IBM LanguageWare Miner For Multidimensional Socio-Semantic Networks For Windows Cracked IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks With Keygen provides a unified API that helps in creating solutions for these types of multidimensional networks (people, documents, tasks, etc.) and provides an integrated platform for combining social computing, semantic processing, and activity-centered computing for enhanced user experience. Multidimensional Socio-Semantic Networks: Socio-Semantic Networks are social networks that are based on a social model of the network (people). In this type of networks, people have a wide variety of interests. For example, we have friends, and co-workers. However, in any social network, there is also the second dimension of the network (documents). For example, Facebook is a social network where people interact with each other and this activity is a result of their interests and values. However, you can have a wide variety of people, but only a small set of documents, as in the case of personal blogs. Language-based Socio-Semantic Networks: Multidimensional Socio-Semantic Networks also exist that are based on the understanding of language. In these networks, people and documents share the same space and exist in the same social network. Meaningful User Experience: In this type of networks, the ability to combine and learn from people's work, and their projects, in a language-based, socio-semantic network is essential. IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API (Application Programming Interface) is a unified API that can be used to create solutions for this type of networks. In order to get started with IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API, visit the IBM Knowledge Center at IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API: The IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API is a unified API that can be used to create solutions for this type of networks. For more information on the API, click For more information on IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks, click IBM LanguageWare Miner For Multidimensional Socio-Semantic Networks Incl Product Key * LanguageWare Miner for Multidimensional Socio-Semantic Networks (MLWMSND) is a structured query language (SQL) and built-in Java API for mining the rich sources of XML, HTML, RSS, and relational data, and recognizing, classifying, filtering, and summarizing the information. * MLWMSND provides a common query interface that allows application developers to take advantage of new types of data sets that previously would have been impossible to work with. *MLWMSND supports a vast variety of modeling scenarios, from discovering social networks and classifying users to finding and annotating objects and events. * MLWMSND creates multidimensional data models that relate abstract concepts and can be used by applications such as news presentation, enterprise content management, or collaborative agents. *MLWMSND is a customizable platform that can be configured to provide a user experience more akin to that of web content. *MLWMSND is built on a modular architecture to allow for easy extension of its capabilities, future scalability, increased speed and performance, and the ability to reuse components. The IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks Crack Mac offers a unique solution for mining diverse and large data sources. The platform allows for flexible configuration and design of data models. The two most important conceptual constructs are the multi-layer data model and the conceptual model. The multi-layer data model connects XML, relational, and social data sources in a single logical data model. The conceptual model is the user interface through which people (users) interact with the multi-layer data model. The conceptual model is used by the application to provide the user experience. The application specifies the user interface through the service interfaces and service builder API. Take the IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks for a test drive to get a feel for its capabilities and let us know what you think! License: This program is protected by international copyright law. The international copyright law does not permit the direct use of this material in whole or in part. This material may be used for any purpose, but copies of the substance of the program, as embedded in other documents or websites, must contain the copyright notice. For inquiries about the download location and the rights to distribute the program, contact: Name: Dusan S. Zivkovic Company: LanguageWare Email: dusan@languageware.com Paddy McGuinness Patric McGuinness (born 8 October 1981) is an Irish singer-songwriter from Sligo. He is an exponent of the acoustic guitar and plays it through a double amplifier. Career McGuinness began playing the guitar at the age of twelve and has been self-taught. He released his first 8e68912320 IBM LanguageWare Miner For Multidimensional Socio-Semantic Networks Product Key [Win/Mac] IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks integrates information analysis and synthesis technologies to enable Web sites, Web services, and other content-enabled applications to deliver online information that can be readily understood and used by humans. This integration enables the following: * Socially-enabled content analytics to understand, mine, and model social, behavioral, and socio-economic information, including user profiles, user interactions, and knowledge/opinion distribution, to provide context-sensitive, collaborative, and intelligent information processing. * Socio-semantic networks with integrated semantics for data-driven content mining, analytics, and synthesis of social and data content for enhanced experience. * User-centric social content mining with IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks as a unified platform for content analysis and synthesis. Users of IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks: * Specialized domain professionals who design, analyze, and mine social, behavioral, and socio-economic data to design, produce, and model content-driven applications. * Content managers who design and publish application-enabled content that incorporates social and data analytics. * Web developers who mine user data and extract socio-economic data from other online information sources. How it Works: The components of IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks are integrated to help you create solutions that enable people to interact with information in an intuitive manner, and to understand the context of any content so that they can make better decisions. The following key components are integrated in IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks: * Socio-semantic networks: includes two modules, sociosemantic network and Socio-semantic network engine; users can use sociosemantic network to directly access sociosemantic network capabilities. * Social mining: includes four modules, social mining, social mining engine, social mining data analyst, and social mining data synthesizer; users can use social mining to directly access the social mining data analyst and social mining data synthesizer capabilities. * Socio-semantic networks: provides a unified API that enables users to access a wide range of capabilities including: a) Context-sensitive, collaborative, and intelligent Web content management; b) Socially-enabled analytics to understand, mine, and model social, behavioral, and socio-economic information; c) User-centric social content What's New In IBM LanguageWare Miner For Multidimensional Socio-Semantic Networks? System Requirements For IBM LanguageWare Miner For Multidimensional Socio-Semantic Networks: • Windows Vista or later • 16GB RAM • 300 MB free HDD space • Minimum system requirements are CPU of 1.9 GHz and 1GB RAM • More than 2GB of RAM recommended • Mac OSX 10.7.5 or later • 32-bit Intel Processor and OSX 10.7.4 or later • 2GB RAM or more • 300 MB of free disk space • Version 1.3.3.0 or later
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