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The pharmaceutical industry is on the brink of entering into the digital age, yet still suffers from fundamental misconceptions and outdated IT systems that inhibit its progress. Four key criteria are identified that have enabled labs to reach the post-modern stage, which are insights generation through advanced analytics, automatic communication through machine to machine interfaces, removal of boundaries for an open lab, and novel means of ensuring trust through automatic submissions. Further progress in these four areas will enable the pharmaceutical laboratory to enter the digital age. Unfortunately, historical roadblocks in the form of an application-centric mindset have so far stifled progress. However, initiatives that supported other industries on their path into the digital age are introduced and evidences for the benefits of the digital age are provided. These span from advanced analytics, data-centric architecture, metadata supported communication, knowledge assisted submissions, to digital maturity models. It is concluded that executives and lab staff within Pharma needs a transition to a data-centric world view to reap all the benefits of the digital age for faster, better, and cheaper drug development.

With the ever-growing usage of artificial intelligence in business decisions, organizations, and the day-to-day lives of the society at large, it is imperative that different forms of biases that human beings are privy to do not get trickled to the machine learning and artificial intelligence systems being developed and used in abundance.

Data provenance analysis has been used as an assistive measure for ensuring system integrity. However, such techniques are typically reactive approaches to identify the root cause of an attack in its aftermath. This is in part due to the fact that the collection of provenance metadata often results in a deluge of information that cannot easily be queried and analyzed in real time. This paper presents an approach for proactively reasoning about provenance metadata within the Automatic Cryptographic Data Centric (ACDC) security architecture, a new security infrastructure in which all data interactions are considered at a coarse granularity, similar to the Function as a Service model. At this scale, we have found that data interactions are manageable for the proactive specification and evaluation of provenance policies—constraints placed on provenance metadata to prevent the consumption of untrusted data. This paper provides a model for proactively evaluating provenance metadata in the ACDC paradigm as well as a case study of an electronic voting scheme to demonstrate the applicability of ACDC and the provenance policies needed to ensure data integrity.

We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French. For the cross-domain case, we address domain shift by integrating unsupervised in-domain data via contextualized string embeddings; and OCR errors by injecting synthetic OCR errors into the source domain and address data centric domain adaptation. We propose a general approach to imitate OCR errors in arbitrary input data. Our cross-domain as well as our in-domain results outperform several strong baselines and establish state-of-the-art results. We publish preprocessed versions of the French and Dutch Europeana NER corpora.

In contemporary business process management software, the context in which a process is executed is largely static. While the execution of the process itself may be flexible, on-the-fly changes to the context, i.e., physical or logical surroundings, are either limited or impossible. This paper presents concepts for enabling context switching at runtime for the object-aware process management paradigm. Such context switches are enabled at various granularity levels, such as shifting entire process instances to different systems, or migrating sub-processes between different parent processes. We further contribute the algorithms employed in our proof-of-concept implementation and discuss use cases in which context switching capabilities can be utilized. Implementing these advanced concepts helps showcase the maturity of data-centric BPM.

This paper presents the main features and the programming constructs of the DCEx programming model designed for the implementation of data-centric large-scale parallel applications on Exascale computing platforms. To support scalable parallelism, the DCEx programming model employs private data structures and limits the amount of shared data among parallel threads. The basic idea of DCEx is structuring programs into data-parallel blocks to be managed by a large number of parallel threads. Parallel blocks are the units of shared- and distributed-memory parallel computation, communication, and migration in the memory/storage hierarchy. Threads execute close to data using near-data synchronization according to the PGAS model. A use case is also discussed showing the DCEx features for Exascale programming.

In any security process the role of trust anchors and trust roots have a complex interaction and applying them to the transport domain where many different forms of trust relationship have to work together is going to be one of greatest of the problems to surmount. ITS and CAV are data centric information systems in which the provenance of the data in the system is key to the success of the system. Provenance and integrity are underpinnings of trust, but in CAV and ITS there is no a priori knowledge of the source and value of data. This means that the provenance of a signal or message from the infrastructure is unlikely to be tested in advance and the verification of trust needs to be applied on demand for each message. Determining trust distribution in the transport infrastructure is one of the biggest unanswered questions regarding the viability of CAV.

