Antoine Auger
email: antoine.auger@isae-supaero.fr

Ernesto Exposito
email: ernesto.exposito@univ-pau.fr

Emmanuel Lochin
email: emmanuel.lochin@isae-supaero.fr

Towards the Internet of Everything: Deployment Scenarios for a QoO-aware Integration Platform

Keywords: Internet of Everything, sensors, observations, quality, Quality of Observation, platform, integration

Built upon the Internet of Things (IoT), the Internet of Everything (IoE) acknowledges the importance of data quality within sensor-based systems, alongside with people, processes and Things. Nevertheless, the impact of many technologies and paradigms that pertain to the IoE is still unknown regarding Quality of Observation (QoO).

This paper proposes to study experimental results from three IoE-related deployment scenarios in order to promote the QoO notion and raise awareness about the need for characterizing observation quality within sensor-based systems. We specifically tailor the definition of QoO attributes to each use case, assessing observation accuracy within Smart Cities, observation rate for virtual sensors and observation freshness within post-disaster areas. To emulate these different experiments, we rely on a custom-developed integration platform for the assessment of QoO as a service called iQAS.

We show that QoO attributes should be used to specify what is an observation of "good quality", that virtual sensors may have specific and limiting capabilities impacting QoO and that network QoS and QoO are two complementary quality dimensions that should be used together to improve the overall service provided to end-users.

I. INTRODUCTION

Over numerous integration platforms that have been developed for the Internet of Things (IoT), network Quality of Service (QoS) [START_REF]Definitions of terms related to QoS[END_REF] has shown to be unsuitable for assessing the actual value of observations [START_REF] Barnaghi | Challenges for Quality of Data in Smart Cities[END_REF]. Basically, within datacentric systems, Quality of Observation (QoO) mainly depends on consumer needs and the specific context in which he/she consumes it. Therefore, we believe that QoO is a critical issue to address in the forthcoming Internet of Everything (IoE) [START_REF] Bradley | Internet of Everything: A $4.6 Trillion Public-Sector Opportunity[END_REF] as more and more decisions will be based on services derived from received observations. Built on the IoT, the IoE paradigm mostly acknowledges the importance of data (and therefore of data quality) within systems, alongside with people, processes and Things. Due to its wide scope, the IoE encompasses many use cases that may bring as much challenges regarding QoO. Among these use cases, Smart Cities, Web of Things and peer-to-peer connections between sensing Things have already become a reality, waiting for bigger scale adoption. However, each of these uses also brings specific QoO challenges to address.

By introducing three deployment scenarios for a custom QoO-aware integration platform, the goal of our study is to promote the QoO notion and the need for characterizing observation quality (QoO assessment as a service). In order to draw more insightful lessons, we focus on specific attributes specifically tailored for each use case: observation accuracy within Smart Cities, observation rate for virtual sensors pertaining to the Web of Things and, finally, freshness when observations are collected in a peer-to-peer decentralized fashion as within post-disaster areas.

The rest of this paper is structured as follows: Section II introduces the required background and the related work. Section III presents the integration platform that we used to obtain the experimental results, with a focus on initial requirements and concrete implementation choices that have been made. Then, Section IV, V and VI presents the three IoErelated deployment scenarios. Finally, Section VII concludes and describes further perspectives.

II. BACKGROUND AND RELATED WORK

This section describes several notions required to fully understand the three deployment scenarios. From Things to people, many enablers will be needed to pursue IoT and IoE paradigms.

A. Internet of Things (IoT) and Internet of Everything (IoE)

The Internet of Things (IoT) [START_REF] Atzori | The Internet of Things: A survey[END_REF] can be defined as a paradigm that envisions pervasive and inter-connected objects (also called Things) that can be uniquely addressed, generally through the Internet. Some examples of these Things can be found with Radio-Frequency IDentification (RFID) tags, sensors, actuators, mobile phones, etc. Within the IoT ecosystem, Things generally produce data that needs to be collected and processed by gateways or IoT platforms. Depending on the use case considered, these platforms may provide enhanced services to end users based on received data.

