Volumen 19 (2019): Edición 4 (November 2019)

Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. In real world applications, the time intervals between elements are also very important. However, recent HUSP mining algorithms cannot extract sequential patterns with time intervals between elements. Thus, in this paper, we propose an algorithm for mining high utility sequential patterns with the time interval problem. We consider not only sequential patterns’ utilities, but also their time intervals. The sequence weight utility value is used to ensure the important downward closure property. Besides that, we use four time constraints for dealing with time interval in the sequence to extract more meaningful patterns. Experimental results show that our proposed method is efficient and effective in mining high utility sequential pattern with time intervals.

Agile methodologies are becoming popular in software development. Managers are required to understand project’s progress and product quality without development documents. During Agile practices of the teams and organizations, Agile project management tools are frequently used. The use of such tools leads to achieving speed and efficiency, affects the quality of the software. The quality of final product is mostly related with to project management. Accordingly, the paper provides brief comparative perspective about the popular project management tools for agile projects. 16 popular Agile project management tools have been presented helping agile developers to plan and manage their tasks in an efficient manner. Taiga, Axosoft, Agielan, Planbox are more appropriate for start-up projects. The most twitted and most appreciated tools are reported as Jira, Trello, and VersionOne. SpiraTeam by Inflectra and Pivotal Tracker are other pricing and popular agile tools, providing flexibility to Agile developers and increase collaboration among team members.

The paper presents a hybrid metaheuristic algorithm, including a Particle Swarm Optimization (PSO) procedure and elements of Tabu Search (TS) metaheuristic. The novel algorithm is designed to solve Flexible Job Shop Scheduling Problems (FJSSP). Twelve benchmark test examples from different reference sources are experimentaly tested to demonstrate the performance of the algorithm. The obtained mean error for the deviation from optimality is 0.044%. The obtained test results are compared to the results in the reference sources and to the results by a genetic algorithm. The comparison illustrates the good performance of the proposed algorithm. Investigations on the base of test examples with a larger dimension will be carried out with the aim of further improvement of the algorithm and the quality of the test results.

In this paper, we focus on two major problems in hard real-time embedded systems fault tolerance and energy minimization. Fault tolerance is achieved via both checkpointing technique and active replication strategy to tolerate multiple transient faults, whereas energy minimization is achieved by adapting Dynamic Voltage Frequency Scaling (DVFS) technique. First, we introduce an original fault-tolerance approach for hard real-time systems on multiprocessor platforms. Based on this approach, we then propose DVFS_FTS algorithm for energy-efficient fault-tolerant scheduling of precedence-constrained applications. DVFS_FTS is based on a list scheduling heuristics, it satisfies real-time constraints and minimizes energy consumption even in the presence of faults by exploring the multiprocessor architecture. Simulation results reveal that the proposed algorithm can save a significant amount of energy while preserving the required fault-tolerance of the system and outperforms other related approaches in energy savings.

With the rapid development of computer networks, more hosts are connected to the Internet where they could communicate with each other. The need for network service has exceeded the service capacity of the network, and the Quality of Service (QoS) is gradually declining. Based on existing Shortest Path First (SPF) algorithm, this paper proposes a new QoS required transmission path approach by considering the overhead balance of network resources. This paper uses the entropy granularity as the main line in the application of routing protocols. Firstly, it researches the optimization of routing algorithms for network load balancing resources, routing algorithms based on link traffic distributing weights, link weight optimization based on adaptive genetic algorithm and computational intelligence based on entropy granularity theory. This research proposes a method to apply entropy granularity to Open Shortest Path First (OSPF) routing, including the implementation of the method. After that, a case study is presented by using some examples.

5th Generation (5G) mobile system is expected to support the requirements of mission critical communications for ultra reliability and availability, and very low latency. With the development of messaging and data transfer in mobile networks, mission critical communication users see more and more potential in data communications. In this paper, we explore the capabilities of Multi-access Edge Computing (MEC) that appears to be a key 5G component, to provide short messaging service at the network edge. The provided use cases illustrate the capabilities for transferring mobile originating and mobile terminating short messages to and from mission critical mobile edge applications. The data model describes the service resource structure and the Application Programming Interface definitions illustrate how the mobile edge applications can use the service. Some implementation aspects related to behavioral logic of the network and applications are provided. The performance analysis enables estimation of latency introduced by the service.

This investigation deals with modeling and availability analysis of cluster-based system inflicted with software aging. Software aging is a phenomenon in which a software system shows performance degradation with time and finally results in software failures. To cope up with this phenomenon, rejuvenation is an innovative concept to recover from software failures. As failures occur, server has the option either to take essential rejuvenation with probability p or may opt for optional rejuvenation with complementary probability q. To achieve high availability of the system, the concept of clustering is also taken into consideration. In this study, restart, reboot and standby concept is used for reducing the downtime cost. The sensitivity analysis of different parameters on system availability has been examined numerically. By integrating clustering, software aging and rejuvenation, the researchers intended to increase the availability and decrease the down time.

Preliminary theoretical analyzes have been made, which show that heavy acceleration reduces comfort are available. This applies both to the acceleration when starting and stopping. High levels of acceleration are most common when braking. An analysis of passenger comfort based on accelerometer data and a survey was conducted. The speed of travel, the acceleration in the direction of accelerometer X axis, the rotation axis C have a significant influence on the degree of passenger discomfort. The results obtained complement and partly improve those from the available literature with a model using only one linear and one rotary axis, by which the discomfort is described with sufficient accuracy. A regression model has been obtained, which is a suitable basis for building a driver assistance system to provide passenger comfort in public road transport. A model of a “Driver Assistant” microprocessor system based on studies is proposed.