Abstract: A high-level object-oriented specification language allows configurable components of a cloud-based deployment to be modeled by a class definition that includes a group of configurable class parameters. The object-oriented specification language supports extension of an existing base class definition to create new class definitions, and supports inheritance of class parameters from the existing base class definition by the new class definitions. A cloud-based deployment can be customized based on class definitions used in configuring one or more generic deployments, such as by modifying class parameter values of the class definitions, varying interrelationships between the classes, and supplementing existing class parameters with new class parameters. The high-level object-oriented specification language supports class definitions that model hardware and virtual resources as well as software roles and service roles served by software applications and services in a cloud-based deployment.

Abstract: A high-level object-oriented specification language allows configurable components of a cloud-based deployment to be modeled by a class definition that includes a group of configurable class parameters. The object-oriented specification language supports extension of an existing base class definition to create new class definitions, and supports inheritance of class parameters from the existing base class definition by the new class definitions. A cloud-based deployment can be customized based on class definitions used in configuring one or more generic deployments, such as by modifying class parameter values of the class definitions, varying interrelationships between the classes, and supplementing existing class parameters with new class parameters. The high-level object-oriented specification language supports class definitions that model hardware and virtual resources as well as software roles and service roles served by software applications and services in a cloud-based deployment.

Abstract: Methods and systems for segmenting printed media pages into individual articles quickly and efficiently. A printed media based image that may include a variety of columns, headlines, images, and text is input into the system which comprises a block segmenter and a article segmenter system. The block segmenter identifies and produces blocks of textual content from a printed media image while the article segmenter system determines which blocks of textual content belong to one or more articles in the printed media image based on a classifier algorithm. A method for segmenting printed media pages into individual articles is also presented.

Abstract: Data can be protected at a production server in a virtually continuous fashion, without necessarily imposing severe constraints on the source application(s). For example, a production server can create an application-consistent backup of one or more volumes, the backups corresponding to a first instance in time. A volume filter driver can monitor data changes in each volume using an in-memory bitmap, while a log file and/or update sequence number journal can keep track of which files have been added to or updated. The volume updates are also consistent for an instance (later) in time. At the next replication cycle, such as every few minutes (however configured), the volume filter driver passes each in-memory bitmap to the physical disk on the production server. The production server then sends the updates to the backup server, which thus stores application-consistent backups for the volume for multiple instances of time.

Abstract: Data can be protected at a production server in a virtually continuous fashion, without necessarily imposing severe constraints on the source application(s). For example, a production server can create an application-consistent backup of one or more volumes, the backups corresponding to a first instance in time. A volume filter driver can monitor data changes in each volume using an in-memory bitmap, while a log file and/or update sequence number journal can keep track of which files have been added to or updated. The volume updates are also consistent for an instance (later) in time. At the next replication cycle, such as every few minutes (however configured), the volume filter driver passes each in-memory bitmap to the physical disk on the production server. The production server then sends the updates to the backup server, which thus stores application-consistent backups for the volume for multiple instances of time.