Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool

Schmid, Sanny; Gerostathopoulos, Ilias; Prehofer, Christian; Bureš, Tomáš

doi:10.1109/seams.2017.20

Cited by 30 publications

(30 citation statements)

References 9 publications

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“…Immediately apparent is the relatively high risk in two vibrantly orange areas near Hardbrücke 16 and Langstrasse 17 . These areas are, by a wide margin, the most dangerous in Zürich and the magnitude of their risk makes visual risk inspection throughout the rest of Zürich challenging.…”

Section: Stage 4-7: Risk Estimationmentioning

confidence: 99%

On cycling risk and discomfort: urban safety mapping and bike route recommendations

2019

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Bike usage in Smart Cities becomes paramount for sustainable urban development. Cycling provides tremendous opportunities for a more healthy lifestyle, lower energy consumption and carbon emissions as well as reduction of traffic jams. While the number of cyclists increase along with the expansion of bike sharing initiatives and infrastructures, the number of bike accidents rises drastically threatening to jeopardize the bike urban movement. This paper studies cycling risk and discomfort using a diverse spectrum of data sources about geolocated bike accidents and their severity. Empirical continuous spatial risk estimations are calculated via kernel density contours that map safety in a case study of Zurich city. The role of weather, time, accident type and severity are illustrated. Given the predominance of self-caused accidents, an open-source software artifact for personalized route recommendations is introduced. The software is also used to collect open baseline route data that are compared with alternative ones that minimize risk or discomfort. These contributions can provide invaluable insights for urban planners to improve infrastructure. They can also improve the risk awareness of existing cyclists' as well as support new cyclists, such as tourists, to safely explore a new urban environment by bike.

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Section: Stage 4-7: Risk Estimationmentioning

confidence: 99%

On cycling risk and discomfort: urban safety mapping and bike route recommendations

2019

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“…In this paper, we study the evolution of an STS through computer simulation, a powerful tool for testing alternative configurations prior to changing the real environment, which is particularly adequate to analyze the behavior of autonomous agents in a large-scale real setting (Luke et al 2005;Tsvetovat and Carley 2004;Wu et al 2015). We start from the CrowdNav smart traffic simulator, an exemplar from the selfadaptive systems literature (Schmid et al 2017) that simulates traffic scenarios in the middle-sized city of Eichstädt, in Germany, with 450 streets and 1200 intersections. We propose CrowdNavExt, 1 which introduces multiple types of navigation services as well as different ways of managing junctions, in line with the requirements model of Fig.…”

Section: The Crowdnavext Smart Traffic Simulatormentioning

confidence: 99%

“…To satisfy the requirement NS, two sub-requirements are assumed to be necessary: whenever a car starts a trip toward a destination, the car shall receive a route from the Central Navigation Service (NSD) and at least 80% of all the route suggestions given by the CNS are respected by the cars equipped with the CNS (RS). NSD can be met by either employing a self-adaptive navigation service (ANS) (Schmid et al 2017) or a static navigation service (SNS). In our simulator, each car relies on a navigation service to determine its route from origin to destination: 90% of the vehicles use their personal navigation service (the default routing algorithm of the simulator), while the remaining 10% are smart cars that can use a centralized navigation service.…”

Section: Fig 3 a Requirements Model For The Smart Traffic Simulationmentioning

confidence: 99%

Requirements-driven evolution of sociotechnical systems via probabilistic reasoning and hill climbing

2019

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Sociotechnical systems (STSs) are defined by the interaction between technical systems, like software and machines, and social entities, like humans and organizations. The entities within an STS are autonomous, thus weakly controllable, and the environment where the STS operates is highly dynamic. As a result, the design artifacts that represent the requirements of an STS, such as requirements models, may end up being invalid when the system operates, for the autonomous entities do not comply with the requirements, or the environment changes. In this paper, we present a framework that uses runtime execution data to support the runtime validation of requirements models and to guide the evolution of an STS. We propose two types of evolution: (i) manual: the analyst uses Bayesian inference to discover which assumptions in a requirements model are invalid and manually adjusts the system or its model; and (ii) automated: requirements are iteratively revised by an hill climbing algorithm searching for requirements that maximize the achievement of the stakeholders' objectives. We evaluate the effectiveness of different revision heuristics on a smart traffic simulation applied to an exemplar from the self-adaptive systems literature. The results show that our heuristics, informed by runtime execution data, outperform standard uninformed heuristics, in terms of convergence speed, solution quality, and stability. Moreover, the algorithms show good resilience to noise introduced into the execution data.

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“…As a final example of the use of Big Data analytics in a software engineering context (software design), consider the paper by Schmid and co-authors [43]. They look at how to make large-scale software-intensive distributed systems selfadaptive.…”

Section: Self-adaptive Systemsmentioning

confidence: 99%

Big Data and software engineering: prospects for mutual enrichment

Arndt

2017

Iran J Comput Sci

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Software engineering has evolved over the last 50 years, initially as a response to the so-called software crisis (the problems that organizations had producing quality software systems on time and on budget) of the 1960s and 1970s. Software engineering (SE) has been defined as "the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software". Software engineering has developed a number of approaches to areas such as software requirements, software design, software testing, and software maintenance. Software development processes such as the waterfall model, incremental development, and the spiral model have been successfully applied to produce high-quality software on time and under budget. More recently, agile software development has gained popularity as an alternative to the more traditional development methods for development of complex systems. Within the last decade or so, advances in technologies such as mobile computing, social networks, cloud computing, and the Internet of things have given rise to massive datasets which have been given the name Big Data (BD). Big Data has been defined as data with 3Vs-high volume, velocity, and variety. Big Data contains so much data that low probability events are captured in the data. These events can be discovered using analytics methods and turned into actionable intelligence which can be used by businesses to gain a competitive advantage. Unfortunately, the very scale of BD often renders inadequate SQL-based relational database systems which have formed the backbone of data intensive systems for the last 30 years, requiring new NoSQL technologies to be effective. In this paper, we will explore how well-established SE technology can be adapted to support successful development of BD projects, as well as how BD techniques can be used to increase the utility of SE processes and techniques. Thus, BD and SE may mutually support and enrich each other.

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Self-Adaptation Based on Big Data Analytics: A Model Problem and Tool

Cited by 30 publications

References 9 publications

On cycling risk and discomfort: urban safety mapping and bike route recommendations

On cycling risk and discomfort: urban safety mapping and bike route recommendations

Requirements-driven evolution of sociotechnical systems via probabilistic reasoning and hill climbing

Big Data and software engineering: prospects for mutual enrichment

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