scholarly journals On Testing Data-Intensive Software Systems

2019 ◽  
pp. 129-148 ◽  
Author(s):  
Michael Felderer ◽  
Barbara Russo ◽  
Florian Auer
Keyword(s):  
Software Systems ◽  
Data Intensive ◽  
Testing Data

eResearch

2018 ◽  
Vol 14 (4) ◽  
pp. 44-63
Author(s):  
Jan C. Thiele
Keyword(s):  
Lessons Learned ◽  
Software Systems ◽  
Functional Requirements ◽  
Joint Research ◽  
Management Tools ◽  
Web Based ◽  
Data Intensive ◽  
Digital Service ◽  
Joint Research Project ◽  
Scientific Framework

Joint research projects in ecology typically aim to integrate scientific knowledge from various disciplines. This raises the request for collaboration technologies. As ecological research is data-intensive, it requires the management and exchange of large datasets, often with spatial reference. The demand for collaboration, data, and information management tools in science is addressed by the creation of digital service infrastructures, so-called eResearch Infrastructures, which are collections of typically web-based software systems. Here, an example eResearch infrastructure implemented for a joint research project is presented. It is described by the user stories, the derived functional requirements, and their implementation in software systems. This infrastructure followed an open-source paradigm with only two exceptions. Based on the lessons learned, recommendations for the future development of eResearch infrastructures and their embedment in an organizational, project, and scientific framework are derived.

Architecting Data-Intensive Software Systems

2011 ◽  
pp. 25-57 ◽  
Author(s):  
Chris A. Mattmann ◽  
Daniel J. Crichton ◽  
Andrew F. Hart ◽  
Cameron Goodale ◽  
J. Steven Hughes ◽  
...  
Keyword(s):  
Software Systems ◽  
Data Intensive

Knowledge Management via Human-Centric, Domain-Specific Visual Languages for Data-Intensive Software Systems

2021 ◽  
pp. 91-108
Author(s):  
John Grundy ◽  
Hourieh Khalajzadeh ◽  
Andrew J. Simmons ◽  
Humphrey O. Obie ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Software Systems ◽  
Visual Languages ◽  
Data Intensive ◽  
Domain Specific

scholarly journals Software Product Line Engineering for Robotic Perception Systems

2018 ◽  
Vol 12 (01) ◽  
pp. 89-107 ◽  
Author(s):  
Davide Brugali ◽  
Nico Hochgeschwender
Keyword(s):  
Autonomous Robots ◽  
Software Product Line ◽  
Product Line ◽  
Software Systems ◽  
Data Intensive ◽  
Robot Perception ◽  
Software Product ◽  
Robotic Perception ◽  
Difficult Challenge ◽  
Application Developers

Control systems for autonomous robots are concurrent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, taking into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. In various application domains, software product line (SPL) development has proven to be the most effective approach to face this kind of challenges. In a previous paper [D. Brugali and N. Hochgeschwender, Managing the functional variability of robotic perception systems, in First IEEE Int. Conf. Robotic Computing, 2017, pp. 277–283.] we have presented a model-based approach to the development of SPL for robotic perception systems, which integrates two modeling technologies developed by the authors: The HyperFlex toolkit [L. Gherardi and D. Brugali, Modeling and reusing robotic software architectures: The HyperFlex toolchain, in IEEE Int. Conf. Robotics and Automation, 2014, pp. 6414–6420.] and the Robot Perception Specification Language (RPSL) [N. Hochgeschwender, S. Schneider, H. Voos and G. K. Kraetzschmar, Declarative specification of robot perception architectures, in 4th Int. Conf. Simulation, Modeling, and Programming for Autonomous Robots, 2014, pp. 291–302.]. This paper extends our previous work by illustrating the entire development process of an SPL for robot perception systems with a real case study.

Adaptive Computation Paradigm in Knowledge Representation

2012 ◽  
pp. 142-156
Author(s):  
Marina L. Gavrilova
Keyword(s):  
Knowledge Representation ◽  
Software Systems ◽  
New Paradigm ◽  
Information System Design ◽  
Adaptive Computation ◽  
Data Intensive ◽  
Collaborative Environment ◽  
Adaptive Models ◽  
E Learning ◽  
Computational Sciences

The constant demand for complex applications, the ever increasing complexity and size of software systems, and the inherently complicated nature of the information drive the needs for developing radically new approaches for information representation. This drive is leading to creation of new and exciting interdisciplinary fields that investigate convergence of software science and intelligence science, as well as computational sciences and their applications. This survey article discusses the new paradigm of the algorithmic models of intelligence, based on the adaptive hierarchical model of computation, and presents the algorithms and applications utilizing this paradigm in data-intensive, collaborative environment. Examples from the various areas include references to adaptive paradigm in biometric technologies, evolutionary computing, swarm intelligence, robotics, networks, e-learning, knowledge representation and information system design. Special topics related to adaptive models design and geometric computing are also included in the survey.

