Attribute-Based Architecture Styles

Klein, Mark; Kazman, Rick; Bass, Leonard J.; Carrière, S. Jeromy; Barbacci, Mario R.; Lipson, Howard F.

doi:10.1007/978-0-387-35563-4_13

Cited by 67 publications

(36 citation statements)

References 4 publications

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“…Pioneer work on the quantitative analysis of software systems at the architectural level includes Attribute-Based Architectural Styles (ABAS) [16]. In general, an architectural style includes the specification of types of basic architectural elements (e.g., pipe and filter) that can be used for specifying a software architecture, constraints on using these types of architectural elements, and patterns describing the data and control interaction among them.…”

Section: Background and Related Workmentioning

confidence: 99%

Quality Analysis of Dependable Systems: A Developer Oriented Approach

Zarras

Kloukinas

Issarny

2003

Lecture Notes in Computer Science

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Abstract. The quality of dependable systems (DS) is characterized by a number of non-functional properties (e.g., performance, reliability, availability, etc.). Assessing the DS quality against these properties imposes the application of quality analysis and evaluation. Quality analysis consists of checking, analytically solving, or simulating models of the system, which are specified using formalisms like CSP, CCS, Markov-chains, Petri-nets, Queuing-nets, etc. However, developers are usually not keen on using such formalisms for modeling and evaluating DS quality. On the other hand, they are familiar with using architecture description languages and object-oriented notations for building DS models. Based on the previous and to render the use of traditional quality analysis techniques more tractable, this paper proposes an architecture-based environment that facilitates the specification and quality analysis of DS at the architectural level.

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Section: Background and Related Workmentioning

confidence: 99%

Quality Analysis of Dependable Systems: A Developer Oriented Approach

Zarras

Kloukinas

Issarny

2003

Lecture Notes in Computer Science

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“…We therefore move directly to step 2, investigation and analysis. Klein et al [70] provide a set of sample ABAS, covering many common styles. Their Data Indirection style matches our architecture.…”

Section: Architectural Evaluationmentioning

confidence: 99%

Ranked Similarity Search of Scientific Datasets: An Information Retrieval Approach

Megler¹

2000

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In the past decade, the amount of scientific data collected and generated by scientists has grown dramatically. This growth has intensified an existing problem: in large archives consisting of datasets stored in many files, formats and locations, how can scientists find data relevant to their research interests? We approach this problem in a new way: by adapting Information Retrieval techniques, developed for searching text documents, into the world of (primarily numeric) scientific data. We propose an approach that uses a blend of automated and curated methods to extract metadata from large repositories of scientific data. We then perform searches over this metadata, returning results ranked by similarity to the search criteria. We present a model of this approach, and describe a specific implementation thereof performed at an ocean-observatory data archive and now running in production. Our prototype implements scanners that extract metadata from datasets that contain different kinds of environmental observations, and a search engine with a candidate similarity measure for comparing a set of search terms to the extracted metadata. We evaluate the utility of the prototype by performing two user studies; these studies show that the approach resonates with users, and that our proposed similarity measure performs well when analyzed using standard Information Retrieval evaluation methods. We performed performance tests to explore how continued archive growth will affect our goal of interactive response, developed and applied techniques that mitigate the effects of that growth, and show that the techniques are effective. Lastly, we describe some of the research needed to extend this initial work into a true "Google for data".

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“…Understanding the causes of architecture changes is important to help practitioners to understand the knowledge of the design decisions that lead to the architecture changes [3], and also allows researchers to devise means to prevent architecture knowledge vaporization and architecture degeneration [4]. The causes of architecture changes are regarded as an essential element of architectural design decision, which is a first-class entity to represent architecture [5], and are used to develop related methods to deal with specific architecture changes, for example, architects analyze due to what cause the property of an architecture is inhibited in order to transform the architecture to satisfy non-functional requirements [6]; architectural styles as analysis tools are used to analyze the causes of architecture changes, and in turn to predict the effect of the architecture changes [7]. Architectural knowledge vaporization (e.g., design decisions and causes of architecture changes) will lead to increased maintenance costs [5].…”

Section: Introductionmentioning

confidence: 99%