Abstract. Data in real-time databases has to be logically consistent as well as temporally consistent.The latter arises from the need to preserve the temporal validity of data items that reflect the state of the environment that is being controlled by the system. Some of the timing constraints on the transactions that process real-time data come from this need. These constraints, in turn, necessitate time-cognizant transaction processing so that transactions can be processed to meet their deadlines.This paper explores the issues in real-time database systems and presents an overview of the state of the art. After introducing the characteristics of data and transactions in real-time databases, we discuss issues that relate to the processing of time-constrained transactions. Specifically, we examine different approaches to resolving contention over data and processing resources. We also explore the problems of recovery, managing I/O, and handling overloads. Real-time databases have the potential to trade off the quality of the result of a query or a transaction for its timely processing. Quality can be measured in terms of the completeness, accuracy, currency, and consistency of the results. Several aspects of this trade-off are also considered.

In data publishing, the owner delegates the role of satisfying user queries to a third-party publisher. As the publisher may be untrusted or susceptible to attacks, it could produce incorrect query results. In this paper, we introduce a scheme for users to verify that their query results are complete (i.e., no qualifying tuples are omitted) and authentic (i.e., all the result values originated from the owner). The scheme supports range selection on key and non-key attributes, project as well as join queries on relational databases. Moreover, the proposed scheme complies with access control policies, is computationally secure, and can be implemented efficiently.

This paper summarizes the state of the real-time field in the areas of scheduling and operating system kernels. Given the vast amount of work that has been done by both the operations research and computer science communities in the scheduling area, we discuss four paradigms underlying the scheduling approaches and present several exemplars of each. The four paradigms are: static tabledriven scheduling, static priority preemptive scheduling, dynamic planning-based scheduling, and dynamic best efSort scheduling. In the operating system context, we argue that most of the proprietary commercial kernels as well as real-time extensions to time-sharing operating system kernels do not fit the needs of predictable realtime systems. We discuss several research kernels that are currently being built to explicitly meet the needs of real-time applications. I. INTRODUCTION Real-time systems are defined as those systems in which the correctness of the system depends not only on the logical result of computation, but also on the time at which the results are produced. Examples of this type of real-time system are command and control systems, process control systems, flight control systems, the Space Shuttle avionics system, future systems such as the space station, spacebased defense systems such as SDI, and large command and control systems. A majority of today's systems assume that much of this knowledge is available a priori, and hence are based on static designs which contribute to their high cost and inflexibility. The next generation hard real-time systems must be designed to be dynamic, predictable, and flexible. When activities have timing constraints, as is typical of real-time computing systems, scheduling these activities to meet their timing constraints is one major problem that comes to mind. However, as we show in Section I1 of this paper, in spite of an extensive literature on Manuscript received July 13, 1993.This material is.

Hard real-time systems require both functionally correa executions and results that are produced on time. This means that the task scheduling algorithm is an important component of these systems. In this paper, efficient scheduling algorithms based on heuristic functions are developed to schedule a set of tasks on a multiprocessor system. The tasks are characterized by worst case computation times, deadlines, and resources requirements. Starting with an empty partial schedule, each step of the search extends the current partial schedule with one of the tasks yet to be scheduled. The heuristic functions used in the algorithm actively direct the search for a feasible schedule, i.e., they help choose the task that extends the current partial schedule. Two scheduling algorithms are evaluated via simulation. For extending the current partial schedule, one of the algorithms considers, at each step of the search,aZZ the tasks that are yet to be scheduled as candidates. The second focuses its attention on a small subset of tasks with the shortest deadlines. The second algorithm is shown to be very effective when the maximum allowable scheduling overhead is fixed. This algorithm is hence appropriate for dynamic scheduling in real-time systems.

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