In recent years, artifact-centric paradigm as a data-centric approach to business process modeling has gained momentum. Compared to the traditional activity-centric paradigm that focuses on process control-flow and treats data as simple black boxes that act as input and output to these activities, the artifact-centric paradigm provides equal support to both the control-flow and data. Most of the existing process modeling is activity-centric, although the artifact-centric modeling enables higher process flexibility and reusability. This is mainly due to the existence of numerous notations, tools and technologies that provide increased support to activity-centric process modeling and execution. Therefore, this paper proposes a trace-based approach to transform artifact-centric process models into activity-centric process models and to analyse the consistency of transformed and base models. A case study is utilized to demonstrate the feasibility of the proposed approach.

Efficient replication technique is the fundamental building block of highly available fault-tolerant cloud system. There are various replication policies that can be implemented in Cloud architecture. Recently proposed cloud application, Cloud-TM, is a data centric middleware platform that aims to reduce operational and administrative cost of cloud application. Cloud-TM integrates GMU (Genuine Multiversion update-serializability partial data replication protocol) in its designing space to support genuine partial replication policy. GMU protocol maintains a tradeoff between consistency and performance. Its consistency semantics guarantees the correctness even in a complex workload. However there are various challenging issues in GMU protocol that are required to be discusses to open up future research scope. In this paper author intervenes in the various aspect of GMU protocol and find its research scope those have notable designing impact in Cloud Data platform.

Over the last decade, print and online publications have published a huge number of articles with proposals for the introduction and use of neural networks in existing business and research. And just two years ago, many leading IT companies showed the world already created smart applications in the field of neural networks, which indicates the uniqueness and relevance of this technology. This is easily explained by the fact that systems based on neural networks are able to perform complex business tasks more efficiently and cheaper than the people. While working with big data, the probability of error remains relatively low. Unlike humans, neural networks are more stable. With long-term high loads, the efficiency of solving problems by the neural network does not sag. Finally, neural networks make free the people from monotonous computing operations and enable creative implementation. However, the issue of choosing the most appropriate topology and type of neural networks remains extremely relevant. The best results are demonstrated by recurrent neural networks. This paper is devoted to the review and proposing on the use of recurrent neural networks in data-centric business. The tests and analysis of their results demonstrated the relevance of the use of modern architectures of artificial neural networks.

In the last few years, we have seen a significant increase both in the number and capabilities of mobile devices, as well as in the number of applications that need more and more computing and storage resources. Currently, in order to deal with this growing need for resources, applications make use of cloud services. This raises some problems, namely high latency, considerable use of energy and bandwidth, and the unavailability of connectivity infrastructures. Given this context, for some applications it makes sense to do part, or all, of the computations locally on the mobile devices themselves. In this paper we present Oregano, a framework for distributed computing on mobile devices, capable of processing batches or streams of data generated on mobile device networks, without requiring centralized services. Contrary to current state-of-the-art, where computations and data are sent to worker mobile devices, Oregano performs computations where the data is located, significantly reducing the amount of exchanged data.

Our world is getting evolved to smart world day by day. This smart world is being developed to make people life easier through the data generated by the smart devices. Data is the fuel that powers the smart world evolution, however, making things smart have its consequences. Smart devices are inherently vulnerable to cyber attacks, that’s why we are observing an increase in crimes related to cyber space comparing to physical space. To address these crimes, police of the future need to evolve as well and data will be at the center stage of this evolution. In this contribution we are proposing a data centric policing proposal for smart cities. We analyzed current and developing technologies and the opportunities they offered for smart policing for a smart world.

The case study methodology introduced in this chapter supplements the data centric approach on global value chains. The objective of this chapter is to introduce the characteristics of case study methodology, to consider its advantages and limitations, to mention applications of case study methodology in the literature, and to present the steps followed in analyzing the case studies. The case study analysis provides additional insights on the degree of integration of clothing manufacturing subsidiaries into global value chains.

The main objective of the paper is to present model-driven approach to development of Information Security System. We use data centric models, in which the main focus is on the data and we define a conceptual model of Information Security System architecture using the main information security concepts. Its construction is based on the domain analysis organized around the viewpoints “Information Security” and “Information Processing”. The meta-models based on these viewpoints concern different aspects of the data and data protection. They are based on the summary of our practical experience in information security activities. Then the conceptual model is transformed to system design model with the help of UML – class, activity and deployment diagrams that transform the conceptual model of system architecture into actual solution or physical system.

