Abstract: A computational device receives a first text message in a text messaging format from a mobile device to access a website that stores information in a markup language format. The computational device converts one or more elements of the stored information from the markup language format to the text messaging format. The computational device sends a second text message that indicates how to interact with the website in the text messaging format to the mobile device.

Abstract: Systems and methods for dynamic product bundling are described herein. For example, embodiments dynamically generate product bundle for customer within a particular segment in view of that customer's interest in a particular product. Embodiments determine customer affinity, customer commonality, and product complementarity and use this information to dynamically generate and optimize product bundles for customers interested in one or more products.

Abstract: An embodiment of the invention provides a method and system for analyzing a plurality of reports. More specifically, a change detection module predicts results of future reports based on past reports and identifies a first report that deviates from its predicted results. A dependency analysis module connected to the change detection module at least one report sharing a dependency with the first report by performing a dependency analysis and/or a usage analysis. The dependency analysis labels the first report and at least one second report as sharing a dependency if the second report deviates from its predicted results. The usage analysis labeling the first report and at least one report analyzed by an analyst as sharing a dependency if the report analyzed by the analyst is analyzed in response to the identification of the first report.

Abstract: Systems and methods for dynamic product bundling are described herein. For example, embodiments dynamically generate product bundle for customer within a particular segment in view of that customer's interest in a particular product. Embodiments determine customer affinity, customer commonality, and product complementarity and use this information to dynamically generate and optimize product bundles for customers interested in one or more products.

Abstract: An embodiment of the invention provides a method and system for analyzing a plurality of reports. More specifically, a change detection module predicts results of future reports based on past reports and identifies a first report that deviates from its predicted results. A dependency analysis module connected to the change detection module at least one report sharing a dependency with the first report by performing a dependency analysis and/or a usage analysis. The dependency analysis labels the first report and at least one second report as sharing a dependency if the second report deviates from its predicted results. The usage analysis labeling the first report and at least one report analyzed by an analyst as sharing a dependency if the report analyzed by the analyst is analyzed in response to the identification of the first report.

Abstract: A Customer Relationship Management (CRM) offer template is established in data storage and includes at least one variable field and having at least one offer attribute. For each of a plurality of customer groups, a data processing system calculates an expected benefit to be obtained by sending an instantiated CRM offer to a customer based upon historical responses, offer cost, and legal values of the at least one variable field. The data processing system outputs a CRM offer instance and target list of customers that maximizes the expected benefit.

Abstract: In the context of data administration in enterprises, an effective manner of providing a central data warehouse, particularly via employing a tool that helps by analyzing existing data and reports from different business units. In accordance with at least one embodiment of the invention, such a tool analyzes the data model of an enterprise and proposes alternatives for building a new data warehouse. The tool, in accordance with at least one embodiment of the invention, models the problem of identifying fact/dimension attributes of a warehouse model as a graph cut on a Dependency Analysis Graph (DAG). The DAG is built using existing data models and the report generation scripts. The tool also uses the DAG for generation of ETL (Extract, Transform Load) scripts that can be used to populate the newly proposed data warehouse from data present in the existing schemas.

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: Opinions about a topic are extracted from a corpus of text documents. Opinions are extracted based on rules defining regular expressions for parts-of-speech tags. Opinions are grouped based on their semantic orientation as favorable, unfavorable or neutral. A balanced and accurate assessment of sentiment towards a topic can thus be determined.

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: A selection system and method. The selection method comprises receiving, by a computing system, a taxonomy of data related to a specified domain of knowledge on the web. A taxonomy tree is constructed from the taxonomy. A sub tree related to a sub-domain from specified domain is selected from the taxonomy tree. A first list comprising user expected universal resource locators (URLs) related to the sub-domain is received. A second list comprising topic expressions defining each node of the taxonomy sub-tree is generated. A query based on the second list is generated. The query is applied on an index of URLs generated from a web crawling process to generate a third list. A recall value is calculated based on the first list and the third list.

Abstract: A selection system and method. The selection method comprises receiving, by a computing system, a taxonomy of data related to a specified domain of knowledge on the web. A taxonomy tree is constructed from the taxonomy. A sub tree related to a sub-domain from specified domain is selected from the taxonomy tree. A first list comprising user expected universal resource locators (URLs) related to the sub-domain is received. A second list comprising topic expressions defining each node of the taxonomy sub-tree is generated. A query based on the second list is generated. The query is applied on an index of URLs generated from a web crawling process to generate a third list. A recall value is calculated based on the first list and the third list.

Abstract: A method (400) is disclosed of extracting factoids from text repositories, with the factoids being associated with a given factoid category. The method (400) starts by training a classifier (230) to recognise factoids relevant to that given factoid category. Documents or document summaries relevant to the given factoid category is next collected (410) from the text repositories. Sentences having a predetermined association to the given factoid category is extracted (420) from the documents or said document summaries. Those sentences are classified (440), in a noisy environment, using the classifier (230) to extract snippets containing phrases relevant to the given factoid category. It is the extracted snippets that are the factoid associated with the given factoid category.

Abstract: Documents are represented based on their structure, which arises from the relationship between various elements in the document. After representing documents based on their structure in vector form, a method of measuring similarity between vectors is used to obtain the measure of structural similarity between two given documents.

Abstract: A system and method of retrieving data from a database comprising unstructured data comprises specifying a text analytic component in an unstructured text query at query runtime; submitting the unstructured text query to a web service database; filtering unstructured text data in the web service database based on constraints defined in the text analytic component in the query; and receiving the filtered unstructured text data based on the submitted query from the web service database, wherein the text analytic component comprises metadata requirements. Preferably, the constraints comprise any of positive sentiments regarding an unstructured text document and negative sentiments regarding the unstructured text document. Alternatively, the constraints may comprise any of name spotting constraints, address spotting constraints, date spotting constraints, and entity spotting constraints. The filtering preferably occurs using a web-based callback service specified in a WFQL XML document.

Abstract: Opinions about a topic are extracted from a corpus of text documents. Opinions are extracted based on rules defining regular expressions for parts-of-speech tags. Opinions are grouped based on their semantic orientation as favourable, unfavourable or neutral. A balanced and accurate assessment of sentiment towards a topic can thus be determined.

Abstract: Documents are represented based on their structure, which arises from the relationship between various elements in the document. After representing documents based on their structure in vector form, a method of measuring similarity between vectors is used to obtain the measure of structural similarity between two given documents.