A Data Cube Model for Analysis of High Volumes of Ambient Data

Abstract

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data.

Automating the Integration of Clinical Studies into Medical Ontologies

“…For each iteration of keyword matching, we begin with a set of terms that represent the Risk Factor: As the structure suggests, we adopt a purely relational approach to data mining queries for performance reasons and as we are dealing with a single relational style dataset. However, we can adopt an XML-based approach where it is necessary to combine ontologies as we have shown in [7] that similar levels of performance can be achieved.…”

The concepts of Smart cities, Smart Tourism Destination and Smart Tourism Cities and their interrelationship

“…The idea of the "Smart City," which is the subject of several efforts, projects, and demonstrations, is often supported by one or more components of ambient systems that need to be integrated through a mediation process [58]. [53] A network-managed city that uses high-performance ICT-based fixed and mobile Smart City infrastructure to provide its residents with services and content over the network [58]. [54] A "smart city" is an urban setting backed by pervasive ICT systems that can provide cutting-edge and cutting-edge services to inhabitants in order to improve their overall quality of life [45].…”

Data Cube Computational Model with Hadoop MapReduce

“…It is difficult to provide a logical and direct mapping from one data model to the other due to the impedance mismatch between them (Rusu et al, 2009). In our previous works (Gui and Roantree, 2012) (Gui and Roantree, 2013), we have proposed a pipeline design based on an OLAP data cube construction framework designed for real time web generated sensor data. We transformed sensor data into an XML stream conforming to the data warehouse logical model and built a corresponding data cube tree and serialized it into an XML data cube representation.…”