Abstract: A computer-implemented method for managing links to resources in a content management system utilizes a link analysis function, the link analysis function being adapted for receiving a link to a target resource; determining a content model of the target resource based at least on content of the target resource; and storing the content model. The method comprises generating a first target model of a first target resource by executing the link analysis function with a first link comprised by the content management system; monitoring the first target resource by generating a temporary first target model of the first target resource by repeating the execution of the link analysis function with the first link; and in response to detecting a difference between the first target model and the temporary first target model, triggering an automatic response action, which comprises storing a change record comprising information indicative of the detected difference.

Abstract: The present disclosure relates to a method for processing data in a database engine. The method includes storing a first instance of a first table in the database engine in plaintext. Encryption information about encryption methods and encryption keys of at least one predefined column of the first table may be maintained. In response to data changes in the column, first log entries for the data changes may be written in plaintext. The data changes may be encrypted by an encryption component using the encryption information. This results in encrypted data changes. Second log entries for the encrypted data changes may be written and applied to a second instance of the first table. The second instance of the first table is stored at a further database engine.

Abstract: The present disclosure relates to a method for processing queries in a database system having a first database engine and a second database engine. The method includes: storing a first instance of a first table in the first database engine in plaintext; encrypting at least one predefined column of the first table, resulting in a second instance of the first table containing at least part of the data of the first table in encrypted format. The second instance of the first table in the second database engine is stored in the second database engine. It may be determined whether to execute a received query in the first database engine on the first table or in the second database engine on the second instance of the first table, where the determination involves a comparison of the query with encryption information.

Abstract: Aspects of the invention include processing queries in a database system having a first database engine and a second database engine. A first instance of a first table is stored in the first database engine in plaintext. At one predefined column of the first table is encrypted, resulting in a set of encrypted columns, using a cascade encryption scheme that includes a set of ordered encryption methods. A subset of the set of columns is stored in a second instance of the first table in the second database engine. An encryption method of the cascade encryption scheme that allows a query operation of a received query on data encrypted with the identified encryption method is identified. The identified encryption method is used to rewrite the query, and the rewritten query is executed using the second instance of the first table.

Abstract: Aspects of the invention include processing queries in a database system having a first database engine and a second database engine. A first instance of a first table is stored in the first database engine in plaintext. At one predefined column of the first table is encrypted, resulting in a set of encrypted columns, using a cascade encryption scheme that includes a set of ordered encryption methods. A subset of the set of columns is stored in a second instance of the first table in the second database engine. An encryption method of the cascade encryption scheme that allows a query operation of a received query on data encrypted with the identified encryption method is identified. The identified encryption method is used to rewrite the query, and the rewritten query is executed using the second instance of the first table.

Abstract: The present disclosure relates to a method for processing queries in a database system having a first database engine and a second database engine. The method includes: storing a first instance of a first table in the first database engine in plaintext; encrypting at least one predefined column of the first table, resulting in a second instance of the first table containing at least part of the data of the first table in encrypted format. The second instance of the first table in the second database engine is stored in the second database engine. It may be determined whether to execute a received query in the first database engine on the first table or in the second database engine on the second instance of the first table, where the determination involves a comparison of the query with encryption information.

Abstract: The present disclosure relates to a method for processing data in a database engine. The method includes storing a first instance of a first table in the database engine in plaintext. Encryption information about encryption methods and encryption keys of at least one predefined column of the first table may be maintained. In response to data changes in the column, first log entries for the data changes may be written in plaintext. The data changes may be encrypted by an encryption component using the encryption information. This results in encrypted data changes. Second log entries for the encrypted data changes may be written and applied to a second instance of the first table. The second instance of the first table is stored at a further database engine.

Abstract: A computer-implemented method, a system, and a computer program product for sorting a data table by an attribute of the data table is provided. Each data block of the data table is provided with attribute value information being indicative of distinct values and/or ranges of values of the attribute in each of the data blocks of the data table. Distinct ranges and/or distinct values of the attribute of the data table are derived from the attribute value information. For each determined distinct range and/or distinct value, a bucket may be created. For each created bucket, it may be determined, using the attribute value information, which data block of the data table is to be scanned. Each scanned record is distributed to a corresponding bucket. The entries or records in each bucket having more than one record, may be sorted by the attribute.

