Abstract: Systems and methods for clustering user reviews are disclosed in which a plurality of user reviews are extracted from electronic documents. The electronic documents contain user reviews of a plurality of items of interest. A set of user reviews is identified in the plurality of user reviews as being associated with the same item of interest in the plurality of items of interest. Item identifying information included in the electronic documents is used for this identification. The set of user reviews is then associated with the same item of interest. Examples of item identifying information include unique product identifiers, brand names, model numbers, and category information. In some instances, the item identifying information is extracted from metadata included in the electronic document. In some instances, the electronic documents are obtained from e-commerce websites or product-review websites.

Abstract: The embodiments disclosed herein include new, more efficient ways to collect product reviews from the Internet, aggregate reviews for the same product, and provide an aggregated review to end users in a searchable format. One aspect of the invention is a graphical user interface on a computer that includes a plurality of portions of reviews for a product and a search input area for entering search terms to search for reviews of the product that contain the search terms.

Abstract: The embodiments disclosed herein include new, more efficient ways to collect product reviews from the Internet, aggregate reviews for the same product, and provide an aggregated review to end users in a searchable format. One aspect of the invention is a graphical user interface on a computer that includes a plurality of portions of reviews for a product and a search input area for entering search terms to search for reviews of the product that contain the search terms.

Abstract: The embodiments disclosed herein include new, more efficient ways to collect product reviews from the Internet, aggregate reviews for the same product, and provide an aggregated review to end users in a searchable format. One aspect of the invention is a graphical user interface on a computer that includes a plurality of portions of reviews for a product and a search input area for entering search terms to search for reviews of the product that contain the search terms.

Abstract: The embodiments disclosed herein include new, more efficient ways to collect product reviews from the Internet, aggregate reviews for the same product, and provide an aggregated review to end users in a searchable format. One aspect of the invention is a graphical user interface on a computer that includes a plurality of portions of reviews for a product and a search input area for entering search terms to search for reviews of the product that contain the search terms.

Abstract: The embodiments disclosed herein include new, more efficient ways to collect product reviews from the Internet, aggregate reviews for the same product, and provide an aggregated review to end users in a searchable format. One aspect of the invention is a graphical user interface on a computer that includes a plurality of portions of reviews for a product and a search input area for entering search terms to search for reviews of the product that contain the search terms.

Abstract: An outlier index for a database and a given workload is generated by identifying sub-relations of tuples in the database induced by selection and group by conditions in queries in the workload. A variance is then generated for values in each sub-relation. Sub-relations having higher variances are selected, and outliers from such sub-relations having higher variances are generated.

Abstract: A method of estimating results of a database query, the results are estimated by performing a sampling of weighted tuples in a database based on a probability of usage of tuples required in executing a workload. A probability is associated with each tuple sampled. An aggregate is computed over values in each sampled tuple while multiplying by the inverses of the probabilities associated with each tuple sampled.

Abstract: Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data.

Abstract: Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data.

Abstract: This disclosure describes leveraging workload information associated with executed database queries for estimating the result of a current database query. The workload information is analyzed to determine the usage of tuples in a database during query execution, such as how often a tuple is accessed and the number of different queries that accessed the tuple. A tuple is assigned a weight value that is based on the analyzed workload information. The particular tuples sampled for estimating a result for the current query is based on each tuple's weight value. The workload information may also be leveraged to generate an outlier index that identifies outlier tuples associated with the executed queries or that identifies outlier tuples associated with particular queries that are executed more frequently than other queries. The result for the current query can also be estimated using the sampled values along with the outlier tuples from the outlier index.

Abstract: Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled in one of many known ways to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data. Further methods involve the use of weighted sampling and weighted selection of outlier values for low selectivity queries, or queries having group by.

Abstract: Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled in one of many known ways to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data. Further methods involve the use of weighted sampling and weighted selection of outlier values for low selectivity queries, or queries having group by.

Abstract: Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled in one of many known ways to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data. Further methods involve the use of weighted sampling and weighted selection of outlier values for low selectivity queries, or queries having group by.

Abstract: Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled in one of many known ways to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data. Further methods involve the use of weighted sampling and weighted selection of outlier values for low selectivity queries, or queries having group by.

Abstract: Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled in one of many known ways to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data.