Built upon the IoT which mainly refers to the deployment and the interconnection of smarter communication-capable Things, the Internet of Everything (IoE) extends this paradigm by going beyond Things to also integrate societal impacts, risks and economic benefits of a more interconnected World. This relatively new paradigm has been introduced by Cisco in an official report dated 2013 [START_REF] Bradley | Internet of Everything: A $4.6 Trillion Public-Sector Opportunity[END_REF]. In this report, Cisco has defined the IoE as "the networked connection of people, process, data, and things". Compared to the IoT that was mainly driven by technology, the IoE has been coined to rather envision business and deep changes in society. We believe that the IoE vision is particularly appropriate as we are witnessing the emergence of more and more sensor-based data-centric systems. Indeed, this paradigm acknowledges the importance of data (and therefore of data quality) within sensor-based IoT systems.

B. Observations and Quality of Observation (QoO)

Like many researchers before us, and in particular Perera et al. in [START_REF] Perera | Context Aware Computing for The Internet of Things: A Survey[END_REF], we distinguish several kinds of sensors: physical sensors are the most common kind consisting in concrete devices often used for environmental monitoring use cases; virtual sensors are generally Web-based services that can be queried through APIs; finally, logical sensors may combine information coming from both physical and virtual sensors. Regardless of their type, all these sensors generate observations, which may be seen as the representation of an observed phenomenon (e.g., the temperature of a place) or an event (e.g., availability of a new software update) at different granularity levels [START_REF] Sheth | Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing[END_REF].

Several quality dimensions have been envisioned in order to characterize observations. Among them, we can cite Quality of Service (QoS) [START_REF]Definitions of terms related to QoS[END_REF], Quality of Information (QoI) [START_REF] Bisdikian | On the Quality and Value of Information in Sensor Networks[END_REF] or even Context annotation [START_REF] Dey | Understanding and Using Context[END_REF]. Such quality dimensions generally define quality attributes that aim at characterizing how fit-for-use is an observation for a specific use by an application (specific context). Table I gives five examples of quality attributes that may be used to characterize sensors or observations. For instance, using both latency (network QoS) and timeliness (QoI), an application may better understand if some outdated observations are the result of poor network performances or due to a sensor sampling rate too low. Recent related work includes the CityPulse framework [START_REF] Puiu | Citypulse: Large scale data analytics framework for smart cities[END_REF], which also considers QoI (with the computation of collection point-related Key Performance Indicators) and QoI inspection (with tools such as the CityPulse QoI Explorer). However, this framework mainly aims at providing large-scale stream processing solutions for Smart City applications.

In this paper, we consider Quality of Observation (QoO) as "the actual observation value for a specific consumer given a specific Context". To remain compliant with previous definitions, we acknowledge the fact that QoO can be impacted by network QoS and we consider QoS as the assembly of network QoS and QoO.

C. Semantics and Ontologies

Ontologies have proven to be suitable for addressing heterogeneity within sensor-based systems. They are mainly used to 1) abstract sensors and their capabilities and 2) annotate observations according to common concepts to enable their sharing and understanding by other systems. The most popular

Attribute name Common definition Mentioned in

Accuracy

Distance between reported observations and the corresponding phenomenon/event.

[7], [START_REF] Barnaghi | Challenges for Quality of Data in Smart Cities[END_REF] Provenance Sensor or mechanism that has output the observation.

[7]

Reputation

Publicly held opinion of a sensor or intermediary mechanism.

[7]

Latency Duration to retrieve an observation (including network transport time).

[7] Timeliness Time horizon over which an observation is considered as valid.

