safety and quality along the buffalo milk and cheese chain

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Driving the Technology Value Stream by Analyzing App Reviews

An emerging feature of mobile application software is the need to quickly produce new versions to solve problems that emerged in previous versions. This helps adapt to changing user needs and preferences. In a continuous software development process, the user reviews collected by the apps themselves can play a crucial role to detect which components need to be reworked. This paper proposes a novel framework that enables software companies to drive their technology value stream based on the feedback (or reviews) provided by the end-users of an application. The proposed end-to-end framework exploits different Natural Language Processing (NLP) tasks to best understand the needs and goals of the end users. We also provide a thorough and in-depth analysis of the framework, the performance of each of the modules, and the overall contribution in driving the technology value stream. An analysis of reviews with sixteen popular Android Play Store applications from various genres over a long period of time provides encouraging evidence of the effectiveness of the proposed approach

Minimising conflicts among run-time non-functional requirements within DevOps

Significant contributions in the existing literature highlight the potential of softgoal interdependency graphs towards analyzing conflicting non-functional requirements (NFRs). However, such analysis is often at a very abstract level and does not quite consider the run-time performance statistics of NFR operationalizations. On the contrary, some initial empirical evaluations demonstrate the importance of the run-time statistics. In this paper, a framework is proposed that uses these statistics and combines the same with NFR priorities for computing the impact of NFR conflicts. The proposed framework is capable of identifying the best possible set of NFR operationalizations that minimizes the impact of conflicting NFRs. A detailed space analysis of the solution framework helps proving the efficiency of the proposed pruning mechanism in terms of better space management. Furthermore, a Dynamic Bayesian Network (DBN) - based system behavioral model that works on top of the proposed framework, is defined and analyzed. An appropriate tool prototype for the framework is implemented as part of this research

Things as a service: Service model for IoT

Leveraging the benefits of service computing technologies for Internet of Things (IoT) can help in rapid system development, composition and deployment. But due to the massive scale, computational and communication constraints, existing software service models cannot be directly applied for IoT based systems. Service discovery and composition mechanism need to be decentralized unlike majority of other service models. Moreover, IoT services' interfaces require to be light weight and able to expose the device profile for seamless discovery onto the IoT based system infrastructure. In addition to this, the 'things' data should be associated with its present context. To address these issues, this paper proposes a formal model for IoT services. The service model includes the physical property of 'things' and exposes it to the user. It also associates the context with the 'things' output, which in turn helps in extracting relevant information from the 'things' data. To evaluate our IoT service model, a weather monitoring system and its associated services are implemented using node.js [31]. The service data is mapped to SSN ontology for generating context-rich RDF data. This way, the proposed IoT service model can expose the device profile to the user and incorporate relevant context information with the things data

Correlating contexts and NFR conflicts from event logs

In the design of autonomous systems, it is important to consider the preferences of the interested parties to improve the user experience. These preferences are often associated with the contexts in which each system is likely to operate. The operational behavior of a system must also meet various non-functional requirements (NFRs), which can present different levels of conflict depending on the operational context. This work aims to model correlations between the individual contexts and the consequent conflicts between NFRs. The proposed approach is based on analyzing the system event logs, tracing them back to the leaf elements at the specification level and providing a contextual explanation of the system’s behavior. The traced contexts and NFR conflicts are then mined to produce Context-Context and Context-NFR conflict sequential rules. The proposed Contextual Explainability (ConE) framework uses BERT-based pre-trained language models and sequential rule mining libraries for deriving the above correlations. Extensive evaluations are performed to compare the existing state-of-the-art approaches. The best-fit solutions are chosen to integrate within the ConE framework. Based on experiments, an accuracy of 80%, a precision of 90%, a recall of 97%, and an F1-score of 88% are recorded for the ConE framework on the sequential rules that were mined

Spatial variability of maximum and minimum monthly temperature in Spain during 1981–2010 evaluated by correlation decay distance (CDD)

The spatial variability of monthly diurnal and nocturnal mean values of temperature in Spain has been analysed to evaluate the optimal threshold distance between neighbouring stations that make a meteorological network (in terms of stations’ density) well representative of the conterminous land of Spain. To this end, the correlation decay distance has been calculated using the highest quality monthly available temperature series (1981–2010) from AEMet (National Spanish Meteorological Agency). In the conterminous land of Spain, the distance at which couples of stations have a common variance above the selected threshold (50 %, r Pearson ~0.70) for both maximum and minimum temperature on average does not exceed 400 km, with relevant spatial and temporal differences, and in extended areas of Spain, this value is lower than 200 km. The spatial variability for minimum temperature is higher than for maximum, except in cold months when the reverse is true. Spatially, highest values are located in both diurnal and nocturnal temperatures to the southeastern coastland and lower spatial variability is found to the inland areas, and thus the spatial variability shows a clear coastland-to-inland gradient at annual and monthly scale. Monthly analyses show that the highest spatial variability in maximum and minimum temperatures occur in July and August, when radiation is maximum, and in lowland areas, (<200 m o.s.l.), which coincide with the mostly transformed landscapes, particularly by irrigation and urbanization. These results highlight local factors could play a major role on spatial variability of temperature. Being maximum and minimum temperature interstation correlation values highly variable in Spanish land, an average of threshold distance of about 200 km as a limit value for a well representative network should be recommended for climate analyses,

A new climatology of maximum and minimum temperature (1951–2010) in the Spanish mainland: a comparison between three different interpolation methods

This study presents a new climatology of monthly temperature for mainland Spain (1951–2010), performed with the highest quality and spatially dense, up-to-date monthly temperature dataset available in the study area (MOTEDAS). Three different interpolation techniques were evaluated: the Local Weighted Linear Regression (LWLR), the Regression-Kriging (RK) and the Regression-Kriging with stepwise selection (RKS), a modification of RK. The performances of the different models were evaluated by the leave-one-out validation procedure, comparing the results from the models with the original data and calculating different error measurements. The three techniques performed better for Tmax than for Tmin, and for the cold, rather than warmer months, also at lower altitude than highland areas. The best results were achieved with LWLR applied for the first time on temperatures in the Spanish mainland. This method improved the accuracy of the temperature reconstruction with respect to RK and RKS. We present a collection of Tmax and Tmin monthly charts, using the same temperature legend to prevent any visual bias in the interpretation of the results. The dataset is available upon request

Computer Information Systems - Analysis and Technologies

This book constitutes the refereed proceedings of the 10th International Conference on Computer Information Systems, CISIM 2011, held in Kolkata, India, in December 2011. The 30 revised full papers presented together with 6 keynote taks and plenary lectures were carefully reviewed and selected from 67 submissions. The papers are organized in topical sections on networking and its applications; agent-based systems; biometric applications; pattern recognition and image processing; industrial applications; algorithmic applications and data management; information and network security