Abstract: A database management system (DBMS) run a host CPU and a hardware coprocessor accelerate traversal of a tree-type data structure by allocating reusable memory in cache to store portions of the tree-type data structure as the tree-type data structure is being requested by the host CPU. The hardware coprocessor manages the cached tree-type data structure in a manner that is transparent to the host CPU. A driver located at the host CPU or at a separate computing device can provide an interface between the host CPU and the hardware coprocessor, thus reducing communications between the host CPU and the hardware coprocessor.

Abstract: The techniques discussed herein facilitate the transmission, storage, and manipulation of data in an encrypted database management system (EDBMS). An untrusted machine is connected to a data store having encrypted records, a client machine that sends encrypted queries, and a trusted machine that receives and decrypts the encrypted records and encrypted queries. The trusted machine processes the query using semantically secure query operators to produce a query result. The trusted machine ensures the size of the query result conforms to an upper bound on the number or records in the query result and returns the query result.

Abstract: The techniques discussed herein facilitate the transmission, storage, and manipulation of data in an encrypted database management system (EDBMS). An untrusted machine is connected to a data store having encrypted records, a client machine that sends encrypted queries, and a trusted machine that receives and decrypts the encrypted records and encrypted queries. The trusted machine processes the query using semantically secure query operators to produce a query result. The trusted machine ensures the size of the query result conforms to an upper bound on the number or records in the query result and returns the query result.

Abstract: A database management system (DBMS) run a host CPU and a hardware coprocessor accelerate traversal of a tree-type data structure by allocating reusable memory in cache to store portions of the tree-type data structure as the tree-type data structure is being requested by the host CPU. The hardware coprocessor manages the cached tree-type data structure in a manner that is transparent to the host CPU. A driver located at the host CPU or at a separate computing device can provide an interface between the host CPU and the hardware coprocessor, thus reducing communications between the host CPU and the hardware coprocessor.

Abstract: The claimed subject matter provides a method and a system for the efficient indexing of error tolerant set containment. An exemplary method comprises obtaining a frequency threshold and a query set. All tokens or token sets within the query set are determined, and then all minimal infrequent tokens or all minimal infrequent tokens sets of data records are found and used to build an index. The minimal infrequent tokens or minimal infrequent tokensets are processed in a fixed order, and then a collection of signatures for each minimal infrequent token or token set is determined.

Abstract: The claimed subject matter provides a method for data generation. The method includes identifying a generative probability distribution based on one or more cardinality constraints for populating a database table. The method also includes selecting one or more values for a corresponding one or more attributes in the database table based on the generative probability distribution and the cardinality constraints. Additionally, the method includes generating a tuple for the database table. The tuple comprises the one or more values.

Abstract: The claimed subject matter provides a method and a system for the efficient indexing of error tolerant set containment. An exemplary method comprises obtaining a frequency threshold and a query set. All tokens or token sets within the query set are determined, and then all minimal infrequent tokens or all minimal infrequent tokens sets of data records are found and used to build an index. The minimal infrequent tokens or minimal infrequent tokensets are processed in a fixed order, and then a collection of signatures for each minimal infrequent token or token set is determined.

Abstract: A machine implemented system and method that efficiently facilitates and effectuates exact similarity joins between collections of sets. The system and method obtains a collection of sets and a threshold value from an interface, and based at least in part on an identifiable similarity, such as an overlap or intersection, between the collection of sets the analysis component generates and outputs a candidate pair that at least equals or exceeds the threshold value.

Abstract: The claimed subject matter relates to incorporating a skyline operator within a relational database engine, and more particularly to a database engine that utilizes novel techniques to determine the lowest cost of generating the skyline produced by the skyline operator. The database engine receives queries and associated preferences and, based on a cardinality estimate and a cost estimate, an appropriate skyline generating technique is utilized to produce a skyline representative of the received queries and its associated preferences.

Abstract: The subject disclosure pertains to a powerful and flexible framework for record matching. The framework facilitates design of a record matching query or package composed of a set of well-defined primitive operators (e.g., relational, data cleaning . . . ), which can ultimately be executed to match records. To assist design of such packages, a learning technique based on examples is provided. More specifically, a set of matching and non-matching record pairs can be input and employed to facilitate automatic package generation. A generated package can subsequently be transformed manually and/or automatically into a semantically equivalent form optimized for execution.

Abstract: A system that facilitates estimating functional relationships associated with one or more columns in a database comprises a sampling component that receives a random sample of records within the database. An estimate generator component calculates an estimate of strength of functional relationships based at least in part upon the received samples. For example, the estimate generator component can calculate an estimate of strength of a column as a key column based at least in part upon the received samples.

