OLAP in Multifunction Multidimensional Database

International audienceMost models proposed for modeling multidimensional data warehouses consider a same function to determine how measure values are aggregated. We provide a more flexible conceptual model allowing associating each measure with several aggregation functions according to dimensions, hierarchies, and levels of granularity. This article studies the impacts of this model on the multidimensional table (MT) and the OLAP algebra [11]. It shows how the MT can handle several aggregation functions. It also introduces the changes of the internal mechanism of OLAP operators to take into account several aggregation functions especially if these functions are non-commutative

Modélisation des bases de données multidimensionnelles : analyse par fonctions d'agrégation multiples

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur

Modélisation des bases de données multidimensionnelles : analyse par fonctions d'agrégation multiples

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur

Designing and generating prototype for E-Governance based election strategy using biometric authentication and smart card device

Not availabl

Hybrid intelligent decision support system for distributed detection based on ad hoc integrated WSN & RFID

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe real time monitoring of environment context aware activities, based on distributed detection, is becoming a standard in public safety and service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt immediate reaction to potential hazards identified in real time, at an early stage to engage appropriate control actions. Effective emergency response can be supported only by available and acquired expertise or elaborate collaborative knowledge in the domain of distributed detection that include indoor sensing, tracking and localizing. This research proposes a hybrid conceptual multi-agent framework for the acquisition of collaborative knowledge in dynamic complex context aware environments for distributed detection. This framework has been applied for the design and development of a hybrid intelligent multi-agent decision system (HIDSS) that supports a decentralized active sensing, tracking and localizing strategy, and the deployment and configuration of smart detection devices associated to active sensor nodes wirelessly connected in a network topology to configure, deploy and control ad hoc wireless sensor networks (WSNs). This system, which is based on the interactive use of data, models and knowledge base, has been implemented to support fire detection and control access fusion functions aimed at elaborating: An integrated data model, grouping the building information data and WSN-RFID database, composed of the network configuration and captured data, A virtual layout configuration of the controlled premises, based on using a building information model, A knowledge-based support for the design of generic detection devices, A multi-criteria decision making model for generic detection devices distribution, ad hoc WSNs configuration, clustering and deployment, and Predictive data models for evacuation planning, and fire and evacuation simulation. An evaluation of the system prototype has been carried out to enrich information and knowledge fusion requirements and show the scope of the concepts used in data and process modelling. It has shown the practicability of hybrid solutions grouping generic homogeneous smart detection devices enhanced by heterogeneous support devices in their deployment, forming ad hoc networks that integrate WSNs and radio frequency identification (RFID) technology. The novelty in this work is the web-based support system architecture proposed in this framework that is based on the use of intelligent agent modelling and multi-agent systems, and the decoupling of the processes supporting the multi-sensor data fusion from those supporting different context applications. Although this decoupling is essential to appropriately distribute the different fusion functions, the integration of several dimensions of policy settings for the modelling of knowledge processes, and intelligent and pro-active decision making activities, requires the organisation of interactive fusion functions deployed upstream to a safety and emergency response.Saudi government, represented by the Ministry of Interior and General Directorate of Civil Defenc

Analysis of building performance data

In recent years, the global trend for digitalisation has also reached buildings and facility management. Due to the roll out of smart meters and the retrofitting of buildings with meters and sensors, the amount of data available for a single building has increased significantly. In addition to data sets collected by measurement devices, Building Information Modelling has recently seen a strong incline. By maintaining a building model through the whole building life-cycle, the model becomes rich of information describing all major aspects of a building. This work aims to combine these data sources to gain further valuable information from data analysis. Better knowledge of the building’s behaviour due to high quality data available leads to more efficient building operations. Eventually, this may result in a reduction of energy use and therefore less operational costs. In this thesis a concept for holistic data acquisition from smart meters and a methodology for the integration of further meters in the measurement concept are introduced and validated. Secondly, this thesis presents a novel algorithm designed for cleansing and interpolation of faulty data. Descriptive data is extracted from an open meta data model for buildings which is utilised to further enrich the metered data. Additionally, this thesis presents a methodology for how to design and manage all information in a unified Data Warehouse schema. This Data Warehouse, which has been developed, maintains compatibility with an open meta data model by adopting the model’s specification into its data schema. It features the application of building specific Key Performance Indicators (KPI) to measure building performance. In addition a clustering algorithm, based on machine learning technology, is developed to identify behavioural patterns of buildings and their frequency of occurrence. All methodologies introduced in this work are evaluated through installations and data from three pilot buildings. The pilot buildings were selected to be of diverse types to prove the generic applicability of the above concepts. The outcome of this work successfully demonstrates that the combination of data sources available for buildings enable advanced data analysis. This largely increases the understanding of buildings and their behavioural patterns. A more efficient building operation and a reduction of energy usage can be achieved with this knowledge