Novel smart devices are equipped with various sensors to capture context data. The Internet of Things (IoT) connects these devices with each other in order to bring together data from various domains. Due to the IoT, new application areas come up continuously. For instance, the quality of life and living can be significantly improved by installing connected and remote-controlled devices in Smart Homes. Or the treatment of chronic diseases can be made more convenient for both, patients and physicians, by using Smart Health technologies.

As cars are turning more and more into “computers on wheels,” the development foci for future generations of cars are shifting away from improved driving characteristics toward features and functions that are implemented in software. Classical decentralized electrical and electronic (E/E) architectures based on a large number of electronic control units (ECUs) are becoming more and more difficult to adapt to the extreme complexity that results from this trend. Moreover, the innovation speed, which will be dictated by the computer industry’s dramatically short product lifecycles, requires new architectural and software engineering approaches if the car industry wants to rise to the resulting multidimensional challenges. While classical evolutionary architectures mapped the set of functions that constitute the driving behavior into a coherent set of communicating control units, RACE (Reliable Control and Automation Environment) is an attempt to redefine the architecture of future cars from an information processing point of view. It implements a straightforward perception-control/cognition-action paradigm; it is data centric, striking a balance between central and decentralized control. It implements mechanisms for fault tolerance and features plug-and-play techniques for smooth retrofitting of functions at any point in a car’s lifetime.

There were two major bureaucracy reduction programs in Hungary that aimed at the digitization of public administration. The expected benefits of digitization are operations that are more efficient and of a higher level of services. Results of these programs are limited, partly due to the simple support of the existing processes with information technology. The paper proposes a data-centric process design methodology that builds on artifacts as a modeling concept. It is argued and shown based on an example that starting from the expected final artifact of a process and applying a systematic approach, better results can be achieved than with the traditional process-based modelling approach.

This paper highlights the importance of developing data-centric teaching-research skills of 21st-century teachers. It critically examines the literature related to the teaching-research nexus and learning analytics, and presents a model of the integration of these concepts, and their relevance to the teaching and learning process. This paper also presents the desirable skills set of learning analysts and identifies the analytic tools that they can use to carry out this role. Finally, this paper suggests the future direction of teacher training and professional development programs that will equip teachers with the right teaching-research skills and tools to help the millennial learners gain academic success. This study recommends that teachers need to be well informed about their students by having access to their data and acquiring the necessary competencies to perform appropriate data analysis. Furthermore, they are expected to engage with analytical tools and educational technologies in order to realize a more effective teaching and learning experience, and improve academic outcomes.

Thales Alenia Space has recently widely deployed user centered design process in their software product conception. This standard breaks today technology and data centric approach by integrating end-users all along the iterative design stages: context and usage understanding, end-user need specification, quick mockups and end-user validation. This paper is a return on experience. It describes and investigates a dirty UCD methodology relevance based on prototyping and user testing only (skipping user research first activities). This process is made to fit project which needs front-end requirements at day one.

According to the requirement of cooperative air defense, network of air defense weapon comprehensive control system is studied. The data centric publish/subscribe model can be used to distribute fire control data on demand and avoid redundant transmission, in order to avoid redundant transmission; At the same time, it can form loosely coupled and open architecture; Using Quality of Service (QoS) strategy can effectively control and manage network and hardware resources, and achieve high real-time and high reliability of data distribution. The DDS specification is applied to the air defense weapon integrated control system, and the structure of the weapon integrated control system based on DDS is constructed.

With the development of big data, a demand for data analysis keeps increasing. This requirement has prompted a need for data-aware task scheduling approach that can simultaneously schedule various tasks such as batched tasks and real-time tasks in a data center efficiently. To this end, we propose a hybrid task scheduling strategy coupled with data migration in data center. Firstly, we translate the task scheduling problem into task selection problem, and give methods of selecting batched tasks and real-time tasks respectively. Then the method for scheduling both batched tasks and real-time tasks is introduced in detail. Finally, we integrate data migration into the hybrid scheduling strategy. Experimental results show that, compared to the traditional FIFO algorithm, the proposed task scheduling strategy greatly improves the data locality and data migration performs very well on reducing the job execution time. Our algorithm also guarantees an acceptable fairness for tasks.

