ACTIVE DATABASE SYSTEMS (ADBS) can react to the occurrence of predefined events automatically by definition a collection of ACTIVE rules. One of the most important modules of ADBS is the rule scheduler, which has considerable impact on performance and efficiency of ADBS. The job of rule scheduler is the selection of a rule for execution from the set of ready for execution rules. In this paper, we propose a new approach based on learning automata to improve the rule scheduling performance in terms of average response time, response time variance, and throughput. Learning automata have been used to obtain better estimations for rule execution probabilities. The results of experimentations show that the performance of the proposed method outperforms the most effective existing rule scheduling method.

Today, data plays an essential role in all levels of human life, from personal cell phones to medical, educational, military and government agencies. In such circumstances, the rate of cyber-attacks is also increasing. According to official reports, data breaches exposed 4. 1 billion records in the first half of 2019. An information system consists of several components, which one of the most important them is the DATABASE. A DATABASE in addition to being a repository of data, acts as a common information bus between system components. For this reason, any attack on the DATABASE may disrupt the operation of other components of the system. In fact, DATABASE security is shared throughout the whole information system. The attack may carried out in various ways, such as data theft, damaging data, and privacy breach. According to the sensitivity of the stored data, DATABASE attack could lead to significant human and financial losses even at the national level. Among the different types of threats, since legitimate operator plays a key role in an information system, his/her threat is one of the most dangerous threats to the security and integrity of a DATABASE system. This type of cyber-attack occurs when an insider operator abuses his/her legal permissions in order to access unauthorized data. In this paper, a new performance-based authorization framework has been presented which is able to reduce the potential of insider threat in the DATABASE system. The proposed method insure that only authenticated operator performs authorized activities on the DATABASE objects. In the proposed framework, the access permission of the operator to a DATABASE table is determined using his/her performance and the level of sensitivity of the table. The value of the operator performance is updated periodically or when an abuse is detected, in order to protect access to the contents of a DATABASE as well as preserve the consistency, integrity, and overall quality of the data. Simulation results, using real dataset from a hospital information system, indicate that the proposed framework has effective performance for mitigating insider threats.

This study was conducted during summer and winter of 2018- 2019 in the agricultural research field of Shahid Chamran University. Experimental design was split- plot based on RCBD with three replications. The main plot was the type of agricultural system in three levels including conventional (Conv), organic (Org) and sustainable (Sust) (integrated between Conv and Org) and sup- plot was the type of pre- cultivated crop in sequence with wheat including cultivation of mung bean (M- W), corn (C- W), sesame (S- W) and fallow (F- W). Yield quantity (yield and its component) and quality (grain protein), an estimate of photosynthesis matter transfer index of wheat and soil organic carbon (SOC) after one double-cropping were measured. The result showed that the highest (545.04 g/m2) and the lowest (409.28 g/m2) seed yields were obtained in Conv and Org respectively. In contract, with the changing type of system from Conv to Org, grain protein was increased significantly (from 8.3 to 9.6 %). In addition, the highest (535.47 g/m2) yield of wheat was obtained from M- W double cropping. On the other hands the highest remobilization and current photosynthesis matter were obtained in the organic agricultural system with M- W and conventional with M- W double cropping. The situation of SOC showed that the highest (33.18 mg/g) SOC was obtained in the organic agricultural system with C- W double cropping. The reason for improving SOC in the organic and sustainable agricultural system was application of organic matter (compost and vermicompost) and crop residue MANAGEMENT. Totally, from the crop ecology point of view, sustainable agricultural method with a sequence of M- W was the most desirable system.

In this research, the data models of widely used social networks, their advantages and disadvantages, as well as the things that need to be taken into account for storing and retrieving social network data, have been reviewed and presented. The usage of social networks has grown significantly in the past few years, and the result of this growth is the production of a large amount of data. On the other hand, it is necessary to effectively store and analyze social network data for all kinds of businesses today. The types of common storage methods based on the relational data model are not scalable for large amounts of information and therefore are not optimal and efficient. As a result, the use of storage methods based on non-relational models along with relational models have become very common. In this research, a large dataset of Twitter social network data, which includes 1,581,468 tweets from posts sent by 300,000 Persian users of this social network, is stored in three DATABASEs: MySQL, MongoDB, and Redis, and the performance of each of them with several different queries has been examined and compared. In addition to measuring the time spent to perform operations on the data, the amount of space occupied by the DATABASEs was also examined. Considering the advantages and limitations associated with the structure of all three DATABASEs, from the obtained values, it can be concluded that if the data has a specific structure, a relational DATABASE like MySQL is a good choice. If the data is unstructured or structured with the potential for rapid growth, NoSQL models will perform better. In addition, storing data temporarily and at high speed in key-value DATABASEs such as Redis, which store data in memory, are more suitable. Also, in terms of execution time, MongoDB executes queries very fast compared to MySQL and Redis, which is a proof that NoSQL DATABASEs show better performance and scalability for most operations in large datasets.

This paper presents behavioral aspect of the spatio-temporal DATABASEs for managing and querying moving objects. The objectives of this paper are to evaluate the current states of the Object-Relational DATABASE MANAGEMENT System and extend the selected system in such a way to support modeling and querying moving objecs. More than thirty five paprameters have been identified to evaluate the existing DBMSs namely; Oracle10g, DB2, MS SQL Server, MySQL, and Postgre SQL. Spatial and Temporal data models are assessed and the needs for a novel spatio-temporal data model have been recognized. It is concluded that the existing DBMSs lacks the required capabilities for handling moving objects. The optimum platform for extending the spatiotemporal capabilities is identified as Oracle10g. The developed extension supports spatio-temporal objects that change position or extent continuously as well as all pure spatial and temporal data types. In this research an extension to the selected DBMS has been developed using C++ language that supports moving objects. In order to examin the developed system, the querries posed by fleet MANAGEMENT companies are selected. The results have been evaluated and showed the superior functionalities of the system. The resulted system supports a wide set of object methods that extend Oracle’s query language PL/SQL with spatio-temporal semantics. Throgh many practical examples, it has been shown that applications such as vehicle tracking can be handled more efficiently, as temporal queries can be answered directly. This research concerned only 2-Dimensional spatial objects as well as the change and motion of such geometries in the 2D Cartesian plane.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an ACTIVE fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.