[7], [START_REF] Barnaghi | Challenges for Quality of Data in Smart Cities[END_REF] TABLE I: Examples of quality attributes used for observation and sensor characterization. For more metrics, see [START_REF] Bisdikian | On the Quality and Value of Information in Sensor Networks[END_REF].

ontology for sensors and observations is without any doubt the Semantic Sensor Network (SSN) ontology developed by the W3C [START_REF] Compton | The SSN ontology of the W3c semantic sensor network incubator group[END_REF], [START_REF]Semantic Sensor Network Ontology[END_REF]. Regarding sensor abstraction, integration platforms generally use ontologies to describe sensor capabilities (what are their type, their sampling rate, their units, etc.). This mechanism allows to consider sensors as abstract observation providers, enhancing their reusability and the global interoperability of the solution.

III. THE IQAS PLATFORM

Before diving into results, we kindly remind the reader that iQAS is a prototype of an integration platform for QoO Assessment as a Service [START_REF] Auger | iQAS: An Integration Platform for QoI Assessment as a Service for Smart Cities[END_REF]. iQAS is interoperable, extensible, configurable and usable by stakeholders with different skills and interests. In this paper, we present new unpublished material by detailing 1) concrete implementation choices that we made during the development of our platform and 2) experimental results for the three deployment scenarios. Please note that the source code of any iQAS component presented in this paper is available upon simple request.

A. Reminder of iQAS Requirements

In [START_REF] Auger | iQAS: An Integration Platform for QoI Assessment as a Service for Smart Cities[END_REF], initial functional (F) and non-functional (NF) requirements for the iQAS platform were expressed as follows: F1 Users should be able to submit queries with QoO constraints about a couple topic/location; F2 Users should be able to monitor QoO attributes in realtime according to several granularity levels; F3 Users should be able to develop their own mechanisms to provide QoO guarantees; F4 Users should be able to express the impact of these mechanisms on QoO attributes; NF1 The platform should be usable and configurable by stakeholders with different skills; NF2 The platform should be extensible by supporting integration of new protocols and observation sources; NF3 The platform should be interoperable; NF4 The platform should be scalable.

B. The QoOnto ontology

Extensibility (NF2) and interoperability (NF3) requirements have led to the development of the QoOnto ontology (see Figure 1). This ontology, which makes the link between For the experiments that we performed in this paper, we instantiated the QoOnto ontology by defining three instances of the QoOAttribute class:

• OBS_ACCURACY is the distance between a reported observation and its corresponding phenomenon or event.

For a given observation, we assumed that its accuracy was equal to 100% when its value was within the sensor measurement range and 0% otherwise. • OBS_FRESHNESS is defined as the age of an observation when it becomes available for final consumers. Annotated to each observation, it measures the additional latency due to 1) transport time over the collection network and 2) iQAS processing time.

• OBS_RATE refers to the number of observations that the platform effectively delivers by unit of time for a given request (e.g., 3/second). On the contrary of the two previous attributes, this metric refers to an observation flow rather than a single observation.

C. Implementation and Deployment

1) Implementation: we developed iQAS following a dataflow component-based architecture (see Figure 2 for an 1 http://linkeddata.org overview). Main iQAS components have been implemented according to the Actor model, thanks to the Akka toolkit 2 . iQAS enables adaptation (with both auto-(re)configuration and QoO-based adaptation) by implementing an adaptation control loop (denoted as the "MAPE-K3 loop" in Figure 2). Adaptation is continuously performed by monitoring the QoO provided by iQAS to its different consumers. When possible, the platform can try to "heal" a request by deploying a set of mechanisms chained to form a "QoO Pipeline". In order to make such a decision, the platform should reason on the QoOnto ontology, performing inference to select a suitable QoOPipeline candidate, when available.

2) Deployment: for this experimentation campaign, we locally deployed the different iQAS components on a Mac Pro server 2013 with 3.7 GHz Quad-Core Intel Xeon E5 processor and 32 GB RAM.