Abstract: The subject disclosure pertains to efficient computation of the difference between queries by exploiting commonality between them. A minimal difference query (MDQ) is generated that roughly corresponds to removal of as many joins as possible while still accurately representing the query difference. The minimal difference can be employed to further substantially the scope of view matching where a query is not wholly subsumed by a view. Additionally, the minimal difference query can be employed as an analytical tool in various contexts.

Abstract: Techniques for estimating the progress of database queries are described herein. In a first implementation, a respective lower-bound parameter is associated with each node in an operator tree that representing a given database query, and the progress of the database query at a given point is estimated based upon the lower-bound parameters. In a second implementation, the progress of the query is estimated by associating respective lower-bound and upper-bound parameters with each node in the operator tree. The progress of the query at the given point is then estimated based on the lower-bound and upper-bound parameters.

Abstract: A set similarity join system and method are provided. The system can be employed to facilitate data cleaning based on similarities through the identification of “close” tuples (e.g., records and/or rows). “Closeness” can be is evaluated using a similarity function(s) chosen to suit the domain and/or application. Thus, the system facilitates generic domain-independent data cleansing. The system can be employed with a foundational primitive, the set similarity join (SSJoin) operator, which can be used as a building block to implement a broad variety of notions of similarity (e.g., edit similarity, Jaccard similarity, generalized edit similarity, hamming distance, soundex, etc.) as well as similarity based on co-occurrences. The SSJoin operator can exploit the observation that set overlap can be used effectively to support a variety of similarity functions. The SSJoin operator compares values based on “sets” associated with (or explicitly constructed for) each one of them.

Abstract: The subject disclosure pertains to a powerful and flexible framework for record matching. The framework facilitates design of a record matching query or package composed of a set of well-defined primitive operators (e.g., relational, data cleaning . . . ), which can ultimately be executed to match records. To assist design of such packages, a learning technique based on examples is provided. More specifically, a set of matching and non-matching record pairs can be input and employed to facilitate automatic package generation. A generated package can subsequently be transformed manually and/or automatically into a semantically equivalent form optimized for execution.

Abstract: The claimed subject matter relates to incorporating a skyline operator within a relational database engine, and more particularly to a database engine that utilizes novel techniques to determine the lowest cost of generating the skyline produced by the skyline operator. The database engine receives queries and associated preferences and based on a cardinality estimate and a cost estimate an appropriate skyline generating technique is utilized to produce a skyline representative of the received queries and its associated preferences.

Abstract: The subject disclosure pertains to efficient computation of the difference between queries by exploiting commonality between them. A minimal difference query (MDQ) is generated that roughly corresponds to removal of as many joins as possible while still accurately representing the query difference. The minimal difference can be employed to further substantially the scope of view matching where a query is not wholly subsumed by a view. Additionally, the minimal difference query can be employed as an analytical tool in various contexts.

Abstract: The subject disclosure pertains to efficient computation of the difference between queries by exploiting commonality between them. A minimal difference query (MDQ) is generated that roughly corresponds to removal of as many joins as possible while still accurately representing the query difference. The minimal difference can be employed to further substantially the scope of view matching where a query is not wholly subsumed by a view. Additionally, the minimal difference query can be employed as an analytical tool in various contexts.

Abstract: The subject disclosure pertains to efficient computation of the difference between queries by exploiting commonality between them. A minimal difference query (MDQ) is generated that roughly corresponds to removal of as many joins as possible while still accurately representing the query difference. The minimal difference can be employed to further substantially the scope of view matching where a query is not wholly subsumed by a view. Additionally, the minimal difference query can be employed as an analytical tool in various contexts.

Abstract: A set similarity join system and method are provided. The system can be employed to facilitate data cleaning based on similarities through the identification of “close” tuples (e.g., records and/or rows). “Closeness” can be is evaluated using a similarity function(s) chosen to suit the domain and/or application. Thus, the system facilitates generic domain-independent data cleansing. The system can be employed with a foundational primitive, the set similarity join (SSJoin) operator, which can be used as a building block to implement a broad variety of notions of similarity (e.g., edit similarity, Jaccard similarity, generalized edit similarity, hamming distance, soundex, etc.) as well as similarity based on co-occurrences. The SSJoin operator can exploit the observation that set overlap can be used effectively to support a variety of similarity functions. The SSJoin operator compares values based on “sets” associated with (or explicitly constructed for) each one of them.