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Abstract Software designers are striving to create software that adapts to their users' requirements. To this end, the development of component-based interfaces that users can compound and customize according to their needs is increasing. However, the success of these applications is highly dependent on the users' ability to locate the components useful for them, because there are often too many to choose from. We propose an approach to address the problem of suggesting the most suitable components for each user at each moment, by creating a recommender system using intelligent data analysis methods. Once we have gathered the interaction data and built a dataset, we address the problem of transforming an original dataset from a real component-based application to an optimized dataset to apply machine learning algorithms through the application of feature engineering techniques and feature selection methods. Moreover, many aspects, such as contextual information, the use of the application across several devices with many forms of interaction, or the passage of time (components are added or removed over time), are taken into consideration. Once the dataset is optimized, several machine learning algorithms are applied to create recommendation systems. A series of experiments that create recommendation models are conducted applying several machine learning algorithms to the optimized dataset (before and after applying feature selection methods) to determine which recommender model obtains a higher accuracy. Thus, through the deployment of the recommendation system that has better results, the likelihood of success of a component-based application is increased by allowing users to find the most suitable components for them, enhancing their user experience and the application engagement. [ABSTRACT FROM AUTHOR]

Nowadays, mashups applications are growing in popularity. They are accessible by cross-device applications, supporting multiple forms of interaction in cloud environments. In general, mashups manage a huge amount of heterogeneous data from different sources and handle different kinds of users. In this respect, mashup User Interfaces are becoming one of the most important pieces in many kinds of current management systems, such as for certain geographic or environmental information systems working on the Internet. In this type of systems, the user interface plays a particular role due to the huge variety of components or apps that the users need to manage at the same time. However, currently, there has been scant attention paid to the management of the user’s interaction with mashups interfaces. This goal involves the need of having important, well-constructed tools and methods conducting the data acquisition process for managing properly: (a) the interaction over the mashup user interfaces, at the front-end side; (b) the storage of the interaction in relational databases; and (c) well-supported microservices structures handled in the cloud. The fact of having valuable and flexible data acquisition processes encourages the deployment of others important issues of the interaction management, i.e., data searching, data mining, marketing, security, accessibility, usability or traceability of interaction data, among others. In this article, we present a flexible Data Acquisition System capable of capturing the human-computer interactions performed by users over mashup (User) Interfaces with the aim of storing them in a relational database. Firstly, the morphology of traditional mashup applications, their specifications and the relevant information that surrounds an interaction have been studied. Thereupon, a data acquisition system that stores user interaction on mashup based on such specifications was constructed. To achieve that purpose, an architecture of microservices was also designed in the cloud to detect, acquire, and collect the interactions performed over this kind of interfaces. The whole process is ready for acquiring internal data of the information system as well as context information and location awareness. To validate the data acquisition system, some tests on empirical case studies have been developed. Efficiency and effectiveness have also been determined by evaluating the performance of the acquisition system during different load tests. Finally, in order to ensure the software quality, a continuous integration strategy for software development and an easy management of the code have been used, facilitating the software maintenance alongside the microservice architecture, where functionalities are well encapsulated. [ABSTRACT FROM AUTHOR]

In the past few years the use of IoT devices has grown exponentially. When it comes to working with them, we find a series of problems that are not easy to solve. On the one hand, the simple fact of communicating with those devices can be problematic since they can use different types of technologies regarding that communication. On the other hand, these types of devices usually aim to perform their function using the lowest possible energy, meaning they have certain constraints in terms of performance. Trying to work with these devices in high-availability environments becomes difficult because of those restraints. This paper introduces digital dice, a virtual representation of IoT devices and cyber-physical systems based on microservices that uses the standard established by the W3C, the Web of Things, as the underlying framework to declare its possible interactions. The article puts forward the different strategies that Digital Dice uses to mitigate the problems raised. Furthermore, we contrast the performance of Digital Dice with using the devices directly, demonstrating its advantages in a process that requires High Availability. [ABSTRACT FROM AUTHOR]

Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join (kNNJQ) or closest pairs queries (kCPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in Apache Sedona, using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed kNNJQ and kCPQ distributed algorithms are efficient, scalable, and robust in Apache Sedona. Finally, Sedona is also compared to other similar cluster computing systems, showing the best performance for kCPQ and competitive results for kNNJQ. [ABSTRACT FROM AUTHOR]

Current open information systems need formal semantic definitions to describe and handle information which is dealt within different interaction environments such as human-to-human, human-to-computer, and computer-to-computer interactions. This paper provides a semantic definition of the knowledge used by environmental management information systems. We describe an ontological model in UML class diagrams and present a formalization of the ontology in first-oder logic and encoded in description logic. A model transformation approach is also used to map the UML class diagram elements into web ontology language. A running example is presented to explain the ontological knowledge modeling of EMIS SOLERES cartography and satellite information. [ABSTRACT FROM AUTHOR]

