Big Data Management Challenges, Approaches, Tools and their limitations

International audienceBig Data is the buzzword everyone talks about. Independently of the application domain, today there is a consensus about the V's characterizing Big Data: Volume, Variety, and Velocity. By focusing on Data Management issues and past experiences in the area of databases systems, this chapter examines the main challenges involved in the three V's of Big Data. Then it reviews the main characteristics of existing solutions for addressing each of the V's (e.g., NoSQL, parallel RDBMS, stream data management systems and complex event processing systems). Finally, it provides a classification of different functions offered by NewSQL systems and discusses their benefits and limitations for processing Big Data

Induced tagging: promoting resource discovery and recommendation in digital libraries

ABSTRACT We introduce the notion of "induced tagging" in the context of learning communities that are supported by digital libraries. We also describe an environment aimed to foster discovery and recommendation of digital library resources based on induced tagging

User Identification, Classification and Recommendation in Web Usage Mining -An Approach for Personalized Web Mining

Abstract In recent years, Web Analytics (WA) is turning out to be an emerging research topic due to the extensive advancements in the techniques that aid in accessing the web contents, which millions of people have shared on the web. The information that has connection to the theme being searched may not be recognized always, if the personalization system operates in accordance with the usage-dependent outcomes alone. In this research work a new method is introduced for Personalized Web Search system, wherein, the users are enabled to have access to the relevant web pages as per their choice from the URL list. The first stage of this research deals with Semantic Web Personalization, which provides a merging between the content semantics as well as the usage data that are stated as ontology terms. This system supports the computation of the navigational patterns that are semantically improvised, so that constructive recommendations can be successfully engendered. It can be perceived that no other systems excluding the semantic web personalization system described here is employed in nonsemantic web sites. The second stage of the work is to assist in augmenting the quality of the recommendations depending on the structure lying beneath the website. Finally, the testing is achieved through the utilization of a prolonged database link. The analysis of variation that exists among the different classes of parameters is made later, when the privacy is formulated using the memory usage and the period of execution