Abstract: The present disclosure relates to a method for enforcing constraints on data in a data processing system. The method comprises providing a set of constraints on the data. A first data update request may be received at the transactional engine and executes on the first dataset. A second data update request associated with the received data update request is determined and sent by the transactional engine to the analytical engine. The analytical engine executes the second data, resulting in a set of changes in the second dataset. The transactional engine commits the update of the first dataset before or after receiving the results of the checking of the set of constraints. The update on the first dataset is aborted by the transactional engine in response to receiving the results of the checking of the set of constraints, wherein the results indicate that the set of constraints are not met.

Abstract: A method for enforcing constraints on a database table. The method includes: storing the database table on an analytical engine of a data processing system. An index is created on the database table on a transactional engine of the data processing system, wherein the database table stored on the analytical engine is scanned for creating the index. A data manipulation statement associated with a set of constraints on the database table is provided. The transactional engine enforces the set of constraints based on the index. In response to the set of constraints being successfully enforced: executing the data manipulation statement on the database table in the analytical engine, and updating the index in the transactional engine accordingly to the data manipulation statement.

Abstract: Data stored in a column-oriented manner is encoded using a data mining algorithm for finding column patterns among a set of data tuples, where each data tuple contains a set of columns, and the data mining algorithm treats all columns and all column combinations and column ordering similarly or in the same manner when looking for column patterns. Column values are ordered occurring in the column patterns based on their frequencies into a prefix tree, where the prefix tree defines a pattern order. The data tuples are sorted according to the pattern order, resulting in sorted data tuples, and columns of the sorted data tuples are encoded using run-length encoding.

Abstract: Embodiments relate to data compression using dictionary encoding. An aspect includes subdividing a table of uncompressed data into a first block and a second block of complete rows. Another aspect includes determining information about a frequency of occurrence of different values for each column of the first block. Another aspect includes selecting a row of the first block to be removed out of the first block using frequency of occurrence-information. Another aspect includes removing the a row out of the first block to form an updated first block and determining information about a frequency of occurrence of different values for each column of the updated first block. Another aspect includes deriving a dictionary containing code-words for encoding the values of the updated first block. Another aspect includes encoding the values of the updated first block based on the code-words. Another aspect includes adding the removed row to the second block.

Abstract: Embodiments relate to data compression using dictionary encoding. An aspect includes subdividing a table of uncompressed data into a first block and a second block of complete rows. Another aspect includes determining information about a frequency of occurrence of different values for each column of the first block. Another aspect includes selecting a row of the first block to be removed out of the first block using frequency of occurrence-information. Another aspect includes removing the a row out of the first block to form an updated first block and determining information about a frequency of occurrence of different values for each column of the updated first block. Another aspect includes deriving a dictionary containing code-words for encoding the values of the updated first block. Another aspect includes encoding the values of the updated first block based on the code-words. Another aspect includes adding the removed row to the second block.

Abstract: A first epoch column pair includes a first global identification (ID) having first maximum value. A second epoch column pair includes a second global identification having second maximum value. The first epoch column pair receives first snapshots, and the first global ID increases with each of the first snapshots. When first global ID reaches first maximum value minus 1, switch to the second epoch column pair. The second epoch column pair receives second snapshots, and second global ID increases with each of the second snapshots. First global ID and first epoch column pair are reset, based on conditions. When second global ID reaches first maximum value minus 1, switch back to first epoch column pair. The first epoch column pair again receives first snapshots, and the first global ID increases with each of the second snapshots. Second global ID and second epoch column pair are reset, based on conditions.

Abstract: Data stored in a column-oriented manner is encoded using a data mining algorithm for finding column patterns among a set of data tuples, where each data tuple contains a set of columns, and the data mining algorithm treats all columns and all column combinations and column ordering similarly or in the same manner when looking for column patterns. Column values are ordered occurring in the column patterns based on their frequencies into a prefix tree, where the prefix tree defines a pattern order. The data tuples are sorted according to the pattern order, resulting in sorted data tuples, and columns of the sorted data tuples are encoded using run-length encoding.

Abstract: A first epoch column pair includes a first global identification (ID) having first maximum value. A second epoch column pair includes a second global identification having second maximum value. The first epoch column pair receives first snapshots, and the first global ID increases with each of the first snapshots. When first global ID reaches first maximum value minus 1, switch to the second epoch column pair. The second epoch column pair receives second snapshots, and second global ID increases with each of the second snapshots. First global ID and first epoch column pair are reset, based on conditions. When second global ID reaches first maximum value minus 1, switch back to first epoch column pair. The first epoch column pair again receives first snapshots, and the first global ID increases with each of the second snapshots. Second global ID and second epoch column pair are reset, based on conditions.