A Metamodel-Based Approach for Automatic User Interface Generation

Hafidz al Quran is the desire of every Muslim. Many Islamic educational institutions ranging from elementary schools to universities are vying to make the lessons of tahfidz al Quran as a flagship program. Hafidz al Quran is not easy, many people fail, forget, are tired, lazy, and even desperate. This is because the learning process is less innovative, monotonous, boring, inefficient, and unattractive to students. The purpose of this research was to conduct a systematic literature review on developing interactive applications especially for the learning of tahfidz al Quran. Data were collected by reviewing all articles related to interactive applications published between 2015 and 2021. In total, 19 articles from national and international journal were selected from Google Scholar and lens.org. Interactive applications are software that contains text, images, sounds, animations, and videos. The software is packaged into apps that can be installed and operated on Android smartphones, laptops, and computers. The results of this study examined the need for software, design, movie clip, and testing. It was found that several selected studies had shown that the development of interactive applications in the form of software, design, movie clip, and testing can be used as a learning strategy for tahfidz al-Quran to improve memorization. Keywords: interactive application, Tahfidz al Quran

Due to problems in correctly understanding user requirements the Information System (IS) development community have recognised the need to involve end-users in the development and maintenance of web applications. End-users perceive web applications through user interfaces (UIs) and commonly use sketches of UIs to express their requirements. Thus, it would be desirable to provide an end-user development methodology centred on UI modelling techniques. In this paper a visual modelling approach is presented to empower end-users in developing data intensive web applications starting from user interface descriptions. The modelling language follows a holistic approach by representing both static and behavioural information of a web application in one visual model. The visual model allows the specification of the look-and-feel of the application through mock-ups, and the user interactions through links, annotations, and widget references. End-users are guided during the modelling process by providing a summary view to manage the design of complex applications and a data model view to improve the quality of the generated applications.

Power, especially that consumed for storing data, and cooling costs for data centers have increased rapidly. The main applications running at data centers are data intensive applications such as large file servers or database systems. Recently, power management of the data intensive applications has been emphasized in the literature. Such reports discuss the importance of power savings. However, these reports lack research on power management models for the efficient use of data intensive applications' I/O behaviors. This paper proposes a novel energy efficient storage management system that monitors both application- and device-level I/O patterns at run time, and uses not only the device-level I/O pattern but also application level patterns. First, the design of the proposed model combined with such large data intensive applications will be shown. The key features of the model are i) classifying application-level I/O into four patterns using run-time access behaviors such as the length of idle time and read/write frequency, and ii) adopting an appropriate power-saving method-based on these application level I/O patterns. Next, the proposed method is quantitatively evaluated with typical data intensive applications such as file servers, OLTP, and DSS. It is shown that energy efficient storage management is effective in achieving large power savings compared with traditional approaches while an application is running.

So far, agent research can be broadly categorized into two areas: intelligent agents and mobile agents. Intelligent agent research has grown from a background of AI, humancomputer interaction, and autonomous systems, while mobile agents research stems from the areas of distributed computing and networked systems. Intelligent agents research addresses intelligent control, planning, domain modeling, user modeling, perception, interagent cooperation, and adaptivity. Intelligent agents can take on many different roles, ranging from representing a human user’s interests to actively collaborating with users and each other. Intelligent agents are being designed to tackle nearly every tedious, non-creative activity that people perform (e.g., Web searches), as well as many more abstract functions (e.g., entertainment). Major issues in intelligent agents research include knowledge representation and communication, architectures for intelligent autonomy, mixed-initiative behavior, collaboration techniques, and learning. Mobile agents address physical communication, agent mobility, tracking and locating mobile agents, security and reliability of agents, etc. Mobile agents are suited for optimizing communication ‐ moving the action towards the source of data ‐ and thereby avoiding the need for huge data transfers. This is beneficial in data-intensive applications such as data mining. Mobility can also improve reliability in certain cases. In the field of electronic commerce, mobile agents naturally reflect the characteristics of buyer and the seller, effectively playing the roles of traveling salesman or broker. They can also be applied for easier software customization, software distribution, and to represent disconnected users. The main issues are in security, providing the infrastructure on the Web (naming, locating, controlling, and messaging) and improving survivability. The biggest challenge for all new agent technology is developing successful applications. For this minitrack, we selected two characteristics of agents that can span the diverging areas of intelligent and mobile agents: mobility and communication. The accepted papers reflect the research issues described in this summary. The minitrack is organized in three sessions. The first session explores the coordination of multiple intelligent agents using dramatically different communication schemes. Andersson and Ygge present an efficient market-based approach to managing very large scale groups of agents engaged in coordination via producer/ consumer transactions. Their CoTree algorithm allows largescale markets to operate efficiently with sparse communication, rapid updates as agent preferences change, and minimal data caching requirements on massively distributed systems. In contrast, Nelson, Penner, and Soken discuss the Interaction Society model of coordination, in which relatively small-scale groups of knowledge-intensive agents collaborate using complex communication about discrete tasking and information requirements. Their model for inter-agent communication and interactions with human users is being applied to build a multi-agent advising system for coordinating military Search and Rescue operations. Finally, Barbuceanu discusses the role of obligations and interdictions in the context of coarse-grain agent interactions. By explicitly capturing social obligations and the costs associated with violating those constraints, he shows how rational agents can achieve coordinated behavior without necessarily sharing goals. The second session addresses Java-based mobile agent systems. The paper by Covaci, Busse and Zhang, describes how a mobile agent system can be used for network management. Their framework is based on existing solutions such as TMN, and can be extended to SNMP.

