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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.

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.

Identifying the influential spreaders in complex networks is crucial to understand who is responsible for the spreading processes and the influence maximization through networks. Targeting these influential spreaders is significant for designing strategies for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing methods such as local centrality measures like degree centrality are less effective, and global measures like closeness and betweenness centrality could better identify influential spreaders but they have some limitations. In this paper, we propose the HybridRank algorithm using a new hybrid centrality measure for detecting a set of influential spreaders using the topological features of the network. We use the SIR spreading model for simulating the spreading processes in networks to evaluate the performance of our algorithm. Empirical experiments are conducted on real and artificial networks, and the results show that the spreaders identified by our approach are more influential than several benchmarks.

The proliferation of indoor and outdoor tracking devices has led to a vast amount of spatial data. Each object can be described by several trajectories that, once analysed, can yield to significant knowledge. In particular, pattern analysis by clustering generic trajectories can give insight into objects sharing the same patterns. Still, sequential clustering approaches fail to handle large volumes of data. Hence, the necessity of distributed systems to be able to infer knowledge in a trivial time interval. In this paper, we detail an efficient, scalable and distributed execution pipeline for clustering raw trajectories. The clustering is achieved via a fuzzy similarity relation obtained by the transitive closure of a proximity relation. Moreover, the pipeline is integrated in Spark, implemented in Scala and leverages the Core and Graphx libraries making use of Resilient Distributed Datasets (RDD) and graph processing. Furthermore, a new simple, but very efficient, partitioning logic has been deployed in Spark and integrated into the execution process. The objective behind this logic is to equally distribute the load among all executors by considering the complexity of the data. In particular, resolving the load balancing issue has reduced the conventional execution time in an important manner. Evaluation and performance of the whole distributed process has been analysed by handling the Geolife project&rsquo, s GPS trajectory dataset.

International audience; A data warehouse stores secret data about the privacy of individuals and important business activities. This makes access to this source a risk of disclosure of sensitive data. Hence the importance of implementing security measures which guarantee the data confidentiality by establishing an access control policy. In this direction, several propositions were made, but none are considered as a standard for access management to data warehouses. In this article, we will present our approach that allows first to exploit the permissions defined in the data sources in order to help the administrator to define access permissions to the data warehouse, and then our system will automatically generate the sensitivity level of each data warehouse element according to the permissions granted to an object in the data warehouse.

Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community., {"references":["N. Shadbolt, T. Berners-Lee, and W. Hall, \"The semantic web revisited,\"\nIEEE intelligent systems, vol. 21, no. 3, pp. 96–101, 2006.","A. H. Asiaee, T. Minning, P. Doshi, and R. L. 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International audience; Respect for privacy and data confidentiality in a company are two fundamentals that must be protected. However, a Data Warehouse can be used as a very powerful mechanism for discovering crucial information, hence the importance of implementing security measures which guarantee the data confidentiality by establishing an access control policy. In this direction, several propositions were made, however, none is considered as a standard of access management to data warehouses. In this article, we will present our approach that allows first to exploit the permissions defined in the data sources in order to help the administrator to define access permissions to the data warehouse, and then our system will automatically generate the sensitivity level of each data warehouse element according to the permissions granted to an object in the data warehouse.

NoSQL databases have recently been introduced as alternatives to traditional relational database management systems because of their capabilities in terms of storing data and query retrieval. Biological datasets can be modelled using various models, for example, graphs (protein-protein interaction) or documents (protein sequence information). Applications that involve these two data models can be combined into a single unique architecture either using the polyglot persistence approach or using a multi-model approach. This paper evaluates the performance of a polyglot persistence approach versus a multi-model store. The polyglot persistence approach combines a graph-oriented database (Neo4j) and a document-oriented database (MongoDB); and the multi-model system is OrientDB. The comparisons are made following these aspects: importation, single operations, and query performance. OrientDB demonstrates a potential to manage large proteomics dataset for query retrieval and graph importation. However, when updating records, OrientDB was found to be slow. There is no single store that performs better in all cases.

Mass Spectrometry (MS) has arguably become thecore technology in proteomics. MALDI and SELDI-TOFtechniques enable the study biological fluids, e.g. human blood.Analysis of these samples can lead to discover new biomarkerswhich can ease the diagnostic and prognostic of several diseases,e.g. various cancers. In this work, we focus on MS data fromSELDI-TOF experiments. We begin with a preprocessing step inorder to remove noises due to the acquisition process of the data.Then, we apply the differential analysis to a SELDI-MS data,using the Significance Analysis of Microarray (SAM) methodimplemented in Matlab. Results using the SAM method arecompared with those obtained by the conventional t -test andAnalysis Of Variance (ANOVA) in order to evaluate its efficacyand its performance. As a result, we demonstrate that the SAMmethod can be adapted for effective significance analysis ofSELDI-MS data. It is deemed powerful and provides betterresults that totes. An easy-to-use application is developed withMatlab for mass spectrometry data analysis from raw spectra todifferential analysis, including the SAM method.