Smart city is a vision that aims to integrate multiple information and communication solutions to residents with essential services like smart parking inside the all streets. Today, the parking systems has been changed by new advances that are empowering urban communities to diminish levels of congestions altogether. Internet of Things (IoT) is also new advancement which helps in detection of vehicle occupancy and congestion by basic intelligence and computational capability to make a smart parking system. The main motivation of using IoT for parking is to collect the data easily for free parking slots. This work presents the prototype of IoT-based Real Time smart street Parking System (IoT-based RTSSPS) with accessibility of data to make it simpler for residents and drivers to locate a free parking slot at the streets. Firstly, this work presents the introduction of IoT for smart parking with technology backgrounds, challenges of accessing IoT and database. Secondly, this work presents the prototype design of IoT-based RTSSPS with architecture and algorithm. IoT-based RTSSPS architecture is divided into three parts IoT-based WSN centric smart street parking module, IoT-based data centric smart street parking module and IoT-based cloud centric smart street parking module with street parking algorithm, evaluation and future directions.

Rule discovery is a challenging but inevitable process in several data centric applications. The main challenges arise from the huge search space that needs to be explored, and from the noise in the data, which makes the mining results hardly useful. While existing state-of-the-art systems pose the users at the beginning and the end of the mining process, we argue that this paradigm must be revised and new rule mining algorithms should be developed to let the domain experts interact during the discovery process. We discuss how new systems that embrace this approach overcome current limitations and ultimately result in shorter time and smaller user effort for rule discovery.

Energy-efficient computing has increasingly come into focus of research and industry over the last decade. Ultra-low-power architectures are a requirement for distributed sensing, wearable electronics, Internet of Things and consumer electronics. In this paper, we present a dual-mode platform that includes an ultra-low power Cortex Arm M4 microcontroller coupled with a highly energy efficient multi-core parallel processor. The platform is designed to maximize the energy efficiency in sensors applications by exploiting the Cortex Arm M4 to achieve ultra-low power processing and power management, and enables the multi-core processor to provide additional computational power for near-sensor data centric processing (i.e. accelerating Convolutional Neural Networks for image classification) increasing energy efficiency. The proposed platform enhances the application scenarios where on-board processing (i.e. without streaming out the sensor data) enables intensive computation to extract complex features. The platform is geared towards applications with limited energy budget, as for example in mobile or wearable scenarios where the devices are supplied by a battery. Experimental results confirm the energy efficiency of the platform, demonstrate the low power consumption, and the benefits of combining the two processing engines. Compared to a pure microcontroller platform we provide a boost of 80× in terms of computational power when running general purpose code and a boost of 560× when performing convolutions. Within a reasonable power budged of 20 mW compatible to battery-operated scenarios the system can perform 345 MOPS of general purpose code or 1.5 GOPS of convolutions.

This paper presents the Gaspar data-centric framework to develop high performance parallel applications in Java. Our approach is based on data iterators and on the map pattern of computation. The framework provides an efficient data Application Programming Inter-face(API) that supports flexible data layout and data tiling. Data layout and tiling enable the improvement of data locality, which is essential to foster application scalability in modern multi-core systems. The paper presents the framework data-centric concepts and shows that the performance is comparable to pure Java code., (undefined), info:eu-repo/semantics/publishedVersion

Most of computing research has focused on the computing technologies themselves versus how full systems make use of them (e.g., memory fabric, interconnect, software, and compute elements combined). Technologists have largely failed to look at the compute system as a whole, instead optimizing subsystems mostly in isolation. The result, for example, is that systems are built where applications can only ask for a fixed multiple of data (e.g., 64-bytes from DRAM), even if what is required is far less. This is efficient from a hardware interface perspective, however, it results in consuming valuable bandwidth that is never utilized by the core; this hidden bandwidth is effectively dark to the system. The causes of dark bandwidth are systemic, built into the very core of our virtual memory abstractions and memory interfaces. Continued focus on newer, revolutionary memory technologies to improve surface performance characteristics without a systems focus on reducing data movement will simply push this problem off onto future systems. This paper examines the problem of dark bandwidth and offers a holistic approach to reduce overall data movement within future compute systems.

This work presents a paradigm shift and introduces a data-centric security architecture for the COMPOSE framework; a platform as a service and marketplace for the IoT. We distinguish our approach from classical device-centric approaches and outline architectural as well as infrastructural specifics of our platform. In particular, we describe how fine-granular and data-centric security requirements can be combined with static and dynamic enforcement to regain governance on devices and data without sacrificing the intrinsic openness of IoT platforms. We also highlight the power of our architecture, converting concepts such as data provenance and reputation into efficient, highly useful, and practically applicable complements.

