Abstract: A computer-implemented method, system, and computer program product for producing a semantic query by example are provided. The method includes receiving examples of potential results from querying a database table with an associated ontology, and extracting features from the database table and the examples based on the associated ontology. The method further includes training a classifier based on the examples and the extracted features, and applying the classifier to the database table to obtain a semantic query result. The method also includes outputting the semantic query result to a user interface, and requesting user feedback of satisfaction with the semantic query result. The method additionally includes updating the classifier and the semantic query result iteratively in response to the user feedback.

Abstract: A method is provided for compressing results of a join query. A join order of a result set is determined from the join query, where the result set includes a plurality of tuples. A plurality of dictionary entries for the result set is received. A nested hierarchy of dictionaries is created based on the join order and the dictionary entries. A plurality of encoded tuples is received. The nested hierarchy of dictionaries is used by a processor to decode the plurality of encoded tuples so as to produce the plurality of tuples of the result set.

Abstract: A method is provided for compressing results of a join query. A join order of a result set is determined from the join query, where the result set includes a plurality of tuples. A plurality of dictionary entries for the result set is received. A nested hierarchy of dictionaries is created based on the join order and the dictionary entries. A plurality of encoded tuples is received. The nested hierarchy of dictionaries is used by a processor to decode the plurality of encoded tuples so as to produce the plurality of tuples of the result set.

Abstract: A method is provided for compressing results of a join query. A join order of a result set is determined from the join query, where the result set includes a plurality of tuples. A plurality of dictionary entries for the result set is received. A nested hierarchy of dictionaries is created based on the join order and the dictionary entries. A plurality of encoded tuples is received. The nested hierarchy of dictionaries is used by a processor to decode the plurality of encoded tuples so as to produce the plurality of tuples of the result set.

Abstract: A method is provided for compressing results of a join query. A join order of a result set is determined from the join query, where the result set includes a plurality of tuples. A plurality of dictionary entries for the result set is received. A nested hierarchy of dictionaries is created based on the join order and the dictionary entries. A plurality of encoded tuples is received. The nested hierarchy of dictionaries is used by a processor to decode the plurality of encoded tuples so as to produce the plurality of tuples of the result set.

Abstract: A method is provided for compressing results of a join query. A join order of a result set is determined from the join query, where the result set includes a plurality of tuples. A plurality of dictionary entries for the result set is received. A nested hierarchy of dictionaries is created based on the join order and the dictionary entries. A plurality of encoded tuples is received. The nested hierarchy of dictionaries is used by a processor to decode the plurality of encoded tuples so as to produce the plurality of tuples of the result set.

Abstract: A method, system, and computer readable medium for collecting statistics associated with data in a database are disclosed. The computer readable medium implements the method comprises determining an amount of memory needed to collect statistics for data associated with a defined data type in a relational database. The defined data type is based upon a mark-up language using a tree structure with one or more root-to-node paths therein. The amount of memory is allocated as determined for collecting the statistics for the data of the defined data type. A statistics collection is performed for the data of the defined data type in a single pass through the database and within the amount of memory which has been allocated. The performing includes at least determining a total number of instances of at least one path-identifier associated with a given value within a given set of documents.

Abstract: Techniques are disclosed for selecting a delete-safe compression method for a plurality of delta encoded data values (e.g., delta encoded integers or deltas). For example, a computer-implemented method for selecting an optimal delete-safe compression algorithm from among two or more compression algorithms for use on a plurality of delta encoded data values includes the following steps. The maximum number of data values eliminated by each of the two or more compression algorithms is computed. For the plurality of delta encoded data values to be compressed, the minimum size of the plurality of delta encoded data values before compression thereof is computed. A delete-safe threshold value is computed based on the minimum size of the plurality of delta encoded data values. Then, the compression algorithm is selected from the two or more compression algorithms that achieves the delete-safe threshold value.

Abstract: Disclosed are a method, information processing system, and computer readable medium for processing queries. The method includes receiving a data query for a set of hierarchical markup documents. At least one query path expression is extracted from the data query. The query path is processed against at least one shared hierarchical markup document in a plurality of shared hierarchical markup documents. The plurality of shared hierarchical documents is associated with the set of hierarchical markup documents. In response to the shared hierarchical markup document completely matching the query path expression, a query result for the data query is generated. The query result is based on the processing of the query path expression against at least one of the shared hierarchical markup document and the difference hierarchical markup document.

Abstract: A method of semantic-aware record matching includes receiving source and target string record specifications associated with a source string record and a target string record, receiving semantic knowledge referring to tokens of the source string record and target string record, creating a first set of tokens for the source string record and a second set of tokens for the target string record based on the semantic knowledge, assigning a similarity score to the source string record and the target string record based on a semantic relationship between the first set of tokens and the second set of tokens, and matching the source string record and the target string record based on the similarity score.

