Log-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites

Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile

Reducing the price of resource provisioning using EC2 spot instances with prediction models

The increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptly terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on non-public Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed

Analysis of Users' Behavior in Structured e-Commerce Websites

Online shopping is becoming more and more common in our daily lives. Understanding users'' interests and behavior is essential to adapt e-commerce websites to customers'' requirements. The information about users'' behavior is stored in the Web server logs. The analysis of such information has focused on applying data mining techniques, where a rather static characterization is used to model users'' behavior, and the sequence of the actions performed by them is not usually considered. Therefore, incorporating a view of the process followed by users during a session can be of great interest to identify more complex behavioral patterns. To address this issue, this paper proposes a linear-temporal logic model checking approach for the analysis of structured e-commerce Web logs. By defining a common way of mapping log records according to the e-commerce structure, Web logs can be easily converted into event logs where the behavior of users is captured. Then, different predefined queries can be performed to identify different behavioral patterns that consider the different actions performed by a user during a session. Finally, the usefulness of the proposed approach has been studied by applying it to a real case study of a Spanish e-commerce website. The results have identified interesting findings that have made possible to propose some improvements in the website design with the aim of increasing its efficiency

A cloud-based parallel system for locating customers in indoor malls

Advances in techniques of locating mobile users have promoted the development of marketing campaigns based on customers’ location. WiFi-based location methods have proven their usefulness in tracking and locating customers within a indoor mall. Nevertheless, in some cases the performance of these methods prevents them from being used in real scenarios. In this paper, we have faced the problem of improving the execution time and reducing the cost of one of these WiFi-based location methods. Parallel programming techniques, service-oriented technologies and the cloud computing paradigm have been combined to solve efficiently these problems. The resulting system has been deployed in the Amazon EC2 environment, evaluating different configuration and deployment options

Nuevos datos sobre Aphodius (Aphodius) conjugatus (Panzer, 1795) (Coleoptera, Aphodiidae) en España

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Use of electrospinning to develop antimicrobial biodegradable multilayer systems: encapsulation of cinnamaldehyde and their physicochemical characterization

In this work, three active bio-based multilayer structures, using a polyhydroxybutyrate-co-valerate film with a valerate content of 8 % (PHBV8) as support, were developed. To this end, a zein interlayer with or without cinnamaldehyde (CNMA) was directly electrospun onto one side of the PHBV8 film and the following systems were developed: (1) without an outer layer; (2) using a PHBV8 film as outer layer; and (3) using an alginate-based film as outer layer. These multilayer structures were characterized in terms of water vapour and oxygen permeabilities, transparency, intermolecular arrangement and thermal properties. The antimicrobial activity of the active bio-based multilayer systems and the release of CNMA in a food simulant were also evaluated. Results showed that the presence of different outer layers reduced the transport properties and transparency of the multilayer films. The active bio-based multilayer systems showed antibacterial activity against Listeria monocytogenes being the multilayer structure prepared with CNMA and PHBV outer layers (PHBV + zein/CNMA + PHBV) the one that showed the greater antibacterial activity. The release of CNMA depended on the multilayer structures, where both Fick's and Case II transport-polymer relaxation explained the release of CNMA from the multilayer systems.Acknowledgments: Miguel A. Cerqueira (SFRH/BPD/72753/2010) andAnaI.Bourbon(SFRH/BD/73178/2010)arerecipientofafellowship from the Fundação para a Ciência e Tecnologia (FCT, POPH-QREN and FSE Portugal). J.L. Castro-Mayorga is supported by the Administrative Department of Science, Technology and Innovation (Colciencias) of Colombian Government. M. J. Fabra is a recipient of a Ramon y Cajal contract (RyC-2014-158) from the Spanish Ministry of Economy and Competitiveness. This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and of the Project RECI/BBB-EBI/ 0179/2012 (FCOMP-01-0124-FEDER-027462). The support of EU Cost Action MP1206 is gratefully acknowledged

Parallel computation of the reachability graph of petri net models with semantic information

Formal verification plays a crucial role when dealing with correctness of systems. In a previous work, the authors proposed a class of models, the Unary Resource Description Framework Petri Nets (U-RDF-PN), which integrated Petri nets and (RDF-based) semantic information. The work also proposed a model checking approach for the analysis of system behavioural properties that made use of the net reachability graph. Computing such a graph, specially when dealing with high-level structures as RDF graphs, is a very expensive task that must be considered. This paper describes the development of a parallel solution for the computation of the reachability graph of U-RDF-PN models. Besides that, the paper presents some experimental results when the tool was deployed in cluster and cloud frameworks. The results not only show the improvement in the total time required for computing the graph, but also the high scalability of the solution, which make it very useful thanks to the current (and future) availability of cloud infrastructures