Cross-platform mobile and web applications become increasingly popular among users. As users begin to use multiple devices, their behavior patterns start to migrate from single-device to multi-device scenarios, where multiple devices are used sequentially or complementary to interact with a user and gather context information. Application developers should follow these trends and focus on multi-device aspects of their applications but efficient and cost effective application development requires proven frameworks and architectures, which are missing in this field. In this paper we identify key aspects of context-aware multi-device device-independent applications that should be supported by such tools and we propose a new application architecture. The presented architecture and conclusions are based on experimental implementations of a sample context-aware device-independent multi-device application called LifeLog.

The computational complexity and the constantly increasing amount of input data for scientific computing models is threatening their scalability. In addition, this is leading towards more data-intensive scientific computing, thus rising the need to combine techniques and infrastructures from the HPC and big data worlds. This paper presents a methodological approach to cloudify generalist iterative scientific workflows, with a focus on improving data locality and preserving performance. To evaluate this methodology, it was applied to an hydrological simulator, EnKF-HGS. The design was implemented using Apache Spark, and assessed in a local cluster and in Amazon Elastic Compute Cloud (EC2) against the original version to evaluate performance and scalability.

The critical nature of many complex software-intensive systems calls for formal, rigorous architecture descriptions as means of supporting automated verification and enforcement of architectural properties and constraints. Model checking has been one of the most used techniques to automatically verify software architectures with respect to the satisfaction of architectural properties. However, such a technique leads to an exhaustive exploration of all possible states of the system, a problem that becomes more severe when verifying dynamic software systems due to their typical non-deterministic runtime behavior and unpredictable operation conditions. To tackle these issues, we propose using statistical model checking (SMC) to support the verification of dynamic software architectures while aiming at reducing computational resources and time required for this task. In this paper, we introduce a novel notation to formally express architectural properties as well as an SMC-based toolchain for verifying dynamic software architectures described in \(\pi \)-ADL, a formal architecture description language. We use a flood monitoring system to show how to express relevant properties to be verified. We also report the results of some computational experiments performed to assess the efficiency of our approach.

Software Reference Architecture (SRA), which is a generic architecture solution for a specific type of software systems, provides foundation for the design of concrete architectures in terms of architecture design guidelines and architecture elements. The complexity and size of certain types of software systems need customized and systematic SRA design and evaluation methods. In this paper, we present a software Reference Architecture Design process Framework (RADeF) that can be used for analysis, design and evaluation of the SRA for provisioning of Tools as a Service as part of a cloud-enabled workSPACE (TSPACE). The framework is based on the state of the art results from literature and our experiences with designing software architectures for cloud-based systems. We have applied RADeF SRA design two types of TSPACE: software architecting TSPACE and software implementation TSPACE. The presented framework emphasizes on keeping the conceptual meta-model of the domain under investigation at the core of SRA design strategy and use it as a guiding tool for design, evaluation, implementation and evolution of the SRA. The framework also emphasizes to consider the nature of the tools to be provisioned and underlying cloud platforms to be used while designing SRA. The framework recommends adoption of the multi-faceted approach for evaluation of SRA and quantifiable measurement scheme to evaluate quality of the SRA. We foresee that RADeF can facilitate software architects and researchers during design, application and evaluation of a SRA and its instantiations into concrete software systems.

This paper presents a planning-based approach for the enumeration of alternative data-centric workflows specified in ASASEL (Abstract State mAchineS Execution Language), which define the coordination of data and computation services for satisfying data requirements. The optimization of data-centric workflows is associated to the exploration of the parallelization of the workflow activities. We address the exploration of parallelism formalizing the enumeration problem in the DLV-K language. Together, our ASASEL language and enactment engine along with our enumeration approach provide the foundation for a highly flexible mechanism for managing data-centric workflows.

In the recent age of information, most of the applications are associated with external database states. The confidentiality of sensitive database information may be compromised due to the influence of sensitive attributes on insensitive ones during the computation by database statements. Existing language-based approaches to capture possible leakage of sensitive database information are coarse-grained and are based on the assumption that attackers are able to view all values of insensitive attributes in the database. In this paper, we propose a data-centric approach which covers more generic scenarios where attackers are able to view only a part of the attribute-values according to the policy. This leads to more precise semantic-based analysis which reduces false positives with respect to the literature.