Data-intensive applications produce huge amounts of data that need to be stored, analyzed, and interpreted. A data grid serves as a cost-effective infrastructure for solving these data-intensive applications. Existing scheduling strategies are best suited for handling compute-intensive applications, although they lack in performance while handling data-intensive applications. In this work, a novel mechanism of incorporating cognitive science in a data grid is proposed for scheduling data-intensive workflows. A unique model is derived in which a cognitive engine (CE) is built into the middleware of the data grid. The intelligent agents present in the CE handle the request for data sets and use the LTP algorithm (learning, thinking, and perception) to effectively schedule the tasks using three phases. The CE also~finds a unique solution for placing data sets dynamically nearer to the execution site based on network resource considerations by reducing the~waiting time and data availability time for I/O-intensive jobs. The performance of the CE is validated by simulation and compared with that of existing scheduling strategies. The results of the simulation show that CE optimizes the data availability time, waiting time, data transfer time, and makespan.

Abstract: Processing and analyzing large volumes of data plays an increasingly important role in many domains of scientific research. We are developing a compiler that processes data intensive applications written in a dialect of Java and compiles them for efficient execution on distributed memory parallel machines. In this paper, we focus on the problem of generating correct and efficient communication for data intensive applications. We present static analysis techniques for 1) extracting a global reduction function from a data parallel loop, and 2) determining if a subscript function is monotonic. We also present a runtime technique for reducing the volume of communication during the global reduction phase. We have experimented with two data intensive applications to evaluate the efficacy of our techniques. Our results show that 1) our techniques for extracting global reduction functions and establishing monotonicity of subscript functions can successfully handle these applications, 2) significant reduction in communication volume and execution times is achieved through our runtime analysis technique, 3) runtime communication analysis is critical for achieving speedups on parallel configurations.

Abstract: Processing and analyzing large volumes of data plays an increasingly important role in many domains of scientific research. We are developing a compiler that processes data intensive applications written in a dialect of Java and compiles them for efficient execution on distributed memory parallel machines. In this paper, we focus on the problem of generating correct and efficient communication for data intensive applications. We present static analysis techniques for 1) extracting a global reduction function from a data parallel loop, and 2) determining if a subscript function is monotonic. We also present a runtime technique for reducing the volume of communication during the global reduction phase. We have experimented with two data intensive applications to evaluate the efficacy of our techniques. Our results show that 1) our techniques for extracting global reduction functions and establishing monotonicity of subscript functions can successfully handle these applications, 2) significant reduction in communication volume and execution times is achieved through our runtime analysis technique, 3) runtime communication analysis is critical for achieving speedups on parallel configurations.

A grid consists of high-end computational, storage, and network resources that, while known a priori, are dynamic with respect to activity and availability. Efficient scheduling of requests to use grid resources must adapt to this dynamic environment while meeting administrative policies. This paper discusses the necessary requirements of such a scheduler and proposes a framework that can administrate grid policies and schedule complex and data intensive scientific applications. We present early experimental results for proposed a framework that effectively utilizes other grid infrastructure such as workflow management systems and execution systems. These results demonstrate that proposed a framework can effectively schedule work across a large number of distributed clusters that are owned by multiple units in a virtual organization.KeywordsDirected Acyclic GraphGrid ResourceGrid EnvironmentSchedule SystemVirtual OrganizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Optimization of data intensive applications is affected greatly by the nature of the platform, the distributed file system, the co-location of data and programs, and the proximity of resources. Data intensive applications running on a public cloud have been shown to exhibit degraded performance compared to a private cluster. This performance degradation is as a result of the inefficient resource allocation adopted in a virtualized, volatile and multi-tenancy environment such as a cloud. The placement of virtual machines is critical for improving performance in geo-clouds environment. We address degradation in performance by designing a scheduling algorithm that dynamically distributes virtual machines based on the characteristics of the job, the network characteristics and the real time performance of the data centers. Our algorithm also finds the best node(s) to host virtual machines that will yield maximum resource utilization and minimize bandwidth consumption. Our results show that incorporating these characteristics in a resource scheduling algorithm increases the performance of data intensive application than known traditional scheduling.

Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, MalStone and CreditStone benchmarks on Hadoop and Sector data layers. Data collected for the Amazon EC2 and GCE clouds measure performance as the number of nodes is varied. This study shows that GCE is more efficient for data-intensive applications compared to Amazon EC2.

Data intensive applications are present in a wide range of domains such as computational science, multimedia signal processing and defense systems. Their particular characteristics in terms of high number of communication operations and data manipulations lead to the need of well-adapted design formalisms. This chapter advocates the combination of a repetitive structure modeling formalism and abstract clocks to specify data intensive applications. The resulting specifications consist of an expression of the potential parallelism inherent to described data-intensive algorithms, refined with environment constraints in the form of application component’s activation rates. The modeling concepts used here are already integrated to the UML Marte standard profile, which allows for a graphical modeling and analysis of real-time and embedded systems.

QoS-aware replica placement for data intensive applications

Parallel, distributed and network-based computing is a continuosly evolving field, driven by progress in microprocessor architecture and interconnection technology, as well as by the needs of computing- and data-intensive applications in science and engineering, and, more recently, in business. This field is currently undergoing a significant change, because of the development of multicore and manycore processors, GPUs, and FPGAs, which are the new building blocks of parallel architectures. At the other end of the parallel and distributed computing scenario, computational grids are far from being a mature infrastructure and are evolving toward cloud computing, to get a higher level of virtualization. The availability of programming models, algorithms and software tools capable of harnessing the processing power offered by the new technologies is a key issue to make them usable by application developers. This special issue provides a view of the efforts carried out in this direction. Barlas introduces an optimization approach for reducing data communication and load imbalance in medical image matching on Grids. Binzenhofer et al. present a distributed and scalable algorithm to monitor a p2p network. Cesario and Talia discuss the use of data mining models and services on Grid systems for analysis of large data repositories. Danelutto et al. describe a performance model for component-based applications with stream communication semantics running on Grids. Danese et al. describe a FPGA-based coprocessor to accelerate double precision floating point operations in high-performance applications. Gross and Marquardt introduce a graphical editor providing abstractions from base technology for user-friendly configuration of Ubiquitous Computing environments. The papers collected here are selected extended versions of papers presented at PDP 2007, the Fifteenth Euromicro Conference on Parallel, Distributed and Network-based Processing, held in Naples, Italy, in February 2007. The conference was organized by the Institute for High-Performance Computing and Networking (ICAR) of the Italian National Research Council (CNR) in collaboration with the Second University of Naples, the University of Naples Parthenope and the University of Naples Federico II. We thank the editors of Scalable Computing: Practice and Experience for providing us the opportunity of publishing this issue, the authors for their contributions, and the referees for their precious help in selecting good-quality papers. Pasqua D'Ambra Institute for High-Performance Computing and Networking (ICAR), CNR Naples, Italy pasqua.dambra@cnr.it Daniela di Serafino Department of Mathematics, Second University of Naples Caserta, Italy daniela.diserafino@unina2.it Mario Rosario Guarracino Institute for High-Performance Computing and Networking (ICAR), CNR Naples, Italy mario.guarracino@cnr.it Francesca Perla Department of Statistics and Mathematics for Economic Research, University of Naples Parthenope Naples, Italy francesca.perla@uniparthenope.it

We describe a step-by-step methodology for the design and construction of hypermedia applications and illustrate our approach using a small application. The Relationship Management Design (RMD) methodology begins with a data model of the application domain and proceeds through the design of the hypertext network, user interface and run-time dynamics finally concluding with the construction and testing of the target hypermedia system. Our ultimate objective is to use the RMD approach as the basis for the construction of computerized tools to support the design and development of hypermedia applications. >

The use of Model Driven Development (MDD) approach is increasing in industry. MDD approach raises the level of abstraction using models as main artifacts of software engineering processes. The development of model transformations is a critical step in MDD. Tasks for defining, specifying and maintaining model transformation rules can be complex in MDD. Model Transformation By Example (MTBE) approaches have been proposed to ease the development process of transformation rules. Starting from pairs of example models the transformation rules are derived semi-automatically.The aim of our approach is to derive the adaptation operations that must be implemented in a legacy model transformation to fulfill a new transformation requirement. An MTBE approach and a tool to develop and evolve ATL transformation rules have been developed. Our approach derives the transformations operations automatically using execution traceability data and models differences. The developed MTBE approach can be applied to evolve legacy model transformations. The tool can be used with endogenous and exogenous model to model transformations. The approach has been validated with several model transformations and the results have been collected. A real case study is introduced to demonstrate the usefulness of the tool.KeywordsModel driven developmentModel transformation developmentModel transformation by exampleModel transformation execution traceModel differences