Abstract: A method is provided for compressing results of a join query. A join order of a result set comprising multiple tuples is determined from the join query, and a nested hierarchy of dictionaries is maintained based on the join order. The nested hierarchy of dictionaries is used to encode each of the tuples of the result set so as to produce an encode tuple, and each of the encoded tuples is transmitted to a client system. Also provided is a method for decompressing results of a join query.

Abstract: Techniques are disclosed for optimizing queries to hierarchically structured data. For example, a method for processing a query directed to data having a hierarchical structure with a plurality of data nodes comprises the following steps. One or more structural attributes describing the hierarchical structure of the data are identified. The query is partitioned into two or more query partitions using at least one of the one or more identified structural attributes. A parallel execution plan is determined for the query by splitting into components one or more of: the query into at least two of the query partitions; and the hierarchical structure of the data. The split components are executed in parallel on different computer processes according to the parallel execution plan.

Abstract: A method, information processing system, and computer program storage product to compress sorted values. At least a first prefix and a second prefix in a plurality of prefixes are compared. Each prefix comprises at least a portion of a plurality of sorted values. A respective prefix comprises a set of consecutive characters including at least a first character of a respective sorted value. The respective sorted value further comprising a respective suffix comprising consecutive characters of the respective sorted value that are after the respective prefix. At least a respective first character of the first prefix and a respective first character of the second prefix are determined to be substantially identical. The first prefix is merged with the second prefix into a single prefix comprising the first character. A set of suffixes associated with the first prefix is updated to reflect an association with the second prefix.

Abstract: Disclosed are a system, method, and computer readable medium for collecting statistics associated with data in a database. The method comprises determining an amount of memory needed to collect statistics for data associated with a defined data type in a relational database. The defined data type is based upon a mark-up language using a tree structure with one or more root-to-node paths therein. The amount of memory as determined is allocated for collecting the statistics for the data of the defined data type. A statistics collection is performed for the data of the defined data type in a single pass through the database and within the amount of memory which has been allocated.

Abstract: A method is provided for compressing results of a join query. A join order of a result set comprising multiple tuples is determined from the join query, and a nested hierarchy of dictionaries is maintained based on the join order. The nested hierarchy of dictionaries is used to encode each of the tuples of the result set so as to produce an encode tuple, and each of the encoded tuples is transmitted to a client system. Also provided is a method for decompressing results of a join query.

Abstract: A method, information processing system, and computer program storage product retrieve data from a database. A search request is received from a user for a set of data in at least one database. An ontology query over is performed over at least one ontology associated with at least one database resulting in an ontological dataset associated with the search request in response to receiving the search request from the user. The ontological dataset includes at least one of a set of synonyms, a set of hypernyms, and a set of hyponyms, associated with the search request. A data query is performed over data in the at least one database using the ontological dataset in response to performing the ontology query. The set of data is returned to the user based on the data query that has been performed.

Abstract: Techniques are disclosed for encoding a variable length structure such that it facilitates forward and reverse scans of a list of such structures as needed. While the techniques are applicable to a wide variety of applications, they are particularly well-suited for use with structures such as those found in compressed database indexes. For example, a computer-implemented method for processing one or more variable length data structures includes the following steps. Each variable length data structure is obtained. Each variable length structure comprises one or more data block. A variable length encoding process is applied to the one or more blocks of each variable length data structure which comprises setting a continuation data value in each block to a first value or a second value, wherein the setting of the continuation data values enables bi-directional scanning of each variable length structure.

Abstract: Disclosed are a method, information processing system, and a computer readable medium for managing documents. The method includes analyzing a plurality of hierarchical markup documents, wherein each hierarchical markup document is representable by a hierarchical tree structure. A shared hierarchical markup document associated with the plurality of hierarchical markup documents is generated based on the analyzing. Each hierarchical markup document in the plurality of hierarchical markup documents is compared with the shared hierarchical document. A plurality of difference hierarchical markup documents is generated based on the comparing.

Abstract: Histogram construction and selectivity estimation for string and substring match queries in databases of data having strings associated with attributes. The histogram construction counts string-attribute pairs in the documents, and outputs string-attribute-count triples sorted by count. The collection is partitioned into buckets. A synopsis is generated for the partition, having an average selectivity or count of the string-attribute-count triples in the partition and summary information representing the set of string-attribute pairs belonging to the bucket. Subsequent queries, both for exact and substring matches, use the synopsis to estimate the selectivity of buckets.

Abstract: Techniques are disclosed for optimizing queries to hierarchically structured data. For example, a method for processing a query directed to data having a hierarchical structure with a plurality of data nodes comprises the following steps. One or more structural attributes describing the hierarchical structure of the data are identified. The query is partitioned into two or more query partitions using at least one of the one or more identified structural attributes. A parallel execution plan is determined for the query by splitting into components one or more of: the query into at least two of the query partitions; and the hierarchical structure of the data. The split components are executed in parallel on different computer processes according to the parallel execution plan.