Abstract: Embodiments of the invention provide data management solutions that go beyond the traditional warehousing system to support advanced analytics. Furthermore, embodiments of the invention relate to systems and methods for extracting data from an existing data warehouse, storing the extracted data in a reusable (intermediate) form using data parallel and compute parallel techniques over cloud, query processing over the data with/without compute parallel techniques, and providing querying using high level querying languages.

Abstract: A workload specification, detailing specific queries and a frequency of execution of each of the queries, and a set of partitions, are obtained for the database, as inputs. A number of candidate tables are identified for the database, the tables having a plurality of attributes. A chosen attribute is allocated for each of the tables, to obtain a set of tables and a set of appropriate partitions for each of the tables.

Abstract: A workload specification is obtained for the database. Based on the workload specification, candidate ones of the tables are identified and ranked. Compression impact is evaluated for the candidate ones of the tables. A design for the database is developed, specifying at least one of: (i) which of the tables should be compressed, and (ii) which of the tables should not be compressed.

Abstract: A data query instruction for execution is received, wherein execution of the data query instruction is to cause a data transfer into or from a data warehouse. The data warehouse includes data servers that are communicatively coupled for data transfer through a link aggregation group having at least two data links. Responsive to receiving the data query instruction and prior to or at least partially overlapping with execution of the data query instruction to cause the data transfer, which of the number of data servers are to receive or transmit data as part of the data transfer is identified; for each of the identified data servers, a minimum number of the at least two data links that are needed for the data transfer are identified; and if inactive, the minimum number of the at least two data links for each of the identified data servers are activated.

Abstract: A method includes receiving a data query instruction for execution, wherein execution of the data query instruction is to cause a data transfer into or from a data warehouse. The data warehouse includes data servers that are communicatively coupled for data transfer through a link aggregation group having at least two data links. Responsive to receiving the data query instruction and prior to or at least partially overlapping with execution of the data query instruction to cause the data transfer, the method includes identifying which of the number of data servers are to receive or transmit data as part of the data transfer; identifying, for each of the identified data servers, a minimum number of the at least two data links that are needed for the data transfer; and activating, if inactive, the minimum number of the at least two data links for each of the identified data servers.

Abstract: To predict edges in a non-cumulative graph, based on a sequence of non-cumulative previous occurrences of the graph, the sequence of non-cumulative previous occurrences of the graph is obtained, and topological properties of each of the previous occurrences of the graph are extracted. Topological similarities between each of the previous occurrences of the graph are computed based on the topological properties. The edges of the graph are predicted by computing a score for each possible edge of the graph, based upon the topological similarities.

Abstract: A method includes receiving a data query instruction for execution, wherein execution of the data query instruction is to cause a data transfer into or from a data warehouse. The data warehouse includes data servers that are communicatively coupled for data transfer through a link aggregation group having at least two data links. Responsive to receiving the data query instruction and prior to or at least partially overlapping with execution of the data query instruction to cause the data transfer, the method includes identifying which of the number of data servers are to receive or transmit data as part of the data transfer; identifying, for each of the identified data servers, a minimum number of the at least two data links that are needed for the data transfer; and activating, if inactive, the minimum number of the at least two data links for each of the identified data servers.

Abstract: A data query instruction for execution is received, wherein execution of the data query instruction is to cause a data transfer into or from a data warehouse. The data warehouse includes data servers that are communicatively coupled for data transfer through a link aggregation group having at least two data links. Responsive to receiving the data query instruction and prior to or at least partially overlapping with execution of the data query instruction to cause the data transfer, which of the number of data servers are to receive or transmit data as part of the data transfer is identified; for each of the identified data servers, a minimum number of the at least two data links that are needed for the data transfer are identified; and if inactive, the minimum number of the at least two data links for each of the identified data servers are activated.

