1,090 research outputs found
Report of the Stanford Linked Data Workshop
The Stanford University Libraries and Academic Information Resources (SULAIR) with the Council on Library and Information Resources (CLIR) conducted at week-long workshop on the prospects for a large scale, multi-national, multi-institutional prototype of a Linked Data environment for discovery of and navigation among the rapidly, chaotically expanding array of academic information resources. As preparation for the workshop, CLIR sponsored a survey by Jerry Persons, Chief Information Architect emeritus of SULAIR that was published originally for workshop participants as background to the workshop and is now publicly available. The original intention of the workshop was to devise a plan for such a prototype. However, such was the diversity of knowledge, experience, and views of the potential of Linked Data approaches that the workshop participants turned to two more fundamental goals: building common understanding and enthusiasm on the one hand and identifying opportunities and challenges to be confronted in the preparation of the intended prototype and its operation on the other. In pursuit of those objectives, the workshop participants produced:1. a value statement addressing the question of why a Linked Data approach is worth prototyping;2. a manifesto for Linked Libraries (and Museums and Archives and âŠ);3. an outline of the phases in a life cycle of Linked Data approaches;4. a prioritized list of known issues in generating, harvesting & using Linked Data;5. a workflow with notes for converting library bibliographic records and other academic metadata to URIs;6. examples of potential âkiller appsâ using Linked Data: and7. a list of next steps and potential projects.This report includes a summary of the workshop agenda, a chart showing the use of Linked Data in cultural heritage venues, and short biographies and statements from each of the participants
A Survey on Web Usage Mining, Applications and Tools
World Wide Web is a vast collection of unstructured web documents like text, images, audio, video or Multimedia content. As web is growing rapidly with millions of documents, mining the data from the web is a difficult task. To mine various patterns from the web is known as Web mining. Web mining is further classified as content mining, structure mining and web usage mining. Web usage mining is the data mining technique to mine the knowledge of usage of web data from World Wide Web. Web usage mining extracts useful information from various web logs i.e. users usage history. This is useful for better understanding and serve the people for better web applications. Web usage mining not only useful for the people who access the documents from the World Wide Web, but also it useful for many applications like e-commerce to do personalized marketing, e-services, the government agencies to classify threats and fight against terrorism, fraud detection, to identify criminal activities, the companies can establish better customer relationship and can improve their businesses by analyzing the people buying strategies etc. This paper is going to explain in detail about web usage mining and how it is helpful. Web Usage Mining has seen rapid increase towards research and people communities
Integrating data warehouses with web data : a survey
This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML
technologies that are currently being used to integrate, store, query, and retrieve Web data and their application to DWs. The paper
reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces
the problem of dealing with semistructured data in a DW. It studies Web data repositories, the design of multidimensional databases for
XML data sources, and the XML extensions of OnLine Analytical Processing techniques. The paper addresses the application of
information retrieval technology in a DW to exploit text-rich document collections. The authors hope that the paper will help to discover
the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as to identify open research
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Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
A conceptual framework and a risk management approach for interoperability between geospatial datacubes
