each user is associated with one host. In response to a request associated with a particular user associated with a first host, a lock is requested from a first local lock manager process executing on the first host. A related method of handling a request for a count-limited resource includes receiving a request from a client process for the computer resource. If it is determined that the request exceeds a maximum count for the resource, then it is determined whether a current time is within a retry time period of the client's first request.

Abstract: Techniques for concurrent access to a resource object, such as a database object, include generating a lock data structure for a particular resource object. The lock data structure includes data values for a resource object identification, a lock type, and a version number. The version number is related to a number of changes to the resource object since the lock data structure was generated. By carrying a lock version number in a lock data structure managed by a lock manager, improved optimistic locking is provided in a database. In particular, the approach enables introduction of optimistic locking to a legacy database without requiring burdensome changes to a database table schema.

Abstract: Techniques for concurrent access to a resource object, such as a database, include generating a lock data structure for a particular resource object. The lock data structure includes data values for a resource object identification, a lock type, and a version number. The version number is related to a number of changes to the resource object since the lock data structure was generated. A request is received from a requesting process. The request is for a requested lock type for access to the particular resource object. It is determined whether to grant the request based on the requested lock type and the lock type in the lock data structure. By carrying a lock version number in a lock data structure managed by a lock manager, improved optimistic locking is provided in a database. In particular, the approach enables introduction of optimistic locking to a legacy database without requiring burdensome changes to a database table schema.