Big data represent a pioneering development in the field of agriculture. By producing intuition, intelligence, and insights, these data have the potential to recast conventional process-driven agriculture, plotting the course for a smarter, data-driven farming. However, many open issues about the use of big data in agriculture remain unanswered. In this work, conceptualizing smart agricultural systems as cyber-physical-social systems, and building upon activity theory, we aim at highlighting some key questions that need to be addressed. To our view, big data constitute a tool reciprocally produced by all the actors involved in the agrifood supply chains. The constant flux of this tool and the intricate nature of the interactions among the actors who share it complicate the translation of big data into value. Moreover, farmers’ limited capacity to deal with data complexity, along with their dual role as producers and users of big data, impedes the institutionalization of this tool at the farm level. Although the approach used left us with more questions than answers, we suggest that unraveling the institutional arrangements that govern value co-creation, capturing the motivations of farmers and other actors, and detailing the direct and indirect effects that big data (and the technologies used to generate them) have in farms are important preconditions for setting forth rules that facilitate the extraction and equal exchange of value from big data.

Key questions on the use of big data in farming: An activity theory approach

“I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce

Now a day’s social media place an important role for sharing human social behaviors and participation of multi users in the network. The social media will create opportunity for study human social behavior to analyze large amount of data streams. In this social media one of the interesting problems is users will introduce some issues and discuss those issues in the social media. So that those discuss will contain positive or negative attitudes of each user in the social network. By taking those problems we can consider formal interpretation social media logs and also take the sharing of information that can spread person to person in the social media. Once the social media of user information is parsed in the network and identified relationship of network can be applied group of different types of data mining techniques. However, the appropriate granularity of user communities and their behavior is hardly captured by existing methods. In this paper we are proposed optimized fuzzy means clustering algorithm for grouping related information. By implementing this algorithm we can get best group result and also reduce time complexity for generating cluster groups. The main goal of our proposed framework is twofold for overcome existing problems. By implementing our approach will be very scalable and optimized for real time clustering of social media.

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Social Media Analysis using Optimized K-Means Clustering

Food security is a global challenge and agriculture can address this challenge through radical improvements in productivity, efficiency and effectiveness. Internet of Things (IoT) is a major enabler of such improvements. This paper discusses challenges that agricultural industry is facing and proposes a solution based on IoT technology and a specific platform called OpenIoT developed jointly by the EU FP7 OpenIoT consortium. Phenonet is an OpenIoT use case developed by CSIRO, Australia and demonstrates how digital agriculture benefits from deploying the IoT. Experience and lessons from using OpenIoT middleware for Phenonet development are also presented and analysed.

Addressing Information Processing Needs of Digital Agriculture with OpenIoT Platform.

A social CRM analytic framework for improving customer retention, acquisition, and conversion

Bringing Big data technologies into agriculture presents a significant challenge; at the same time, this technology contributes effectively in many countries' economic and social development. In this work, we will study environmental data provided by precision agriculture information technologies, which represents a crucial source of data in need of being wisely managed and analyzed with appropriate methods and tools in order to extract the meaningful information. Our main purpose through this paper is to propose an effective Big data architecture based on profiling system which can assist (among others) producers, consulting companies, public bodies and research laboratories to make better decisions by providing them real time data processing, and a dynamic big data service composition method, to enhance and monitor the agricultural productivity. Thus, improve their traditional decision-making process, and allow better management of the natural resources.

A profile-based Big data architecture for agricultural context

Social networks have grown exponentially in popularity around the world. They are increasingly generating a considerable social and economic impact. Nevertheless, the majority of social networks is wondering how to leverage their growing popularity in order to achieve sustainable revenue. In this context, measuring social network balance is of vital importance for analysing and improving social relationships. This paper presents a novel method to ensure balance in social networks, which is fundamentally based on K-means clustering algorithm. To validate our approach, we employ Monte Carlo simulations, where we estimate two parameters for evaluating the social balance, represented as the equilibrium function along with the equilibrium percentage of the network. The obtained results indicate that the proposed model is efficient in terms of improving social network balance, and provides a considerable added value.

Enhancing Social Network Communication through Dynamic Clustering Balance

Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data in order to launch an efficient customer-centric and cost-effective marketing strategy. However, targeting all potential customers with one general marketing strategy seems to be inefficient. While targeting each potential customer with a specific strategy can be cost demanding. Thus, it is essential to group customers into specific classes and target each class according to its respective customer needs. In this paper, we develop a Random Forest- based approach to classify potential customers into three main categories namely, prospects, satisfied and unsatisfied customers. The proposed model has been trained, tested, and compared to some state-of-the-art classifiers viz., Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) based on several metrics including accuracy, sensitivity, specificity, false positive rate, and false negative rate. The reported results were satisfactory with an accuracy of 98.46%, a sensitivity of 97.69%, and a specificity of 98.84%.

Soumaya Lamrhari

Papers

A social CRM analytic framework for improving customer retention, acquisition, and conversion

A profile-based Big data architecture for agricultural context

Enhancing Social Network Communication through Dynamic Clustering Balance

Random Forest-based Approach for Classifying Customers in Social CRM