Over the past 30 years, software developers have been conveniently taking advantage of hardware performance increase, giving little consideration to internal architecture changes of the hardware. However, hardware architectural changes like central processing unit will affect software architectures and can no longer be ignored. This is especially true for real-time applications, including computer games, which tend to push the limits of hardware and take the most advantage of available resources. By applying the concepts of concurrency, multithreading and multi-core Central Processing Unit (CPU) technology, this paper redefines the existing linear architecture of game engines as a generic concurrent and multi-core friendly architecture. Major game engine modules and their inter-dependencies are identified in order to design the new architecture. A sample game was developed to evaluate the performance of the proposed architecture. The comparison of the test results provided in this paper indicates noticeable improvements (5.1 % to 61.2 %) in the concurrent architecture over the conventional linear approach. User acceptance evaluation with several industry experts also showed encouraging feedback.

The experiment described in this chapter is performed to test our hypothesis that our formal model and contracting framework enables detection of programming errors at design time in data-centric Web services. Again, we use mutation testing techniques in order to measure the effectiveness of the data contract attached to services.

Biobanking has been in existence for many decades and over that time has developed significantly. Biobanking originated from a need to collect, store and make available biological samples for a range of research purposes. It has changed as the understanding of biological processes has increased and new sample handling techniques have been developed to ensure samples were fit-for-purpose.As a result of these developments, modern biobanking is now facing two substantial new challenges. Firstly, new research methods such as next generation sequencing can generate datasets that are at an infinitely greater scale and resolution than previous methods. Secondly, as the understanding of diseases increases researchers require a far richer data set about the donors from which the sample originate.To retain a sample-centric strategy in a research environment that is increasingly dictated by data will place a biobank at a significant disadvantage and even result in the samples collected going unused. As a result biobanking is required to change strategic focus from a sample dominated perspective to a data-centric strategy.

Software architecture recovery is a bottom-up process that aims at building high-level views that support the understanding of existing software applications. Many approaches have been proposed to support architecture recovery using various techniques. However, very few approaches are driven by the architectural styles that were used to build the systems under analysis. In this paper, we address the problem of recovering layered views of existing software systems. We re-examine the layered style to extract a set of fundamental principles which encompass a set of constraints that a layered system must conform to at design time and during its evolution. These constraints are used to guide the recovery process of layered architectures. In particular, we translate the problem of recovering the layered architecture into a quadratic assignment problem (QAP) based on these constraints, and we solve the QAP using a heuristic search algorithm. In this paper, we introduce the QAP formulation of the layering recovery and we present and discuss the results of the experimentation with the approach on four open source software systems.

In systems design, we generally distinguish the architecture and the protocol levels. In the context of privacy by design, in the first case, we talk about privacy architectures, which define the privacy goals and the main features of the system at high level. In the latter case, we consider the underlying concrete protocols and privacy enhancing technologies that implement the architectures. In this paper, we address the question that whether a given protocol conforms to a privacy architecture and provide the answer based on formal methods. We propose a process algebra variant to define protocols and reason about privacy properties, as well as a mapping procedure from protocols to architectures that are defined in a high-level architecture language., FPS - 7th International Symposium on Foundations \& Practice of Security, Nov 2014, Montreal, Canada. Springer

Non-uniform memory access (NUMA) is one of the main architectures of today’s high-performance server. The key feature of NUMA is the non-uniformity of access latency. Access from a processor to attached memory is faster, and it also reduces the possibility of causing contention on interconnect links and memory controller. Multithreaded programs may experience high memory latency without careful placement of data and thread. Thus, it is necessary to develop a tool to identify and help ameliorate NUMA problems. In this paper, we present a data-centric tool to analyze the performance of multithreaded programs on NUMA architectures and provide advices on how to improve the performance. This paper describes the design and implementation of the tool. The tool is evaluated on Linux using three benchmark applications, and the evaluation shows how this tool helps to identify costly variables and choose optimization methods. The result shows performance improvement of up to 51.92 %.

In this chapter, I propose an approach to architectures, which makes precise exactly what it means for an agent to ‘have’ an architecture, and allows us to establish properties of an agent expressed in terms of its architectural description. Using this approach, it is possible to verify whether two different agent programmes really have the same architecture or the same properties, allowing a more precise comparison of architectures and the agent programmes which implement them. I illustrate the approach with a number of examples which show both how to establish qualitative and quantitative properties of architectures and agent programmes, and how to establish whether a particular agent has a particular architecture. I focus on architectures of software agents, however, a similar approach can be applied to robots and other kinds of intelligent systems.