Abstract: In the field of engineering, guidelines and computerisation are important to facilitate the resolution of complex problems. Engineers apply computer techniques of distributed problem solving (DPS) to design and simulation tasks, such as in industrial manufacture (e.g., simulation of aircraft, modelling of structures and mass customization). An important application of these and other computer-aided techniques is the design and manufacture of industrial greenhouses in south-eastern Spain. The importance of greenhouses in this region led to the establishment of European Standard UNE-EN 13031-1 for the design and construction of structures for commercial production. Computer-assisted techniques are helping to put this European Manufacturing Standard into effect. This article discusses how DPS techniques help to solve problems in industrial manufacturing processes, and presents the case of modelling and simulation of 3D-structures used in greenhouse construction. [Copyright &y& Elsevier]

Abstract: The Unified Modeling Language (UML) [OMG, Unified Modeling Language Specification, Version 2.0, Technical Report, Object Management Group http://www.omg.org/technology/documents/formal/uml.htm , 2005] provides system architects working on analysis and design (A&D) with one consistent language for specifying, visualizing, constructing, and documenting the artifacts of software systems, as well as for the business modeling. The user interface (UI), as a significant part of most applications, should be modeled using UML, and automatic CASE tools may help to generate UIs from UML designs. In this paper, we describe how to use and specialize UML diagrams in order to describe the UIs of a software system based on WIMP (Windows, Icons, Menus and Pointers). Use case diagrams are used for extracting the main UIs. Use cases are described by means of user-interaction diagrams, a special kind of activity diagrams in which states represent data output actions and transitions represent data input events. Input and output interactions in the user-interaction diagrams help the designer to extract the UI components used in each UI. We obtain a new and specialized version of the use case diagram for the UI modeling (called UI diagram) and a class diagram for UI components—called UI-class diagram. The user-interaction, UI and UI-class diagrams, can be seen as the UML-based UI models of the system. Finally, UI prototypes can be generated from UI-class diagrams with CASE tool support. As case study of our technique, we will describe an Internet book shopping system. [Copyright &y& Elsevier]

• Coarse-grained components for building smart software at run-time. • Adapted trading service to build configurations of components. • Application of heuristics during the construction of software architectures. • Semantic and syntactic information used to score the possible solutions. • Adapted A * search algorithm to find the optimal solution at run-time. Smart architectures are increasingly being used in current software development. Smart user interfaces, smart homes, or smart buildings are becoming common examples in the new era of smart cities. Software architectures usually related to these domains need to be adapted and reconfigured at run-time, for example, to provide new services, react to user interaction, or due to changes decided from the business logic of the application. Component-based techniques are a suitable way to carry out this kind of adaptation, as dynamic reconfiguration operations can be applied to the architecture. In this paper, we address run-time generation of component-based applications, taking the abstract definitions of their architecture as a reference, in addition to a set of available components. The process calculates the best configuration of components from the abstract definition by applying a trading approach based on an adapted A* algorithm. This algorithm uses heuristics based on syntactic and semantic information obtained from the component definitions. A case study related to mashup user interfaces formed by coarse-grained components is also explained. In short, the results show the usefulness of heuristics and suitable execution times for building the best configurations. [ABSTRACT FROM AUTHOR]

With the development of IoT devices and web services, the objects of the real world are more interconnected, which allows applications to extend their characteristics in different fields, including industrial or home environments, among other possible examples such as health, trade, transport, or agriculture. However, this development highlights the challenge of interoperability, because devices are heterogeneous and use different communication protocols and different data formats. For this reason, we propose a model for point-to-point integration in three-layer IoT applications: (a) hardware, which corresponds to the physical objects (controller, sensor and actuator), (b) communication, which is the bridge that allows the exchange of data between a MQTT queue and REST web services, and (c) integration, which establishes a sequence of transactions to coordinate the components of the system. For this purpose, a metamodel, a graphic editor and a code generator have been developed that allow the developer to design IoT systems formed by heterogeneous components without having in-depth knowledge of every hardware and software platform. In order to validate our proposal, a smart home scenario has been developed, with a series of sensors and actuators that combined show a complex behavior. [ABSTRACT FROM AUTHOR]

Highlights from the article: Proper evolution of data concept is of vital importance, due Spatial Interaction Based on Microblog Data: A Case of As follows: (1) the data interface provided by the microblog Trajectory of microblog data can explore the spatial re- Elements on a canvas, (2) validating the pattern syntax, (3).

Efficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains. The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ). These types of join queries are characterized by a number of desired pairs (K) or a distance threshold (ε) between the components of the pairs in the final result, over two spatial datasets. Both are expensive operations, since two spatial datasets are combined with additional constraints. Given the increasing volume of spatial data originating from multiple sources and stored in distributed servers, it is not always efficient to perform DBJQs on a centralized server. For this reason, this paper addresses the problem of computing DBJQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports efficient processing of spatial queries in a cloud-based setting. We propose novel algorithms, based on plane-sweep, to perform efficient parallel DBJQs on large-scale spatial datasets in SpatialHadoop. We evaluate the performance of the proposed algorithms in several situations with large real-world as well as synthetic datasets. The experiments demonstrate the efficiency and scalability of our proposed methodologies. [ABSTRACT FROM AUTHOR]

Abstract: Spatial memory can be assessed by virtual reality-based tasks, some of them similar to those previously used in rodents. In this study a virtual place avoidance task was developed based on a rodent labyrinth to evaluate human spatial memory. The task required subjects to avoid in a virtual room a prohibited zone defined solely by distal cues, while they collected rewards. The size of the to-be-avoided area was used as an independent variable as well as the gender of the participants. Subjects displayed an adequate performance after a single 5-min trial, entering the prohibited area on very few occasions. Our results showed that the size of the prohibited zone can be used to adapt task difficulty to different populations. Moreover, males were faster than females. They collected more rewards and spent less time in the prohibited area. Data collected supports the spatial component of the task and the validity of this procedure for spatial memory assessment in human beings. [Copyright &y& Elsevier]

Integration of different cyber-physical systems involves a development process that takes into account some solutions for intercommunicating and interoperating heterogeneous devices. Each device can be managed as a thing within the Internet-of-Things concept by using web technologies. In addition, a “thing” can be managed as an encapsulated component by applying component-based software engineering principles. Based on this context, we propose a solution for integrating heterogeneous systems using a specific component-based technology. Specifically, we focus on enabling the connection of different types of subsystems present in smart home solutions. This technology enables interoperability by applying a homogeneous component representation that provides communication features through web sockets, and by implementing gateways in proprietary network connections. Furthermore, our solution eases the extension of these systems by means of abstract representations of the architectures and devices that form part of them. The approach is validated through an example scenario with different subsystems of a smart home solution. [ABSTRACT FROM AUTHOR]

Mashup user interfaces provides their functionality through the combination of different services. The integration of such services can be solved by using reusable and third-party components. Furthermore, these interfaces must be adapted to user preferences, context changes, user interactions and component availability. Model transformation is a useful mechanism to address this adaptation but normally these operations only focus on the functional requirements. In this sense, quality attributes should be included in the adaptation process to obtain the best adapted mashup user interface. This paper proposes a generic quality-aware transformation process to support the adaptation of software architectures. The transformation process has been applied in ENIA, a geographic information system, by constructing a specific quality model for the adaptation of mashup user interfaces. This model is taken into account for evaluating the different transformation alternatives and choosing the one that maximizes the quality assessments. The approach has been validated by a set of adaptation scenarios that are intended to maximize different quality factors and therefore apply distinct combinations of metrics. [ABSTRACT FROM AUTHOR]

Software architecture management, especially in component-based web user interfaces is important to enhance their run-time accessibility, dynamics and management. The cloud offers some excellent mechanisms for this kind of systems, since software can be managed remotely, easy availability of the resources is ensured and mass storage is possible. This article presents an infrastructure solution, based on the use of web services and cloud computing, for managing COTS-based architectures. [ABSTRACT FROM AUTHOR]

Remote sensing has been used in numerous environmental simulations with the aim of solving and improving many different kinds of problems, e.g., meteorology applications, soil quality studies, water resource exploration, and environmental protection. Besides, cellular automata have been widely used in the field of remote sensing for simulating natural phenomena over two-dimensional satellite images. However, simulations on Digital Elevation Models (DEM), or three-dimensional (3D) satellite images, are scarce. This paper presents a study of modeling and simulation of the weather phenomena of rainfall, water evaporation and groundwater flow in 3D satellite images through a new algorithm, developed by the authors, named RACA (RAinfall with Cellular Automata). The purpose of RACA is to obtain, from the simulation, numerical and 3D results related to the total cumulative flow and maximum level of water that allow us to make decisions on important issues such as analyzing how climate change will affect the water level in a particular area, estimating the future water supply of a population, establishing future construction projects and urban planning away from locations with high probability of flooding, or preventing the destruction of property and human life from future natural disasters in urban areas with probability of flooding. [ABSTRACT FROM AUTHOR]

• A fully reusable WoT microservice architecture for smart ports is provided. • The architecture provides real-time data processing for smart IoT and WoT. • Evaluation proves architecture's usefulness for smart port air quality monitoring. Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results. [ABSTRACT FROM AUTHOR]

Satellite image classification is an important technique used in remote sensing for the computerized analysis and pattern recognition of satellite data, which facilitates the automated interpretation of a large amount of information. Today, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but it is still necessary to improve their performance in terms of accuracy rate. On the other hand, over the last few decades, cellular automata have been used in remote sensing to implement processes related to simulations. Although there is little previous research of cellular automata related to satellite image classification, they offer many advantages that can improve the results of classical classification algorithms. This paper discusses the development of a new classification algorithm based on cellular automata which not only improves the classification accuracy rate in satellite images by using contextual techniques but also offers a hierarchical classification of pixels divided into levels of membership degree to each class and includes a spatial edge detection method of classes in the satellite image. [ABSTRACT FROM PUBLISHER]