A data cube model for analysis of high volumes of ambient data

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

Standards-based sensor web for wide area monitoring of power systems

The balance of supply and demand of energy is the key factor in the stability of power systems. A small disturbance in the supply demand relationship, if not properly handled, can cascade into a major outage, costing millions of dollars to the stakeholders. Proper monitoring and exchange of critical information in real time is the only solution to prevent the instability in this vulnerable system. But, the disparity in the protocols used by power utilities and the lack of infrastructure for information exchange are proving to be hindrance to obtaining a reliable de-regularized power industry. In this thesis, an emerging Sensor Web Enablement (SWE) has been adapted for the wide area monitoring of power systems. SWE and CIM provide a solution to both problems; the heterogeneity of data and the lack of central repository of the data for proper action. The sensor data from utilities that are published in CIM were modeled thorough a SensorML and exposed via a Sensor Observation Service (SOS). This provides a standard method for discovering and accessing the sensor data between utilities and facilitates rapid response functionality to handle contingences

A Web-Based Collaborative Multimedia Presentation Document System

With the distributed and rapidly increasing volume of data and expeditious development of modern web browsers, web browsers have become a possible legitimate vehicle for remote interactive multimedia presentation and collaboration, especially for geographically dispersed teams. To our knowledge, although there are a large number of applications developed for these purposes, there are some drawbacks in prior work including the lack of interactive controls of presentation flows, general-purpose collaboration support on multimedia, and efficient and precise replay of presentations. To fill the research gaps in prior work, in this dissertation, we propose a web-based multimedia collaborative presentation document system, which models a presentation as media resources together with a stream of media events, attached to associated media objects. It represents presentation flows and collaboration actions in events, implements temporal and spatial scheduling on multimedia objects, and supports real-time interactive control of the predefined schedules. As all events are represented by simple messages with an object-prioritized approach, our platform can also support fine-grained precise replay of presentations. Hundreds of kilobytes could be enough to store the events in a collaborative presentation session for accurate replays, compared with hundreds of megabytes in screen recording tools with a pixel-based replay mechanism

Grouping related attributes

Grouping objects that are described by attributes, or clustering is a central notion in data mining. On the other hand, similarity or relationships between attributes themselves is equally important but relatively unexplored. Such groups of attributes are also known as directories, concept hierarchies or topics depending on the underlying data domain. The similarities between the two problems of grouping objects and attributes might suggest that traditional clustering techniques are applicable. This thesis argues that traditional clustering techniques fail to adequately capture the solution we seek. It also explores domain-independent techniques for grouping attributes. The notion of similarity between attributes and therefore clustering in categorical datasets has not received adequate attention. This issue has seen renewed interest in the knowledge discovery community, spurred on by the requirements of personalization of information and online search technology. The problem is broken down into (a) quantification of this notion of similarity and (b) the subsequent formation of groups, retaining attributes similar enough in the same group based on metrics that we will attempt to derive. Both aspects of the problem are carefully studied. The thesis also analyzes existing domainindependent approaches to building distance measures, proposing and analyzing iii several such measures for quantifying similarity, thereby providing a foundation for future work in grouping relevant attributes. The theoretical results are supported by experiments carried out on a variety of datasets from the text-mining, web-mining, social networks and transaction analysis domains. The results indicate that traditional clustering solutions are inadequate within this problem framework. They also suggest a direction for the development of distance measures for the quantification of the concept of similarity between categorical attributes

A Semantic Framework for Declarative and Procedural Knowledge

In any scientic domain, the full set of data and programs has reached an-ome status, i.e. it has grown massively. The original article on the Semantic Web describes the evolution of a Web of actionable information, i.e.\ud information derived from data through a semantic theory for interpreting the symbols. In a Semantic Web, methodologies are studied for describing, managing and analyzing both resources (domain knowledge) and applications (operational knowledge) - without any restriction on what and where they\ud are respectively suitable and available in the Web - as well as for realizing automatic and semantic-driven work\ud ows of Web applications elaborating Web resources.\ud This thesis attempts to provide a synthesis among Semantic Web technologies, Ontology Research, Knowledge and Work\ud ow Management. Such a synthesis is represented by Resourceome, a Web-based framework consisting of two components which strictly interact with each other: an ontology-based and domain-independent knowledge manager system (Resourceome KMS) - relying on a knowledge model where resource and operational knowledge are contextualized in any domain - and a semantic-driven work ow editor, manager and agent-based execution system (Resourceome WMS).\ud The Resourceome KMS and the Resourceome WMS are exploited in order to realize semantic-driven formulations of work\ud ows, where activities are semantically linked to any involved resource. In the whole, combining the use of domain ontologies and work ow techniques, Resourceome provides a exible domain and operational knowledge organization, a powerful engine for semantic-driven work\ud ow composition, and a distributed, automatic and\ud transparent environment for work ow execution