A Formal Verification and Approach for Real-Time Databases

A Formal Verification and Approach for Real-Time Databases

Pedro Fernandes Ribeiro Neto (Universidad do Estado do Rio Grande do Norte, Brazil), Maria Lígia Barbosa Perkusich (Universidade Católica de Pernambuco, Brazil), Hyggo Oliveira De Almeida (Federal University of Campina Grande, Brazil) and Angelo Perkusich (Federal University of Campina Grande, Brazil)
Copyright: © 2009 |Pages: 28
DOI: 10.4018/978-1-60566-098-1.ch013


Real-time database-management systems provide efficient support for applications with data and transactions that have temporal constraints, such as industrial automation, aviation, and sensor networks, among others. Many issues in real-time databases have brought interest to research in this area, such as: concurrence control mechanisms, scheduling policy, and quality of services management. However, considering the complexity of these applications, it is of fundamental importance to conceive formal verification and validation techniques for real-time database systems. This chapter presents a formal verification and validation method for real-time databases. Such a method can be applied to database systems developed for computer integrated manufacturing, stock exchange, network-management, and command- and-control applications and multimedia systems. In this chapter, we describe a case study that considers sensor networks.
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This article discusses an approach to mining; that is, seeking interesting crime patterns in a type of census data — U.S. state data. With an increasing crime rate and enormous amounts of data being stored in crime databases, it is becoming increasingly important to discover knowledge about crime from databases; that is, mining crime databases. Several other trends in data mining applications also can be found in Chen and Liu (2005) and Hu et al. (2005) (the latter paper discusses usage of data mining in the clinical area). By mining data, we refer to a process of nontrivial extraction of implicit, previously unknown, and potentially useful information, such as knowledge rules, constraints, regularities, and so forth (Agrawal, Imielinski, & Swami, 1993).

The dataset used in this study was retrieved from www.geocities.com/adotsaha/NN/SOMinExcel.html) was used to generate a SOM graph to view multidimensional clusters on a regular two-dimensional grid.

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