D. The iQAS ecosystem

As part of the iQAS ecosystem, two additional tools have been developed to specifically evaluate functional requirements F1, F2 and F4 of the platform. The first tool is a Virtual Sensor Container (VSC) image, which allows to create virtual sensors that may generate observations at random, from file or by first retrieving them from other observation sources (such as the Web) as a transparent proxy. The second tool is a Virtual Application Consumer (VAC) image, which allows to emulate fake consumers that submit iQAS requests and then consume observations while reporting back the perceived QoO to the iQAS platform in real-time. Both these tools are two Docker4 container images. We chose to use the Docker virtualization for its great modularity and reusability: once a Docker image has been defined, it is easy to deploy several container instances that may accept custom parameters at runtime. Besides, since virtualization is performed at application level, containers are less resource demanding than common Virtual Machines.

IV. SCENARIO 1: SMART CITIES

A. Motivation and Scenario considered

The motivation to envision QoO within Smart Cities has already been extensively described in the original paper that introduced the iQAS platform [START_REF] Auger | iQAS: An Integration Platform for QoI Assessment as a Service for Smart Cities[END_REF]. In the same paper, we have also shown the benefits to apply some QoO Pipeline (such as observation filtering) on observation streams to cope with systematic errors of sensors.

For this first use case, let us consider two stakeholders: Matt, the first one, is a city employee in charge of the sensor maintenance; Maggie is a meteorologist for a private weather forecast company. Let us imagine that Matt is asked to check the good working of all visibility sensors across the city. In parallel, Maggie is interested in collecting visibility measurements to release a weather report for an upcoming Fig. 2: iQAS: an integration platform for QoO Assessment as a Service airshow that will take place on the same day. In this case, both stakeholders are interested in visibility for the same spatiotemporal context, but with different QoO needs. As a domain-expert, Maggie only wants to retrieve accurate observations, which will help her to write her weather report and make accurate forecasts. For physical sensors, this requirement mainly translates into selecting records that have a consistent value regarding their sensor's measurement range. As visibility is a distance measurement, Maggie specifies that she only wants to retrieve positive observation values. Matt's needs are different: even if he surely also knows that visibility sensors only output positive measurements, he wants to identify the defective ones in order to replace or repair them. Therefore, he decides to submit a query with no specific thresholds. He could also had limited the received observations to the interval ] -∞, 0[. To emulate this Smart City use case, we created two VACs for our stakeholders Matt and Maggie. Each VAC submitted a representative request to our iQAS platform. In response, iQAS auto-configured itself by creating two observation pipelines, reusing the first pipeline to construct the second one. Then, we created a VSC to emit observations corresponding to a raw visibility dataset recorded in the city of Aarhus in Denmark 5 from February 2014 to June 2014 at the sensing rate of 2 measurements per second. This dataset was chosen specifically since we were aware that it contained some systematic measurement errors (with some values equal to -9999 km). As soon as we started the VSC, observations started to flow throughout iQAS and were delivered to the two VACs. While consuming the visibility observations, the VACs reported to iQAS the QoO for the received observations, enabling real-time visualization (see Figure 3 for the first 500 seconds of the simulation).

B. Results and Analysis

As expected, Figure 3a shows several visibility values equal to -9999 km, which have been annotated as "not accurate" 5 http://iot.ee.surrey.ac.uk:8080/datasets.html#weather by the iQAS platform (0% for OBS_ACCURACY, as defined in Section III-B). In comparison, the QoO visualization for Maggie the meteorologist only contains accurate visibility observations (OBS_ACCURACY = 100%) according to the iQAS platform. The first lesson learned from this deployment scenario is that QoO can be significantly improved by the deployment of intermediary transformation functions such as filtering. The second lesson learned is that QoO needs are best expressed by the final consumers who will consume observations. For instance, if iQAS had automatically filtered inaccurate visibility observations, Matt could not have identified and replaced faulty sensors. As a result, developers should avoid to implement default behaviors, especially when it comes to QoO. Finally, inaccurate observations may sometimes represent high-quality observations and actually worth something for some consumers. Therefore, the expression of some QoO constraints may help to precise the definition of a "good-quality observation".