De nos jours, nous observons un intĂ©rĂȘt grandissant pour les bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ces bases de donnĂ©es sont dĂ©veloppĂ©es pour faciliter la prise de dĂ©cisions stratĂ©giques des organisations, et plus spĂ©cifiquement lorsquâil sâagit de donnĂ©es de diffĂ©rentes Ă©poques et de diffĂ©rents niveaux de granularitĂ©. Cependant, les utilisateurs peuvent avoir besoin dâutiliser plusieurs bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ces bases de donnĂ©es peuvent ĂȘtre sĂ©mantiquement hĂ©tĂ©rogĂšnes et caractĂ©risĂ©es par diffĂ©rent degrĂ©s de pertinence par rapport au contexte dâutilisation. RĂ©soudre les problĂšmes sĂ©mantiques liĂ©s Ă lâhĂ©tĂ©rogĂ©nĂ©itĂ© et Ă la diffĂ©rence de pertinence dâune maniĂšre transparente aux utilisateurs a Ă©tĂ© lâobjectif principal de lâinteropĂ©rabilitĂ© au cours des quinze derniĂšres annĂ©es. Dans ce contexte, diffĂ©rentes solutions ont Ă©tĂ© proposĂ©es pour traiter lâinteropĂ©rabilitĂ©. Cependant, ces solutions ont adoptĂ© une approche non systĂ©matique. De plus, aucune solution pour rĂ©soudre des problĂšmes sĂ©mantiques spĂ©cifiques liĂ©s Ă lâinteropĂ©rabilitĂ© entre les bases de donnĂ©es gĂ©ospatiales multidimensionnelles nâa Ă©tĂ© trouvĂ©e. Dans cette thĂšse, nous supposons quâil est possible de dĂ©finir une approche qui traite ces problĂšmes sĂ©mantiques pour assurer lâinteropĂ©rabilitĂ© entre les bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ainsi, nous dĂ©finissons tout dâabord lâinteropĂ©rabilitĂ© entre ces bases de donnĂ©es. Ensuite, nous dĂ©finissons et classifions les problĂšmes dâhĂ©tĂ©rogĂ©nĂ©itĂ© sĂ©mantique qui peuvent se produire au cours dâune telle interopĂ©rabilitĂ© de diffĂ©rentes bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Afin de rĂ©soudre ces problĂšmes dâhĂ©tĂ©rogĂ©nĂ©itĂ© sĂ©mantique, nous proposons un cadre conceptuel qui se base sur la communication humaine. Dans ce cadre, une communication sâĂ©tablit entre deux agents systĂšme reprĂ©sentant les bases de donnĂ©es gĂ©ospatiales multidimensionnelles impliquĂ©es dans un processus dâinteropĂ©rabilitĂ©. Cette communication vise Ă Ă©changer de lâinformation sur le contenu de ces bases. Ensuite, dans lâintention dâaider les agents Ă prendre des dĂ©cisions appropriĂ©es au cours du processus dâinteropĂ©rabilitĂ©, nous Ă©valuons un ensemble dâindicateurs de la qualitĂ© externe (fitness-for-use) des schĂ©mas et du contexte de production (ex., les mĂ©tadonnĂ©es). Finalement, nous mettons en Ćuvre lâapproche afin de montrer sa faisabilitĂ©.Today, we observe wide use of geospatial databases that are implemented in many forms (e.g., transactional centralized systems, distributed databases, multidimensional datacubes). Among those possibilities, the multidimensional datacube is more appropriate to support interactive analysis and to guide the organizationâs strategic decisions, especially when different epochs and levels of information granularity are involved. However, one may need to use several geospatial multidimensional datacubes which may be semantically heterogeneous and having different degrees of appropriateness to the context of use. Overcoming the semantic problems related to the semantic heterogeneity and to the difference in the appropriateness to the context of use in a manner that is transparent to users has been the principal aim of interoperability for the last fifteen years. However, in spite of successful initiatives, today's solutions have evolved in a non systematic way. Moreover, no solution has been found to address specific semantic problems related to interoperability between geospatial datacubes. In this thesis, we suppose that it is possible to define an approach that addresses these semantic problems to support interoperability between geospatial datacubes. For that, we first describe interoperability between geospatial datacubes. Then, we define and categorize the semantic heterogeneity problems that may occur during the interoperability process of different geospatial datacubes. In order to resolve semantic heterogeneity between geospatial datacubes, we propose a conceptual framework that is essentially based on human communication. In this framework, software agents representing geospatial datacubes involved in the interoperability process communicate together. Such communication aims at exchanging information about the content of geospatial datacubes. Then, in order to help agents to make appropriate decisions during the interoperability process, we evaluate a set of indicators of the external quality (fitness-for-use) of geospatial datacube schemas and of production context (e.g., metadata). Finally, we implement the proposed approach to show its feasibility
Feature Extraction and Duplicate Detection for Text Mining: A Survey
Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. Hence we also review the literature on duplicate detection and data fusion (remove and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user
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