Abstract: To predict edges in a non-cumulative graph, based on a sequence of non-cumulative previous occurrences of the graph, the sequence of non-cumulative previous occurrences of the graph is obtained, and topological properties of each of the previous occurrences of the graph are extracted. Topological similarities between each of the previous occurrences of the graph are computed based on the topological properties. The edges of the graph are predicted by computing a score for each possible edge of the graph, based upon the topological similarities.

Abstract: The invention relates to ranking Service Level Agreement violations. A method for ranking said Service Level Agreements comprising determining a set of attributes for Service Level Agreements subject to violation, and predicting importance of Service Level Agreement violations using a model which performs ordinal regression based on said attributes of Service Level Agreements.

Abstract: Embodiments of the invention provide data management solutions that go beyond the traditional warehousing system to support advanced analytics. Furthermore, embodiments of the invention relate to systems and methods for extracting data from an existing data warehouse, storing the extracted data in a reusable (intermediate) form using data parallel and compute parallel techniques over cloud, query processing over the data with/without compute parallel techniques, and providing querying using high level querying languages.

Abstract: A method, system, and computer program product for a multi-resolution storage scheme for historical data are provided. The method includes dividing the historical data into a plurality of time frames and determining an amount of compression for each of the time frames, where at least two of the time frames have a different amount of compression. The method further includes applying the amount of compression to each of the time frames, resulting in multi-resolution historical data, and storing the multi-resolution historical data.

Abstract: A workload specification, detailing specific queries and a frequency of execution of each of the queries, and a set of partitions, are obtained for the database, as inputs. A number of candidate tables are identified for the database, the tables having a plurality of attributes. A chosen attribute is allocated for each of the tables, to obtain a set of tables and a set of appropriate partitions for each of the tables.

Abstract: A workload specification is obtained for the database. Based on the workload specification, candidate ones of the tables are identified and ranked. Compression impact is evaluated for the candidate ones of the tables. A design for the database is developed, specifying at least one of: (i) which of the tables should be compressed, and (ii) which of the tables should not be compressed.

Abstract: A workload specification is obtained for the database. Based on the workload specification, candidate ones of the tables are identified and ranked. Compression impact is evaluated for the candidate ones of the tables. A design for the database is developed, specifying at least one of: (i) which of the tables should be compressed, and (ii) which of the tables should not be compressed.

Abstract: A workload specification, detailing specific queries and a frequency of execution of each of the queries, and a set of partitions, are obtained for the database, as inputs. A number of candidate tables are identified for the database, the tables having a plurality of attributes. A chosen attribute is allocated for each of the tables, to obtain a set of tables and a set of appropriate partitions for each of the tables.

Abstract: A workload specification, detailing specific queries and a frequency of execution of each of the queries, and a set of partitions, are obtained for the database, as inputs. A number of candidate tables are identified for the database, the tables having a plurality of attributes. A chosen attribute is allocated for each of the tables, to obtain a set of tables and a set of appropriate partitions for each of the tables.

Abstract: To predict edges in a non-cumulative graph, based on a sequence of non-cumulative previous occurrences of the graph, the sequence of non-cumulative previous occurrences of the graph is obtained, and topological properties of each of the previous occurrences of the graph are extracted. Topological similarities between each of the previous occurrences of the graph are computed based on the topological properties. The edges of the graph are predicted by computing a score for each possible edge of the graph, based upon the topological similarities.

Abstract: To predict edges in a non-cumulative graph, based on a sequence of non-cumulative previous occurrences of the graph, the sequence of non-cumulative previous occurrences of the graph is obtained, and topological properties of each of the previous occurrences of the graph are extracted. Topological similarities between each of the previous occurrences of the graph are computed based on the topological properties. The edges of the graph are predicted by computing a score for each possible edge of the graph, based upon the topological similarities.

Abstract: The invention relates to ranking Service Level Agreement violations. A method for ranking said Service Level Agreements comprising determining a set of attributes for Service Level Agreements subject to violation, and predicting importance of Service Level Agreement violations using a model which performs ordinal regression based on said attributes of Service Level Agreements.