Animal thermoregulation: a review of insulation, physiology and behaviour relevant to temperature control in buildings

Birds and mammals have evolved many thermal adaptations that are relevant to the bioinspired design of temperature control systems and energy management in buildings. Similar to many buildings, endothermic animals generate internal metabolic heat, are well insulated, regulate their temperature within set limits, modify microclimate and adjust thermal exchange with their environment. We review the major components of animal thermoregulation in endothermic birds and mammals that are pertinent to building engineering, in a world where climate is changing and reduction in energy use is needed. In animals, adjustment of insulation together with physiological and behavioural responses to changing environmental conditions fine-tune spatial and temporal regulation of body temperature, while also minimizing energy expenditure. These biological adaptations are characteristically flexible, allowing animals to alter their body temperatures to hourly, daily, or annual demands for energy. They exemplify how buildings could become more thermally reactive to meteorological fluctuations, capitalising on dynamic thermal materials and system properties. Based on this synthesis, we suggest that heat transfer modelling could be used to simulate these flexible biomimetic features and assess their success in reducing energy costs while maintaining thermal comfort for given building types

Providing resilience to UAV swarms following planned missions

As we experience an unprecedented growth in the field of Unmanned Aerial Vehicles (UAVs), more and more applications keep arising due to the combination of low cost and flexibility provided by these flying devices, especially those of the multirrotor type. Within this field, solutions where several UAVs team-up to create a swarm are gaining momentum as they enable to perform more sophisticated tasks, or accelerate task execution compared to the single-UAV alternative. However, advanced solutions based on UAV swarms still lack significant advancements and validation in real environments to facilitate their adoption and deployment. In this paper we take a step ahead in this direction by proposing a solution that improves the resilience of swarm flights, focusing on handling the loss of the swarm leader, which is typically the most critical condition to be faced. Experiments using our UAV emulation tool (ArduSim) evidence the correctness of the protocol under adverse circumstances, and highlight that swarm members are able to seamlessly switch to an alternative leader when necessary, introducing a negligible delay in the process in most cases, while keeping this delay within a few seconds even in worst-case conditions

Assessing the life cycle environmental impacts of titania nanoparticle production by continuous flow solvo/hydrothermal synthesis

Continuous-flow hydrothermal and solvothermal syntheses offer substantial advantages over conventional processes, producing high quality materials from a wide range of precursors. In this study, we evaluate the “cradle-to-gate” life cycle environmental impacts of alternative titanium dioxide (TiO₂) nanoparticle production parameters, considering a range of operational conditions, precursors, material properties and production capacities. A detailed characterisation of the nano-TiO₂ products allows us, for the first time, to link key nanoparticle characteristics to production parameters and environmental impacts, providing a useful foundation for future studies evaluating nano-TiO₂ applications. Five different titanium precursors are considered, ranging from simple inorganic precursors, like titanium oxysulphate (TiOS), to complex organic precursors such as titanium bis(ammonium-lactato)dihydroxide (TiBALD). Synthesis at the laboratory scale is used to determine the yield, size distribution, crystallinity and phase of the nanoparticles. The specifications and operating experience of a full scale plant (>1000 t per year) are used to estimate the mass and energy inputs of industrial scale production for the life cycle assessment. Overall, higher process temperatures are linked to larger, more crystalline nanoparticles and higher conversion rates. Precursor selection also influences nano-TiO₂ properties: production from TiOS results in the largest particle sizes, while TiBALD achieves the smallest particles and narrowest size distribution. Precursor selection is the main factor in determining cradle-to-gate environmental impacts (>80% in some cases), due to the production impact of complex organic precursors. Nano-TiO2 production from TiOS shows the lowest global warming potential (GWP) (<12 kg CO₂-eq. per kg TiO₂) and cumulative energy demand (CED) (<149 MJ kg¯¹ TiO₂) due to the low environmental impact of the precursor, the use of water as a solvent and its high yield even at lower temperatures. Conversely, the TiBALD precursor shows the highest impact (86 kg CO₂-eq. per kg TiO₂ and 1952 MJ kg¯¹ TiO₂) due to the need for additional post-synthesis steps and complexity of precursor manufacturing. The main purpose of this study is not a direct comparison of the environmental impacts of TiO₂ nanoparticles manufactured utilizing various precursors under different conditions, but to provide an essential foundation for future work evaluating potential applications of nano-TiO₂ and their life cycle environmental impacts