A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

Historically, databases have been the most crucial issue in the study of information systems, and they constitute an essential part of all information management systems. Since, it complicated due to restricting the number of potential users, particularly non-expert database users who must comprehend the database structure to submit such queries. Natural language interface (NLI), the simplest method to retrieve information, is one possibility for interacting with the database. The transformation of a natural language query into a Structured Query (SQL) in a database is known as a "Natural Language Interface to Database" (NLIDB). This study uses NLIDB to handle the works performed under various aggregations with aggregation functions, a grouping phrase, and a possessing clause. This study carefully examines the numerous systematic aggregation approaches utilized in the NLIDB. This review provides extensive information about the many methods, including query-based, pattern-based, general, keyword-based NLIDB, and grammar-based systems, to extract data for a dissertation from a generic module for use in such systems that support query execution utilizing aggregations

Uncertainty in Quantitative Risk Analysis - Characterisation and Methods of Treatment

The fundamental problems related to uncertainty in quantitative risk analyses, used in decision making in safety-related issues (for instance, in land use planning and licensing procedures for hazardous establishments and activities) are presented and discussed, together with the different types of uncertainty that are introduced in the various stages of an analysis. A survey of methods for the practical treatment of uncertainty, with emphasis on the kind of information that is needed for the different methods, and the kind of results they produce, is also presented. Furthermore, a thorough discussion of the arguments for and against each of the methods is given, and of different levels of treatment based on the problem under consideration. Recommendations for future research and standardisation efforts are proposed

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Applications of Expert Systems in Transport

BACKGROUND Experienced judgement and specialist knowledge are essential to the proper specification, understanding and interpretation of data and computer analyses. The human expert has traditionally supplied this knowledge and judgement with the computer doing the necessary number-crunching. However, artificial intelligence (AI) research provides ways of embodying this knowledge and judgement within computer programs. Despite an early lead in the field, UK research and developmnent into AI techniques was held back in the 1970s when the then Science Research Council took the view that the 'combinatorial explosion' of possibilities would be an insurmountable obstacle to AI developent. But in America and Japan research continued, and the surge of interest in the 1980s has been a consequence of the 'Fifth Generation Computer' research programme initiated by Japan (Feigenbaum and McCorduck; 1984). This led in Europe to the ESPRIT programme of advanced technology research, and in the UK to the Alvey programme (Department of Industry, 1982). As a result, all sectors of industry have been encouraged to consider how such advanced technology can be applied, and the transport industry is no exception. This paper sets out to explain some of the relevant techniques in simple terms, and to describe a number of situations in which transport planning and operations might be helped through their use, illustrating this by reference to the pioneering work going on in transport applications in the USA, Britain and Australia

Analysis of plant construction accidents and loss estimation using insurance loss records

There are many risks and uncertainties in plant construction projects, because of their complexity, difficulty in loss prediction and size of construction being large. The risk management of plant construction projects should not be relied solely on experiences and intuition of the contractors or the construction managers as it has been in the past. Therefore, a new quantitative and empirical risk analysis is required, in order for the development of a risk assessment using risk indicators for the plant construction projects. This research used the insurance payout record from a global insurance company to reflect the actual quantitative loss in the risk assessment model for plant construction project. The researchers adopted the geographic information as well as construction information (construction phase and commissioning phase, schedule rate, total duration), as the independent variables, which found to be statistically significant in the analysis in this study. It was found that the relationship between damage ratio and the valid variables was statistically significant, and thus, the damage model is also statistically significant. This research suggests that the regression model containing such valid independent variables could be beneficial in terms of providing foundational guidelines for the plant construction project risk analysis

Object reational data base management systems and applications in document retrieval

http://deepblue.lib.umich.edu/bitstream/2027.42/96902/1/MBA_JayaramanaF_1996Final.pd

Reducing Unknown Risk:The Safety Engineers’ New Horizon

A significant gap exists between accident scenarios as foreseen by company safety management systems and actual scenarios observed in major accidents. The mere fact that this gap exists is pointing at flawed risk assessments, is leaving hazards unmitigated, threatening worker safety, putting the environment at risk and endangering company continuity. This scoping review gathers perspectives reported in scientific literature about how to address these problems. Safety managers and regulators, attempting to reduce and eventually close this gap, not only encounter the pitfalls of poor safety studies, but also the acceptance of ‘unknown risk’ as a phenomenon, companies being numbed by inadequate process safety indicators, unsettled debates between paradigms on improving process safety, and inflexible recording systems in a dynamic industrial environment. The immediacy of the stagnating long term downward major accident rate trend in the Netherlands underlines the need to address these pitfalls. A method to identify and systematically reduce unknown risks is proposed. The main conclusion is that safety management can never be ready with hazard identification and risk assessment.</p