XIS-mobile

Ribeiro, A. M. R.; Silva, Alberto Rodrigues da

doi:10.1145/2554850.2554926

Cited by 31 publications

(4 citation statements)

References 26 publications

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“…Furthermore, this survey constitutes a starting point for our ongoing research goals in the context of the Data Storm and MDD Lingo initiatives. Specifically, we intend to extend existing domain-specific modeling languages, like XIS [44] and XIS-Mobile [45] [46], and their MDE-based framework to support both the data modeling and data analytics of data-intensive applications, such as those researched in the scope of the Data Storm initiative [47]- [50].…”

Section: Discussionmentioning

confidence: 99%

Data Modeling and Data Analytics: A Survey from a Big Data Perspective

Ribeiro¹,

Silva²,

Silva³

2015

JSEA

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These last years we have been witnessing a tremendous growth in the volume and availability of data. This fact results primarily from the emergence of a multitude of sources (e.g. computers, mobile devices, sensors or social networks) that are continuously producing either structured, semi-structured or unstructured data. Database Management Systems and Data Warehouses are no longer the only technologies used to store and analyze datasets, namely due to the volume and complex structure of nowadays data that degrade their performance and scalability. Big Data is one of the recent challenges, since it implies new requirements in terms of data storage, processing and visualization. Despite that, analyzing properly Big Data can constitute great advantages because it allows discovering patterns and correlations in datasets. Users can use this processed information to gain deeper insights and to get business advantages. Thus, data modeling and data analytics are evolved in a way that we are able to process huge amounts of data without compromising performance and availability, but instead by "relaxing" the usual ACID properties. This paper provides a broad view and discussion of the current state of this subject with a particular focus on data modeling and data analytics, describing and clarifying the main differences between the three main approaches in what concerns these aspects, namely: operational databases, decision support databases and Big Data technologies.

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Section: Discussionmentioning

confidence: 99%

Data Modeling and Data Analytics: A Survey from a Big Data Perspective

Ribeiro¹,

Silva²,

Silva³

2015

JSEA

Self Cite

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“…Requirements specifications have been often documented according to a blended approach, i.e., by using natural language (or CNL) textual sentences complemented with graphical models. These models are represented with general-purpose modeling languages, such as UML [21] or SysML [52], or with more specific modeling languages, such as RML [53] or XIS* [54][55][56]. Although these models can be more precise than plain textual specifications, they still leave room for ambiguity and inconsistency; thus, they are usually considered as being semi-formal.…”

Section: Modeling Languagesmentioning

confidence: 99%

“…For these reasons, the data-specific models of RML (i.e., BDD and DD) are classified as P 2 E 3 N 2 S 2 (see Table 2). XIS* represents a set of modeling languages designed to model web and mobile applications in a platform-independent way, sharing a common structure with concrete languages like XIS-Mobile [54,55], XIS-Web [56], and XIS [57]. These languages have been implemented as UML Profiles and so their models can be considered UML extensions based on specific stereotypes.…”

Section: Modeling Languagesmentioning

confidence: 99%

Linguistic Patterns and Linguistic Styles for Requirements Specification: Focus on Data Entities

Silva

Savić

2021

Applied Sciences

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Requirements specification includes technical concerns of an information system and is used throughout its life cycle. It allows for sharing the vision of the system among stakeholders and facilitates its development and operation processes. Natural languages are the most common form of requirements representation, however, they also exhibit characteristics that often introduce quality problems, such as inconsistency, incompleteness, and ambiguousness. This paper adopts the notions of linguistic pattern and linguistic style and discusses their relevance to produce better technical documentation. It focuses on the textual specification of data entities, which are elements commonly referred to throughout different types of requirements, like use cases, user stories, or functional requirements. This paper discusses how to textually represent the following elements: data entity, attribute, data type, data entity constraint, attribute constraint, and even cluster of data entities. This paper shows concrete examples and supports the discussion with three linguistic styles, represented by a rigorous requirements specification language and two informal controlled natural languages, one with a more compact and another with a more verbose, expressive, and complete representation. We analyzed how other languages cope with the representation of these data entity elements and complemented that analysis and comparison based on the PENS classification scheme. We conducted a pilot evaluation session with nineteen professional subjects who participated and provided encouraging feedback, with positive scores in all the analyzed dimensions. From this feedback, we preliminarily conclude that the adoption of these linguistic patterns would help to produce better requirements specifications written more systematically and consistently.

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“…The adoption of such a language-oriented vision has motivated the construction of a large variety of DSLs. There are, for example, DSLs to build graphical user interfaces [4], to specify security policies [5], or to ease off mobile applications' prototyping [6].…”

Section: Introductionmentioning

confidence: 99%

Reverse engineering language product lines from existing DSL variants

Méndez-Acuña

Galindo

Combemale

et al. 2017

Journal of Systems and Software

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The use of domain-specific languages (DSLs) has become a successful technique to develop complex systems. In this context, an emerging phenomenon is the existence of DSL variants, which are different versions of a DSL adapted to specific purposes but that still share commonalities. In such a case, the challenge for language designers is to reuse, as much as possible, previously defined language constructs to narrow implementation from scratch. To overcome this challenge, recent research in software languages engineering introduced the the notion of language product lines. Similarly to software product lines, language product lines are often built from a set of existing DSL variants.In this article, we propose a reverse-engineering technique to ease-off such a development scenario. Our approach receives a set of DSL variants which are used to automatically recover a language modular design and to synthesize the corresponding variability models. The validation is performed in a project involving industrial partners that required three different variants of a DSL for finite state machines. This validation shows that our approach is able to correctly identify commonalities and variability.

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XIS-mobile

Cited by 31 publications

References 26 publications

Data Modeling and Data Analytics: A Survey from a Big Data Perspective

Data Modeling and Data Analytics: A Survey from a Big Data Perspective

Linguistic Patterns and Linguistic Styles for Requirements Specification: Focus on Data Entities

Reverse engineering language product lines from existing DSL variants

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