We live in a world of increasingly complex, interconnected, societal problems. Design Thinking (DT), as an academic concern, and amongst other disciplines, has been grappling with such problems since the 1970s in order to solve the problems facing humanity and the environment. Initially, this paper briefly introduces the discourse of design thinking before describing in reference to selected theory from the field of design thinking a brief account of the characteristics of complexity and indeterminacy within the design phases of researching, ideation and prototyping. This paper then examines the ways in which the practice of information architecture (information architecture, IA) operates in some very similar ways and how this view reframes an understanding of the practice of IA. The paper will then present three ‘illusions’ embedded in the current view of information architecture that we believe account for its misconception. The reframing of IA presented here has implications for the field of information architecture, its theory, its practice and the teaching thereof, but perhaps more importantly also for other fields of design that stand to gain enormous value from the application of the thinking, tools and techniques of IA to grapple with the complex problems of our time.

Integrating robotic systems into our everyday life needs that we prove that they will not endanger people, i.e. that they will behave correctly with respect to some safety rules. In this paper, we propose a validation toolchain based on a Domain Specific Language. This DSL allows to model the software architecture of a robot using a component-based approach. From these models, we provide tools to generate deployable components, as well as a two-step validation phase. This validation first performs a real-time analysis of the component architecture, leading to an evaluation of the software architecture schedulability. Then we can check the validity of some behavioral property on the components.

We present a set of practices for the development of data-centric information systems (IS’s). The practices are about the architectural envisioning of systems and model-driven analysis of their certain subsystems. The goal of the architectural envisioning is to quickly achieve a suitable (in terms of well-established patterns) decomposition of the system into relatively autonomous subsystems to facilitate better understanding of it as well as support its design, implementation, maintenance, and future evolution. The goal of the model-driven analysis is to treat models as first-class citizens to facilitate better understanding of the system as well as the automation of its development. The practices can be used in the context of different development methodologies.

Traditionally, database management systems (DBMSs) have been employed exclusively for data management in infrastructures supporting Ambient Assisted Living (AAL) systems. However, DBMSs provide other mechanisms, such as for security, dependability, and extensibility that can facilitate the development, use, and maintenance of AAL applications. This work utilizes such mechanisms, particularly extensibility, and proposes a database-centric architecture to support home-based healthcare applications. An active database is used to monitor and respond to events taking place in the home, such as bed-exits. In-database data mining methods are applied to model early night behaviors of people living alone. Encapsulating the processing into the DBMS avoids transferring and processing sensitive data outside of database, enables changes in the logic to be managed on-the-fly, and reduces code duplication. As a result, such an approach leads to better performance and increased security and privacy, and can facilitate the adaptability and scalability of AAL systems. An evaluation of the architecture with datasets collected in real homes demonstrated the feasibility and flexibility of the approach.

Alignment of application architecture to business architecture is a central problem in the design, acquisition and implementation of information systems in current large-scale information-processing organizations. Current research in architecture alignment is either too strategic or too software implementation-oriented to be of use to the practicing information systems architect. This paper presents a framework to analyze the alignment problem and operationalizes this as an approach to application architecture design given a business context. We summarize guidelines for application architecture design and illustrate our approach and guidelines with an example.

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we delve into the logical design of ETL scenarios. We describe a framework for the declarative specification of ETL scenarios with two main characteristics: genericity and customization. Moreover, we present a palette of several templates, representing frequently used ETL activities along with their semantics and their interconnection. Finally, we discuss implementation issues and we present a graphical tool, ARKTOS II that facilitates the design of ETL scenarios, based on our model.

Architectures depict design principles: paradigms that can be understood by all, allow thinking on a higher plane and avoiding low-level mistakes. They provide means for ensuring correctness by construction by enforcing global properties characterizing the coordination between components. An architecture can be considered as an operator A that, applied to a set of components B , builds a composite component A ( B ) meeting a characteristic property Φ . Architecture composability is a basic and common problem faced by system designers. In this paper, we propose a formal and general framework for architecture composability based on an associative, commutative and idempotent architecture composition operator ⊕ . The main result is that if two architectures A 1 and A 2 enforce respectively safety properties Φ 1 and Φ 2 , the architecture A 1 ⊕ A 2 enforces the property Φ 1 ∧ Φ 2 , that is both properties are preserved by architecture composition. We also establish preservation of liveness properties by architecture composition. The presented results are illustrated by a running example and a case study.