A case study of applying data mining to sensor data for contextual requirements analysis

Rook, Angela; Knauss, Alessia; Damian, Daniela; Thomo, Alex

doi:10.1109/aire.2014.6894855

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…However, the study of the variation due to the internal operation of the data mining algorithm is out of the scope of this work. For better understanding of this and other data mining algorithm we refer the reader to previous works Rook, 2014;Rook et al, 2014). In order to reduce this kind of threat, we quantitatively interpret our results using descriptive statistics for determine tendencies, dispersion and dependencies.…”

Section: Fig 13 Adaptation Response Time Per Uncertainty Scenario Rmentioning

confidence: 99%

“…However, the study of the variation due to the internal operation of the data mining algorithm is out of the scope of this work. For better understanding of this and other data mining algorithm we refer the reader to previous works Rook, 2014;Rook et al, 2014). The internal validity of the evaluation concerns our ability to draw conclusions about the connections between the uncertainty scenarios and the resulting SACRE's adaptation response time and data mining measures.…”

Section: Fig 13 Adaptation Response Time Per Uncertainty Scenario Rep...mentioning

confidence: 99%

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SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Zavala

Franch

Marco

et al. 2018

Expert Systems with Applications

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Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing selfadaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.

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Section: Fig 13 Adaptation Response Time Per Uncertainty Scenario Rmentioning

confidence: 99%

“…However, the study of the variation due to the internal operation of the data mining algorithm is out of the scope of this work. For better understanding of this and other data mining algorithm we refer the reader to previous works Rook, 2014;Rook et al, 2014). The internal validity of the evaluation concerns our ability to draw conclusions about the connections between the uncertainty scenarios and the resulting SACRE's adaptation response time and data mining measures.…”

Section: Fig 13 Adaptation Response Time Per Uncertainty Scenario Rep...mentioning

confidence: 99%

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Zavala

Franch

Marco

et al. 2018

Expert Systems with Applications

Self Cite

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“…Given the nature of the data, for learning route preferences, we have utilized the IBk (K-nearest neighbors) classifier on vehicle's position data; meanwhile, for learning about the self-driving usage, we have utilized the JRip (Rule-based) classifier on a Boolean class variable that indicated whether the functionality was active or not. We have selected this algorithm based on previous works [17,31,68,69]. The resulting rules regarding the self-driving functionality usage are shown in Fig.…”

Section: Scenarios' Execution 621 An Improved Context-aware Self-driv...mentioning

confidence: 99%

HAFLoop: An architecture for supporting Highly Adaptive Feedback Loops in self-adaptive systems

Zavala

Franch

Marco

et al. 2020

Future Generation Computer Systems

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“…To address this challenge, we investigate how to update the monitoring specification of one (contextual) variation point automatically at runtime with the help of machine learning. In our previous work we obtained promising results when using data mining algorithms to make runtime context conditions measurable [26].…”

Section: Background and Related Workmentioning

confidence: 99%

“…For the application of JRip, we used WEKA, the Waikato Machine Learning suite of algorithm implementations in Java. More details on the performance of JRIP algorithm, the sensor data set, and preprocessing are included in our AIRE workshop paper [26], as well as in Angela Rook's Master thesis [35].…”

Section: Application Of Data Miningmentioning

confidence: 99%

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Knauss

Damian

Franch

et al. 2016

Information and Software Technology

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Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty a↵ecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are a↵ected by runtime uncertainty, ACon analyzes and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analyzed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty a↵ecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analyzing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today's complex system environments.

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A case study of applying data mining to sensor data for contextual requirements analysis

Cited by 6 publications

References 24 publications

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

HAFLoop: An architecture for supporting Highly Adaptive Feedback Loops in self-adaptive systems

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

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