Tool-automation for supporting the DSL learning process

Recent technologies advances reduced significantly the effort needed to develop Domain Specific Languages (DSLs), enabling the transition to language oriented software development. In this scenario new DSLs are developed and evolve at fast-pace, to be used by a small user-base. This impose a large effort on users to learn the DSLs, while DSL designers can use little feedback to guide successive evolutions, usually just based on anecdotal considerations. We advocate that a central challenge with the proliferation of DSLs is to help users to learn the DSL and providing useful analyses to the language designers, to understand what is working and what is not in the developed DSL. In this position paper we sketch possible directions for tool-automation to support the learning processes associated with DSL adoption and to permit faster evolution cycles of the DSLs

Recommender Systems based on Linked Data

Backgrounds: The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources in the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies Recommender Systems (RS) that use Linked Data as a source for generating recommendations exploiting the large amount of available resources and the relationships among them. Aims: The main objective of this study was to propose a recommendation tech- nique for resources considering semantic relationships between concepts from Linked Data. The specific objectives were: (i) Define semantic relationships derived from resources taking into account the knowledge found in Linked Data datasets. (ii) Determine semantic similarity measures based on the semantic relationships derived from resources. (iii) Propose an algorithm to dynami- cally generate automatic rankings of resources according to defined similarity measures. Methodology: It was based on the recommendations of the Project management Institute and the Integral Model for Engineering Professionals (Universidad del Cauca). The first one for managing the project, and the second one for developing the experimental prototype. Accordingly, the main phases were: (i) Conceptual base generation for identifying the main problems, objectives and the project scope. A Systematic Literature Review was conducted for this phase, which highlighted the relationships and similarity measures among resources in Linked Data, and the main issues, features, and types of RS based on Linked Data. (ii) Solution development is about designing and developing the experimental prototype for testing the algorithms studied in this thesis. Results: The main results obtained were: (i) The first Systematic Literature Re- view on RS based on Linked Data. (ii) A framework to execute and an- alyze recommendation algorithms based on Linked Data. (iii) A dynamic algorithm for resource recommendation based on on the knowledge of Linked Data relationships. (iv) A comparative study of algorithms for RS based on Linked Data. (v) Two implementations of the proposed framework. One with graph-based algorithms and other with machine learning algorithms. (vi) The application of the framework to various scenarios to demonstrate its feasibility within the context of real applications. Conclusions: (i) The proposed framework demonstrated to be useful for develop- ing and evaluating different configurations of algorithms to create novel RS based on Linked Data suitable to users’ requirements, applications, domains and contexts. (ii) The layered architecture of the proposed framework is also useful towards the reproducibility of the results for the research community. (iii) Linked data based RS are useful to present explanations of the recommen- dations, because of the graph structure of the datasets. (iv) Graph-based algo- rithms take advantage of intrinsic relationships among resources from Linked Data. Nevertheless, their execution time is still an open issue. Machine Learn- ing algorithms are also suitable, they provide functions useful to deal with large amounts of data, so they can help to improve the performance (execution time) of the RS. However most of them need a training phase that require to know a priory the application domain in order to obtain reliable results. (v) A log- ical evolution of RS based on Linked Data is the combination of graph-based with machine learning algorithms to obtain accurate results while keeping low execution times. However, research and experimentation is still needed to ex- plore more techniques from the vast amount of machine learning algorithms to determine the most suitable ones to deal with Linked Data

Analyzing wsn-based iot systems using mde techniques and petri-net models?

4th Workshop on Model-Driven Engineering for the Internet-of-Things, 1st International Workshop on Modeling Smart Cities, and 5th International Workshop on Open and Original Problems in Software Language Engineering, STAF-WS 2020 -- 22 June 2020 through 26 June 2020 -- -- 1642832-s2.0-85095588831There are various computation components, operating systems, and firmware used in the development of the Internet of Things (IoT). This variety increases the structural complexity and development cost and effort of the IoT systems. Besides, analyzing and troubleshooting these systems are time-consuming, costly, and cumbersome. To address these problems, this study aims to provide a higher level of abstraction for analyzing and developing IoT systems using Model-driven Engineering techniques and Petri-net models. To this end, a Domain-specific modeling Language (DSML), called DSML4Contiki, was presented in our previous study for the development of Wireless Sensor Systems (WSN) based IoT systems. The current study extends DSML4Contiki by providing an automated mechanism to analyze the IoT system at the early design phase, resulting in a reduction of the number of errors in the system and iterations in the development process. This is achieved using model transformation rules to transform the domain models at a high level to both the target platform artifacts as well as Petri-net models. By applying k-boundedness property checking on the Petri-net models, different analyses (such as power consumption, bottlenecks, and first crashing node) are realized for WSN based IoT systems. To evaluate the proposed approach, the engineering of a smart fire detection system is considered as a case study. © 2020 CEUR-WS. All rights reserved