V. SCENARIO 2: WEB OF THINGS A. Motivation and Scenario considered

The Web of Things (WoT) [START_REF] Guinard | Towards the Web of Things: Web Mashups for Embedded Devices[END_REF] is another paradigm encompassed by the IoE. The WoT is generally defined as a set of practices, architectures and programming patterns used in order to expose sensors to the World Wide Web. For instance, a real-world physical sensor that can be remotely accessed through the Internet using main HTTP verbs (GET, POST, etc.) can be considered as being part of the WoT. Current trends regarding the number of connected Things show that more and more physical sensors will continue to be abstracted as virtual sensors in the next years. This second deployment scenario gives us the opportunity to study some QoO-related challenges that can be raised by the integration of third-party observation sources. First, we created a free account on the OpenWeath-erMap website6 in order to use the "current weather data" API that it provides. We only applied for a free plan that allows no more than 60 API calls per minute. API documentation says that "current weather is frequently updated based on global models and data from more than 40000 weather stations". In order to integrate this virtual sensor to iQAS, we used a new VSC as a proxy that we configured to retrieve the current temperature in the city of London from this API at a rate of 2 observations per second. Then, we submitted an iQAS request for temperature measurements with a QoO constraint regarding OBS_RATE (guaranteed minimum of 2 observation per second). Finally, we counted the number of observations received by grouping them based on 10-second windows.

B. Results and Analysis

Despite the QoO needs and the guaranteed Service Level Agreement (SLA) submitted to iQAS, Figure 4 shows that the platform was only able to retrieve 60 temperature records per minute maximum (for the intervals [0, 60[, [60, 120[, [120, 180[ and so on). This result is consistent with the fact that the free plan of OpenWeatherMap only allows 60 calls per minute maximum for a same API key. Integration platforms (such as iQAS) are systems of systems. As a consequence, their ability to meet SLAs is often conditioned by other resources (such as external observation sources, Cloud-based infrastructure for commercial platforms, etc.). In fact, the OpenWeatherMap API could be seen as a virtual sensor that has a maximum sensing rate of 1 observation per second. Furthermore, its capabilities are also conditioned by the ones of the sensors (physical or virtual) that it uses in turn. The main lesson to be learned from this deployment scenario is that ensuring QoO guarantees requires a deep knowledge of the available resources as well as their characteristics. As a result, capabilities of third-party observation sources should always be carefully identified and described. In that way, semantics can help to make the link between an observed symptom (e.g., sensor unavailability) and its cause (e.g., battery drained for a physical sensor; API call limit reached for a virtual sensor). To provide finer QoO guarantees, we strongly believe that more research is required to better describe sensor capabilities (according to their type, as they evolve over time, etc.). So far, we only considered scenarios where sensors had permanent connection to the Internet. The objective of this deployment scenario consists in assessing the impact of postdisaster areas on QoO. We define as "post-disaster" the environments where sensors could have intermittent connection to the Internet. To emulate such environments, we envision Delay Tolerant Networks (DTNs) [START_REF] Fall | DTN: an architectural retrospective[END_REF] where observation collection is performed in a decentralized peer-to-peer manner according to the store-and-forward paradigm. To that end, we reused HINT [START_REF] Baudic | HINT: from network characterization to opportunistic applications[END_REF], which is a self-developed hybrid emulation system for opportunistic networks where nodes can be either Android phones or virtual ones. For more details on our HINT emulator, the interested reader can refer to the associated publication. First, we developed a specific asynchronous adapter Python class to poll observations from HINT as soon as they arrive. We used this adapter to configure a new VSC that acts as a transparent proxy between the HINT emulator and the iQAS platform. Then, we submitted a request without any QoO constraints to retrieve all observations coming from the HINT emulator. Finally, within HINT, we generated 200 observations from a real node to a gateway node. Each message had to be first internally exchanged within HINT in a peer-topeer manner before reaching the gateway node where it was consumed by our VSC. Once that all messages were delivered to iQAS consumers, we computed offline the freshness (the age) of the observations when 1) they arrived at the HINT gateway node (HINT viewpoint) and 2) they were delivered by iQAS to consumers (iQAS viewpoint).

