The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

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Requirements Engineering A Good Practice Guide

What is knowledge? § is a theoretical or practical understanding of a subject or a domain. § is also the sim of what is currently known, and apparently knowledge is power. Those who possess knowledge are called experts. § Anyone can be considered a domain expert if he or she has deep knowledge and strong practical experience in a particular domain. § The human mental process is internal, and it is too complex to be represented as an algorithm § However, most experts are capable of expressing their knowledge in the form of rules for problem solving.

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Rule‐Based Expert Systems

In recent years, the rapid development of blockchain technology and cryptocurrencies has influenced the financial industry by creating a new crypto-economy. Then, next-generation decentralized applications without involving a trusted third-party have emerged thanks to the appearance of smart contracts, which are computer protocols designed to facilitate, verify, and enforce automatically the negotiation and agreement among multiple untrustworthy parties. Despite the bright side of smart contracts, several concerns continue to undermine their adoption, such as security threats, vulnerabilities, and legal issues. In this paper, we present a comprehensive survey of blockchain-enabled smart contracts from both technical and usage points of view. To do so, we present a taxonomy of existing blockchain-enabled smart contract solutions, categorize the included research papers, and discuss the existing smart contract-based studies. Based on the findings from the survey, we identify a set of challenges and open issues that need to be addressed in future studies. Finally, we identify future trends.

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Blockchain smart contracts: Applications, challenges, and future trends

A new framework that captures many of the design issues was presented in this paper and it comprises seven models: an object model, event model, observation model, naming model, time model, notification model, resource model. This paper discusses each of these models in detail. We discuss the some issues related to Internet-scale event-based infrastructure and give a speculation about the potential areas for research.

A Design Framework for Internet-scale Event Observation and Notification

Cloud Computing offers storage resources as well as network and computing resources to the organizations. This eliminates the high infrastructure cost for the organizations that are using these services as they can now dynamically pay for these services, i.e., pay per use model, which is followed by most of the cloud providers. As the organization does not locally host these resources, these are comparatively far easier to manage and use than the traditional infrastructural resources. As a result of these factors, the popularity of cloud computing is increasing continuously. But this transfer of data and applications to the cloud server also creates some challenges. It poses problems that must be dealt with properly to ensure a secure cloud computing environment. As more and more sensitive data is being uploaded on the cloud in the present scenario, the privacy and security concerns associated with the data is continuously increasing. To address this, issue the data is stored on the cloud in the encrypted form. Also, as the amount of data stored is usually tremendous, so an efficient search scheme is also necessary. So here, we deal with two significant aspects of cloud computing: Encryption and Searching. We are proposing a secure and efficient encryption scheme to encrypt the data stored in the cloud as well as the queries along with a multi-keyword search scheme to search over the encrypted cloud data.

Algorithm for Multi Keyword Search Over Encrypted Data in Cloud Environment

Alarm Diffusion Systems (ADS) have complex, exacting and critical requirements. In this work we aim to provide a software architecture perspective towards ADS. We look at both functional and quality requirements for an ADS and also ttempt to identify certain quality attributes specific to an ADS and attempted to provide a set of architectural tact ics o realise them. We also propose a Reference Architecture for designing such system s. We have provided ample examples to support our i nferences and take a deeper look at a case study of the Traffic Collision Avoidance System (TCAS) in aircrafts. MOTS-CLÉS : architecture, système de diffusion d’alarmes.

Roadmap architecturale pour la création d’un système de diffusion d’alarme

Multi-tenant architectural principle for cloud applications is a relatively new principle as compared to others such as multiuser and multi-instance. It allows to share a single instance of an application among multiple tenants with a high degree of customization. In this paper, we present a collection of patterns to design a multi-tenant application such that it can manage its tenants efficiently. The patterns described here are - multi-tenant, tenant manager, tenant observability, tenant separability and tenant elasticity. Each pattern covers the different aspects of systematic tenant management.

Patterns for Managing Tenants in a Multi-tenant Application

In today’s time, the number of IoT devices are increasing rapidly. We everyday hear about Amazon echo, Google Mini, Smart watches etc. These devices collect confidential data of a person and as most of these systems follow centralized architecture approach, So most of the data on internet is basically managed by some central authority or organization. Though these organizations have strict policy regarding data misuse or changing of data without consent but one can’t overlook the fact that these organizations have the ability to do so. Blockchain helps solve such problem as it is not managed by single party if somebody tries to change data in his Blockchain then the hash of the particular block will no longer match and the particular Blockchain will become invalid and other Blockchain will still be intact and therefore users data won’t be compromised. But due to high resource requirement, it becomes problem to run complete Blockchain node on all IoT devices mainly on low power or memory devices. In this paper, we have developed decentralized architecture leveraged from Blockchain technology coupled with an alternative centralized cloud architecture which is a classic client/server architecture with an underlying Blockchain at back-end support with smart contract application written in Solidity language at Ethereum platform. We implement this framework and show how this architecture prevent data misuse by using functionality of Blockchain without requiring the all IoT devices to actually run a Blockchain node. For an end-user experience, it will appear to be same as a normal web app and from the developers perspective, the smart contract application will have similar software design to current web apps thus allowing an easy transition for both of centralized and decentralized methods while still retaining the trust of decentralization.

A Blockchain Based Solution for Securing Data of IoT Devices

A software architect uses quality requirements to design the architecture of a system. However, it is essential to ensure that the system's final architectural design achieves the standard quality requirements. The existing architectural evaluation frameworks require basic skills and experience for practical usage, which novice software architects lack. 
We propose a framework that enables novice software architects to infer the system's quality requirements and tactics using the software architectural block-line diagram. The framework takes an image as input, extracts various components and connections, and maps them to viable architectural patterns, followed by identifying the system's corresponding quality attributes (QAs) and tactics. The framework includes a specifically trained machine learning model based on image processing and semantic similarity methods to assist software architects in evaluating a given design by a) evaluating an input architectural design based on the architectural patterns present in it, b) lists out the strengths and weaknesses of the design in terms of QAs, c) recommends the necessary architectural tactics that can be embedded in the design to achieve the lacking QAs. 
To train our framework, we developed a dataset of 2,035 architectural images from fourteen architectural patterns such as Client-Server, Microservices, and Model View Controller, available at this https URL. The framework achieves a Correct Recognition Rate of 98.71% in identifying the architectural patterns. We evaluated the proposed framework's effectiveness and usefulness by using controlled and experimental groups, in which the experimental group performed approximately 150% better than the controlled group. The experiments were performed as a part of the Masters of Computer Science course in an Engineering Institution.

Sumit Kalra

Papers

Algorithm for Multi Keyword Search Over Encrypted Data in Cloud Environment

Roadmap architecturale pour la création d’un système de diffusion d’alarme

Patterns for Managing Tenants in a Multi-tenant Application

A Blockchain Based Solution for Securing Data of IoT Devices

Training Software Engineers for Qualitative Evaluation of Software Architecture