Temporal data, temporal data models, temporal data languages and temporal database systems.

The study of temporal database systems is relatively new in the field of computer science. Two developments have led to the present interest. The advances of the storage technology for large amounts of data and applications' requirements for time-dependent data have prompted our study of temporal databases. This thesis conducts a survey of the major research areas concerning temporal databases. Temporal data, taxonomies of temporal data models, temporal data languages, and temporal database systems are presented. It is argued here that future database systems should handle the temporal domain by an integrated temporal database system. By understanding the present technology and the need of temporal database systems, our research in the area of real-time temporal database systems can begin. It is the purpose of this thesis to provide the background information and research references of temporal database systems as a first step towards the real-time database system research. Real-time database systems are time-constrained and temporally constituted. Solutions in temporal database systems can contribute to the design of real-time military applications using temporal database computers.http://archive.org/details/temporaldatatemp00homdCaptain, United States Marine CorpsApproved for public release; distribution is unlimited

Advances in Real-Time Database Systems Research Special Section on RTDBS of ACM SIGMOD Record 25(1), March 1996.

A Real-Time DataBase System (RTDBS) can be viewed as an amalgamation of a conventional DataBase Management System (DBMS) and a real-time system. Like a DBMS, it has to process transactions and guarantee ACID database properties. Furthermore, it has to operate in real-time, satisfying time constraints imposed on transaction commitments. A RTDBS may exist as a stand-alone system or as an embedded component in a larger multidatabase system. The publication in 1988 of a special issue of ACM SIGMOD Record on Real-Time DataBases signaled the birth of the RTDBS research area -- an area that brings together researchers from both the database and real-time systems communities. Today, almost eight years later, I am pleased to present in this special section of ACM SIGMOD Record a review of recent advances in RTDBS research. There were 18 submissions to this special section, of which eight papers were selected for inclusion to provide the readers of ACM SIGMOD Record with an overview of current and future research directions within the RTDBS community. In this paper [below], I summarize these directions and provide the reader with pointers to other publications for further information. -Azer Bestavros, Guest Edito

Real-Time Databases: Issues and Applications (RTDB'96 Workshop Report)

This report summarizes the technical presentations and discussions that took place during RTDB'96: the First International Workshop on Real-Time Databases, which was held on March 7 and 8, 1996 in Newport Beach, California. The main goals of this project were to (1) review recent advances in real-time database systems research, (2) to promote interaction among real-time database researchers and practitioners, and (3) to evaluate the maturity and directions of real-time database technology

Real-Time Databases: Issues and Applications (RTDB'96 Workshop Report)

This report summarizes the technical presentations and discussions that took place during RTDB'96: the First International Workshop on Real-Time Databases, which was held on March 7 and 8, 1996 in Newport Beach, California. The main goals of this project were to (1) review recent advances in real-time database systems research, (2) to promote interaction among real-time database researchers and practitioners, and (3) to evaluate the maturity and directions of real-time database technology

Is Real-Time Mobile Content-Based Image Retrieval Feasible?

Content-based image retrieval (CBIR) is a method of searching through a database of images by using another image as a query instead of text. Recent advances in the processing power of smart phones and tablets, collectively known as mobile devices, have prompted researchers to attempt to construct mobile CBIR systems. Most of the research that has been conducted on mobile CBIR has focused on improving either its accuracy or its run-time, but not both simultaneously. We set out to answer the question: is real-time CBIR with manageable accuracy possible on current mobile devices? To find the answer to this question, we ran tests using a compiled database of 930 high-resolution images on both a desktop computer and a Nexus 7 tablet. These tests examined the relationship between image resolution, matching method, and image descriptor type on match time and accuracy. By scaling down the images before matching them, we were able to achieve a run-time on Android of less than 10 seconds while maintaining 60% accuracy on average. These results suggest that a mobile CBIR system can be developed with current technology that can sufficiently balance accuracy and run-time

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future

Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.Comment: In Proceedings of the 27th ACM SIGSPATIAL, International Conference on Advances in Geographic Information Systems (2019). arXiv admin note: substantial text overlap with arXiv:1906.0540

Design of Cognitive Radio Database using Terrain Maps and Validated Propagation Models

Cognitive Radio (CR) encompasses a number of technologies which enable adaptive self-programing of systems at different levels to provide more effective use of the increasingly congested radio spectrum. CRs have potential to use spectrum allocated to TV services, which is not used by the primary user (TV), without causing disruptive interference to licensed users by using appropriate propagation modelling in TV White Spaces (TVWS). In this paper we address two related aspects of channel occupancy prediction for cognitive radio. Firstly, we continue to investigate the best propagation model among three propagation models (Extended-Hata, Davidson-Hata and Egli) for use in the TV band, whilst also finding the optimum terrain data resolution to use (1000, 100 or 30 m). We compare modelled results with measurements taken in randomly-selected locations around Hull UK, using the two comparison criteria of implementation time and accuracy, when used for predicting TVWS system performance. Secondly, we describe how such models can be integrated into a database-driven tool for CR channel selection within the TVWS environment by creating a flexible simulation system for creating a TVWS database