Superposition of Power-Law Models for Hardware/Software System Reliability Data Analysis

2003 ◽  
Vol 10 (02) ◽  
pp. 121-130 ◽  
Author(s):  
Ming Zhao
Keyword(s):  
Embedded System ◽  
Power Law ◽  
Software Systems ◽  
Failure Behavior ◽  
Power Law Model ◽  
System Failures ◽  
Failure Data ◽  
Testing Data ◽  
Duane Model ◽  
New Type

One of the widely used NHPP models in reliability is the so-called power-law model, also known as the Duane model. A power-law model can be applied in analyzing failure data of both software and hardware systems. Nevertheless, the power-law model is no longer applicable to describe the failure behavior when a hardware/software embedded system is concerned since the failures can come from both hardware and software. How to analyze the failure data of this type is still a problem to study and a new type of model is needed to develop. In this paper, we consider the superposition of the power-law models (SPLM) as one candidate to describe the failure behaviors of hardware/software systems. The characteristics of SPLMs are thoroughly studied. It is shown that an SPLM has the intensity function that can be increasing, decreasing, increasing-then-decreasing or decreasing-then-increasing. Specifically, the identification method of the superposition of power-law processes is provided by using the TTT-plot technique. The TTT-transform of a superposition of power-law processes is DFR-like. Therefore, the conditional TTT-plot should present a convex pattern if the system failures follow a superposition process by a few power-law processes. This provides us an easy way to identify the model when the testing data is available.

Analyzing the Evolution of Database Usage in Data-Intensive Software Systems

2018 ◽  
pp. 208-240
Author(s):  
Loup Meurice ◽  
Mathieu Goeminne ◽  
Tom Mens ◽  
Csaba Nagy ◽  
Alexandre Decan ◽  
...  
Keyword(s):  
Software Systems ◽  
Data Intensive

Adaptive Computation Paradigm in Knowledge Representation

2009 ◽  
pp. 301-314
Author(s):  
Marina L. Gavrilova
Keyword(s):  
Knowledge Representation ◽  
Software Systems ◽  
New Paradigm ◽  
Information System Design ◽  
Adaptive Computation ◽  
Data Intensive ◽  
Collaborative Environment ◽  
Adaptive Models ◽  
E Learning ◽  
Computational Sciences

The constant demand for complex applications, the ever increasing complexity and size of software systems, and the inherently complicated nature of the information drive the needs for developing radically new approaches for information representation. This drive is leading to creation of new and exciting interdisciplinary fields that investigate convergence of software science and intelligence science, as well as computational sciences and their applications. This survey article discusses the new paradigm of the algorithmic models of intelligence, based on the adaptive hierarchical model of computation, and presents the algorithms and applications utilizing this paradigm in data-intensive, collaborative environment. Examples from the various areas include references to adaptive paradigm in biometric technologies, evolutionary computing, swarm intelligence, robotics, networks, e-learning, knowledge representation and information system design. Special topics related to adaptive models design and geometric computing are also included in the survey.

Adaptive Computation Paradigm in Knowledge Representation

2009 ◽  
pp. 3258-3271
Author(s):  
Marina L. Gavrilova
Keyword(s):  
Knowledge Representation ◽  
Software Systems ◽  
New Paradigm ◽  
Information System Design ◽  
Adaptive Computation ◽  
Data Intensive ◽  
Collaborative Environment ◽  
Adaptive Models ◽  
E Learning ◽  
Computational Sciences

The constant demand for complex applications, the ever increasing complexity and size of software systems, and the inherently complicated nature of the information drive the needs for developing radically new approaches for information representation. This drive is leading to creation of new and exciting interdisciplinary fields that investigate convergence of software science and intelligence science, as well as computational sciences and their applications. This survey article discusses the new paradigm of the algorithmic models of intelligence, based on the adaptive hierarchical model of computation, and presents the algorithms and applications utilizing this paradigm in data-intensive, collaborative environment. Examples from the various areas include references to adaptive paradigm in biometric technologies, evolutionary computing, swarm intelligence, robotics, networks, e-learning, knowledge representation and information system design. Special topics related to adaptive models design and geometric computing are also included in the survey.

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