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International audience; The conceptual design of information systems is mandatory in several application domains. The advent of the Internet of Things (IoT) technologies pushes conceptual design tools and methodologies to consider the complexity of IoT data, architectures, and communication networks. In agroecology applications, the usage of IoT is quite promising, but it raises several methodological and technical issues. These issues are related to the complexity and heterogeneity of data (social, economic, environmental, and agricultural) needed by agroecology practices. Motivated by the lack of a conceptual model for IoT data, in this work, we present a UML profile taking into account different kinds of data (e.g., sensors, stream, or transactional) and non-functional Requirements. We show how the UML profile integrates with classical UML diagrams to support the design of complex systems. Moreover, We prove the feasibility of our conceptual framework through a theoretical quality assessment and its implementation in the agroecology case study concerning the monitoring of autonomous agricultural robots.

Variety and variability are an inherent source of information wealth in schemaless sources, and executing OLAP sessions on multidimensional data in their presence has recently become an object of research. However, all models devised so far propose a ``rigid'' view of the multidimensional content, without taking into account variety and variability. To fill this gap, in this paper we propose V-ICSOLAP, an extension of the ICSOLAP UML profile that supports extensibility and type/name variability for each multidimensional element, as well as complex data types for measures and levels. The real case study we use to motivate and illustrate our approach is that of trajectory analysis for agricultural robots. As a proof-of-concept for V-ICSOLAP, we propose an implementation that relies on the PostgreSQL multi-model DBMS and we evaluate its performances. We also provide a validation of our UML profile by ranking it against other meta-models based on a set of quality metrics.

Spatio-temporal events often describe the movements of an object in terms of space, time, and potential other attributes. Significant knowledge can be inferred by analysing them, either individually or atomically in form of trajectories. The trajectories can abstract additional properties and lead to deeper value. Moreover, external contextual information can be attributed to them to change their structure and lead to different perspectives. Because of this potentially valuable knowledge, nowadays indoor and outdoor tracking devices are used everywhere; generating countless events instantaneously. However, the extraction of knowledge from such heterogeneous, massive data is not a trivial task. In other terms, there is a need for a sophisticated system that is efficient in terms of distributed computing, failure handling, responsiveness, and abstraction. To answer this need, our study incorporates a fully fledged, reactive system for big trajectory data management. The system is unique of its kind because it is actor-based and features responsiveness, resiliency, and elasticity. Furthermore, our system is implemented using Scala; hence, we have the expressive power of both the Object-Oriented (OO) and Functional Programming (FP) paradigms. Allowing us to reach a higher level of abstraction to be able to process any trajectory type. The scope of this paper is to detail our system and discuss elasticity, routing strategies, load balancing, and our proper fault-tolerance mechanism. To fulfill this study, we consider the Geolife project’s GPS trajectory dataset.

Community detection is omnipresent in all complex network analyses. It consists of dividing networks into a set of nodes that are highly interconnected. These groups of nodes allow a better understanding of the structure of the networks and widely help in complex network analysis. The community detection problem has been dealt with in many research papers; however, the definition of the community and the proposed methods used for detecting communities differ based on community definition and the type of network dealt with. In this chapter, we provide the state-of-the-art existing community detection methods based on different criteria.

Nowadays, distributed systems have become requisite to process and analyse the large amount of generated data. In particular, spatio-temporal data has known an exponential growth in the last years. This could be explained by the proliferation of indoor and outdoor tracking devices and the value of the knowledge that can be extracted from their analysis. The data describe a moving objects’ behavior in space and time. Once these coordinates are assembled they form a raw trajectory. The knowledge extracted by analysing the set of trajectories is very valuable; they can be even mapped to other contextual data to add value. Preprocessing is an essential step in the mining process to filter unnecessary records and clean the noise. Furthermore, reactive systems offer the possibility to process massive data in a non-blocking, resilient, responsive and elastic manner. Despite this interest, to the best of our knowledge no works have been conducted into offering a reactive system to preprocess massive trajectories. The aim of this study is to fill this gap by proposing a reactive system based on distributed actors to manage big trajectory data. Our system is deployed with the Play Framework, Akka, MongoDB, AngularJS and D3.js. Initially, the system can load batches of trajectories stored in HDFS in a distributed manner. The scope of our study is to provide an overview of the system, to study the impact of the increase in computing resources over the scalability, and to provide the optimal node configuration. The results indicate a higher scalability of the system, and the evaluation is conducted by considering the Geolife project’s GPS trajectory dataset.

Supply chain management and complex logistics compel constant monitoring and handling of even more growing shipment chains. Therefore, it prompts the necessity to track and trace goods, for ensuring the control and management of the different logistics operations. In port terminals, tracking and tracing cargo is primitive for smooth and flexible services. Technologies such as RFID, GPS, RTLS, etc. are widely used for monitoring cargo flow in the logistics chain. In light of this, this paper proposes a novel architecture based on auto-ID technologies like QRcode, Barcode, Magnetic card ID to support real-time tracking of vehicles in a RoRo terminal and provide their stay time as well as processing time in each service. The modeled system aims to solve dynamic logistics process management problems, monitor rolling cargo, identify similar behavior as well as increase traffic flow and reduce check-in time. Design/approach: The proposed architecture provides a real-time tracking capability. It tracks a vehicle in each area of the terminal through a portal that identify it using a barcode, a QRcode or a magnetic ID card. The system performs the process logic checking/reasoning and provides process knowledge support. Originality: The proposed architecture is a novel approach which leverages the logistics performance in RoRo terminals and facilitates the tracking of vehicles and provides a real-time knowledge support on processes for those who handle logistics operations.

International audience