PropBase QueryLayer: a single portal to UK physical property databases

Until recently, the delivery of geological information for industry and public was achieved by geological mapping. Now pervasively available computers mean that 3D geological models can deliver realistic representations of the geometric location of geological units, represented as shells or volumes. The next phase of this process is to populate these with physical properties data that describe subsurface heterogeneity and its associated uncertainty. Achieving this requires capture and serving of physical, hydrological and other property information from diverse sources to populate these models. The British Geological Survey (BGS) holds large volumes of subsurface property data, derived both from their own research data collection and also other, often commercially derived data sources. This can be voxelated to incorporate this data into the models to demonstrate property variation within the subsurface geometry. All property data held by BGS has for many years been stored in relational databases to ensure their long-term continuity. However these have, by necessity, complex structures; each database contains positional reference data and model information, and also metadata such as sample identification information and attributes that define the source and processing. Whilst this is critical to assessing these analyses, it also hugely complicates the understanding of variability of the property under assessment and requires multiple queries to study related datasets making extracting physical properties from these databases difficult. Therefore the PropBase Query Layer has been created to allow simplified aggregation and extraction of all related data and its presentation of complex data in simple, mostly denormalized, tables which combine information from multiple databases into a single system. The structure from each relational database is denormalized in a generalised structure, so that each dataset can be viewed together in a common format using a simple interface. Data are re-engineered to facilitate easy loading. The query layer structure comprises tables, procedures, functions, triggers, views and materialised views. The structure contains a main table PRB_DATA which contains all of the data with the following attribution: • a unique identifier • the data source • the unique identifier from the parent database for traceability • the 3D location • the property type • the property value • the units • necessary qualifiers • precision information and an audit trail Data sources, property type and units are constrained by dictionaries, a key component of the structure which defines what properties and inheritance hierarchies are to be coded and also guides the process as to what and how these are extracted from the structure. Data types served by the Query Layer include site investigation derived geotechnical data, hydrogeology datasets, regional geochemistry, geophysical logs as well as lithological and borehole metadata. The size and complexity of the data sets with multiple parent structures requires a technically robust approach to keep the layer synchronised. This is achieved through Oracle procedures written in PL/SQL containing the logic required to carry out the data manipulation (inserts, updates, deletes) to keep the layer synchronised with the underlying databases either as regular scheduled jobs (weekly, monthly etc.) or invoked on demand. The PropBase Query Layer’s implementation has enabled rapid data discovery, visualisation and interpretation of geological data with greater ease, simplifying the parameterisation of 3D model volumes and facilitating the study of intra-unit heterogeneity

Experimental Demonstration and Results of Cross-layer Monitoring Using OpenNOP: an Open Source Network Observability Platform

Ensuring the smooth operation and optimal performance of communication networks requires continuous moni- toring of key network elements. Network operators can detect and prevent potential issues by monitoring various real-time network parameters. This paper proposes and presents results from the implementation of a cross- layer monitoring system for OpenROADM-compliant optical transport networks using an open source network observability platform called OpenNOP, and for the first time includes simultaneous optical layer and transport layer metrics. It leverages open source tools as a cost-effective and efficient solution for network monitoring and management. OpenNOP collects and analyzes data from various network layers, including physical, data link, network, and transport layers. OpenNOP can also ingest status and log information. This data is all stored in a common time-series database. The results show that OpenNOP can provide comprehensive network visibility and effective cross-layer monitoring of OpenROADM-based networks

ScienceDirect 2013 AASRI Conference on Parallel and Distributed Computing and Systems 5-Layered Architecture of Cloud Database Management System

Abstract Cloud Database Management System is a new emerging concept recently introduced in the world. In Cloud the concept of Standard architecture of Cloud Database Management System is not yet been implemented. In this paper we are proposing a framework for 5-layered architecture in cloud database management system. First layer introduced is the External Layer, this layer is closest to the user, in which manageability, providing transparency and security are the important issue that should be considered. Second layer is the Conceptual Middleware Layer, as there are heterogeneous databases and clouds are available in the market, so here interoperability is the major issue. Third layer is the Conceptual Layer in which programming techniques, transaction management, query processing and optimization are the issues that should be considered. Forth layer is the Physical Middleware Layer, as there are various platforms available so here also, interoperability between various platforms are the biggest issue and the last layer is the Physical Layer in which how data can be stored so that it can be easily accessible without so much overhead so here data security, storage, backup, load balancing, partitioning, scaling, elasticity, fault tolerance and replication are the important issues that should be considered

ScienceDirect 2013 AASRI Conference on Parallel and Distributed Computing and Systems 5-Layered Architecture of Cloud Database Management System

Abstract Cloud Database Management System is a new emerging concept recently introduced in the world. In Cloud the concept of Standard architecture of Cloud Database Management System is not yet been implemented. In this paper we are proposing a framework for 5-layered architecture in cloud database management system. First layer introduced is the External Layer, this layer is closest to the user, in which manageability, providing transparency and security are the important issue that should be considered. Second layer is the Conceptual Middleware Layer, as there are heterogeneous databases and clouds are available in the market, so here interoperability is the major issue. Third layer is the Conceptual Layer in which programming techniques, transaction management, query processing and optimization are the issues that should be considered. Forth layer is the Physical Middleware Layer, as there are various platforms available so here also, interoperability between various platforms are the biggest issue and the last layer is the Physical Layer in which how data can be stored so that it can be easily accessible without so much overhead so here data security, storage, backup, load balancing, partitioning, scaling, elasticity, fault tolerance and replication are the important issues that should be considered

Query management in a sensor environment

Traditional sensor network deployments consisted of fixed infrastructures and were relatively small in size. More and more, we see the deployment of ad-hoc sensor networks with heterogeneous devices on a larger scale, posing new challenges for device management and query processing. In this paper, we present our design and prototype implementation of XSense, an architecture supporting metadata and query services for an underlying large scale dynamic P2P sensor network. We cluster sensor devices into manageable groupings to optimise the query process and automatically locate appropriate clusters based on keyword abstraction from queries. We present experimental analysis to show the benefits of our approach and demonstrate improved query performance and scalability