B. Results and Analysis

Figure 5 depicts the Complementary Cumulative Distribution Function (CCDF) that represents the age of the observations from both HINT and iQAS perspectives. For example, this figure shows that, in more than 10% of time, observations that are effectively consumed by iQAS consumers are, at least, 15-second old. Beyond freshness' order of magnitude that may vary depending on the HINT configuration, the main lesson to retain from this deployment scenario is that the overhead introduced by the iQAS platform in terms of delay can be negligible compared to the routing overhead introduced when observation collection is achieved in a peer-to-peer decentralized way. Therefore, in order to significantly improve the overall service provided to their end-users, IoE platforms should consider both network QoS (e.g., latency) and QoO (e.g., freshness) as complimentary quality dimensions.

VII. CONCLUSIONS AND PERSPECTIVES

Our research work aims at raising awareness about the QoO notion and promoting the characterization of observation quality within sensor-based systems. The challenge is considerable as providing QoO guarantees is an ambitious task that depends on many parameters including use cases, software used and configuration.

In this paper, we envisioned three IoE-related deployment scenarios for a QoO-aware integration platform. For each of them, we presented experimental results obtained with the help of iQAS, a custom integration platform for QoO assessment as a service. To better understand results, we briefly recalled the main requirements for iQAS, as well as the main implementation choices that we made during its development. Then, by introducing specific QoO attributes tailored for each use case, we managed to draw some QoO-related lessons that integration platforms should take into account in order to correctly fulfill the IoE. Hence, the study of observation accuracy within Smart Cities confirmed that there might be several definitions of a "good-quality observation"; the analyze of observation rate allowed us to identify limiting capabilities for virtual sensors; finally, the study of freshness for observations reported in a decentralized way demonstrated that network QoS and QoO are two complementary quality dimensions that should be used together in order to improve the overall service provided to end-users, especially within post-disaster areas.

As future work, we plan to pursue the study of the QoO notion and perform more complex deployment scenarios with iQAS. We also plan to conduct an extended performance evaluation campaign of the platform.

  (a) QoO visualization for the maintenance request (b) QoO visualization for the meteorologist request

Fig. 3 :

 3 Fig. 3: Visibility measurements with OBS_ACCURACY assessment for two different iQAS requests

Fig. 4 :

 4 Fig. 4: OBS_RATE assessment for a Virtual Sensor Consumer (VSC) that retrieves temperature for the city of London through the OpenWeatherMap API (free plan allowing no more than 60 API calls per minute)

Fig. 5 :

 5 Fig. 5: CCDF for the OBS_FRESHNESS for observations generated by a single HINT node and consumed by a single iQAS consumer (log-log scale)

http://akka.io

Monitor, Analyze, Plan, Execute according to a Knowledge base

https://www.docker.com

http://openweathermap.org/api

ACKNOWLEDGEMENT

This research was supported in part by the French Ministry of Defense through financial support of the Direction Générale de l'Armement (DGA).


Richard C Gerkin

Kathrin Ohla

Maria G Veldhuizen

Paule V Joseph

Christine E Kelly

Alyssa J Bakke

Kimberley E Steele

Michael C Farruggia

Robert Pellegrino

Marta Y Pepino

Cédric Bouysset

Graciela M Soler

Veronica Pereda-Loth

Michele Dibattista

Keiland W Cooper

Ilja Croijmans

Antonella Di Pizio

M Hakan Ozdener

Alexander W Fjaeldstad

Cailu Lin

Mari A Sandell

Preet B Singh

V Evelyn Brindha

Shannon B Olsson

Luis R Saraiva

Gaurav Ahuja

Mohammed K Alwashahi

Surabhi Bhutani

Anna D'errico

Marco A Fornazieri

Jérôme Golebiowski

Liang-Dar Hwang

Lina Öztürk

Eugeni Roura

Sara Spinelli

Katherine L Whitcroft

Farhoud Faraji

Florian S Ph

Fischmeister

Thomas Heinbockel

Julien W Hsieh

Caroline Huart

Iordanis Konstantinidis

Anna Menini

Gabriella Morini

Jonas K Olofsson

Carl M Philpott

Denis Pierron

Vonnie D C Shields

Vera V Voznessenskaya

Javier Albayay

Aytug Altundag

Moustafa Bensafi

María Adelaida Bock

Orietta Calcinoni

William Fredborg

Christophe Laudamiel

Juyun Lim

Johan N Lundström

Alberto Macchi

Pablo Meyer

Shima T Moein

Enrique Santamaría

Debarka Sengupta

Paloma Rohlfs Dominguez

Hüseyin Yanik

Gccr Group

Thomas Hummel

John E Hayes

Danielle R Reed

Masha Y Niv

Steven D Munger

PhD Valentina Parma
email: valentina.parma@temple.edu

†c

Sanne Boesveldt

Jasper H B De Groot

Caterina Dinnella

Jessica Freiherr

Tatiana Laktionova

Sajidxa Mariño

Erminio Monteleone

Alexia Nunez-Parra

Olagunju Abdulrahman

Marina Ritchie

Thierry Thomas-Danguin

Julie Walsh-Messinger

Rashid Al Abri

Rafieh Alizadeh

Emmanuelle Bignon

Elena Cantone

Maria Paola Cecchini

Jingguo Chen

Maria Dolors Guàrdia

Kara C Hoover

Noam Karni

Marta Navarro

Alissa A Nolden

Patricia Portillo Mazal

Nicholas R Rowan

Atiye Sarabi- Jamab

Nicholas S Archer

Ben Chen

Elizabeth A Di Valerio

Emma L Feeney

Johannes Frasnelli

Mackenzie E Hannum

Claire Hopkins

Hadar Klein

Coralie Mignot

Carla Mucignat

Yuping Ning

Elif E Ozturk

Mei Peng

Ozlem Saatci

Elizabeth A Sell

Carol H Yan

Raul Alfaro

Cinzia Cecchetto

Gérard Coureaud

Riley D Herriman

Jeb M Justice

Pavan Kumar Kaushik

Sachiko Koyama

Jonathan B Overdevest

Nicola Pirastu

Vicente A Ramirez

S Craig Roberts

Barry C Smith

Hongyuan Cao

Hong Wang

Patrick Balungwe Birindwa

Marius Baguma

The best COVID-19 predictor is recent smell loss: a cross-sectional study

Background: COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.

Methods: This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.

Results: Both C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ~50% of participants and was best predicted by time since illness onset.

Conclusions:

As smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable.

Introduction

The novel coronavirus SARS-CoV-2 responsible for the global COVID-19 pandemic has left a staggering level of morbidity, mortality, and societal and economic disruption in its wake. 1 Early publications [START_REF] Paderno | Smell and taste alterations in Covid-19: a cross-sectional analysis of different cohorts[END_REF][START_REF] Giacomelli | Self-reported Olfactory and Taste Disorders in Patients With Severe Acute Respiratory Coronavirus 2 Infection: A Cross-sectional Study[END_REF][START_REF] Yan | Association of chemosensory dysfunction and COVID-19 in patients presenting with influenza-like symptoms[END_REF][START_REF] Lechien | Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study[END_REF][START_REF] Moein | Smell dysfunction: a biomarker for COVID-19[END_REF][START_REF] Hornuss | Anosmia in COVID-19 patients[END_REF][START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] indicate that sudden smell and taste loss are cardinal, early and potentially specific symptoms of COVID-19, [START_REF] Menni | Real-time tracking of self-reported symptoms to predict potential COVID-19[END_REF] including in otherwise asymptomatic individuals. [START_REF] Menni | Quantifying additional COVID-19 symptoms will save lives[END_REF][START_REF] Kim | Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19[END_REF][START_REF] Walsh-Messinger | Standardized Testing Demonstrates Altered Odor Detection Sensitivity and Hedonics in Asymptomatic College Students as SARS-CoV-2 Emerged Locally[END_REF][START_REF] Hopkins | Presentation of new onset anosmia during the COVID-19 pandemic[END_REF] While fever and cough are common symptoms of diverse viral infections, the potential specificity of early chemosensory loss to COVID-19 could make it valuable in screening and diagnosis.

Anosmia and other chemosensory disorders have serious health and quality-of-life consequences for patients. However, the general lack of awareness of anosmia and other chemosensory disorders by clinicians and the public, including their association with upper respiratory infections, [START_REF] Soler | A primer on viral-associated olfactory loss in the era of COVID-19[END_REF] contributed to an underappreciated role of chemosensory symptoms in the diagnosis of COVID-19. Additionally, the impact of smell loss as a clinical consequence of COVID-19 has not been adequately addressed. Thus, there is an urgent need to better define the chemosensory dysfunctions associated with COVID-19 and to determine their relevance as predictors of this disease. It is critical to develop rapid clinical tools to efficiently and effectively integrate chemosensory assessments into COVID-19 screening and treatment protocols. Information on the duration and reversibility of post-COVID-19 chemosensory impairment is also lacking.

We used binary, categorical and continuous self-report measures to determine the chemosensory phenotype, along with other symptoms and characteristics, of COVID-19-positive (C19+) and COVID-19-negative (C19-) individuals who had reported recent symptoms of respiratory illness. Using those results in logistic regression models, we identified predictors of COVID-19 and recovery from smell loss. Finally, we propose the Olfactory Determination Rating scale for COVID-19 (ODoR-19), a quick, simple-to-use, telemedicine-friendly tool to improve the utility of current COVID-19 screening protocols, particularly when access to rapid testing for SARS-CoV-2 is limited.

Methods

Study design

This preregistered, [START_REF] Parma | Is olfactory loss a sensitive symptomatic predictor of COVID-19? A preregistered, crowdsourced study[END_REF] cross-sectional online study was approved by the Office of Research Protections of The Pennsylvania State University (STUDY00014904); it is in accordance with the revised Declaration of Helsinki, and compliant with privacy laws in the U.S.A. and European Union. Data reported here were collected between April 7 and July 3, 2020 from the Global Consortium for Chemosensory Research (GCCR) core questionnaire (Appendix 1 and https://gcchemosensr.org), [START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] an online crowdsourced survey deployed in 32 languages that used binary response and categorical questions (e.g. Appendix 1, Questions 6,9) and visual analog scales (e.g., Appendix 1, Question 13) to measure self-reported chemosensory ability and other symptoms in adults with current or recent respiratory illness. Data reported here include responses in Arabic, Bengali, Chinese (Simplified and Traditional), Danish, Dutch, English, Farsi, Finnish, French, German, Greek, Hebrew, Hindi, Italian, Japanese, Korean, Norwegian, Portuguese, Russian, Spanish, Swedish, Turkish, and Urdu.

Participants (272)

A convenience sample of 52,334 volunteers accessed the GCCR questionnaire; 25,620 met e criteria (≥19 years old, respiratory illness or suspicion thereof within the past two weeks). After applyi registered exclusion criteria, 15,747 participants were included in reported analyses (Figure 1). Based on the self-reported outcome of a COVID-19 lab test, participants were labeled as eithe (positive result) or C19-(negative result). The specific collider bias characterizing this sample (high fra C19+ participants and high prevalence of chemosensory disorders in both groups) underestimates the p correlation between smell loss and COVID-19 (Figure S1). Thus, it represents a conservative scenario the hypothesis that smell loss reliably predicts COVID-19 status. We benchmarked the GCCR datase representative samples collected with the Imperial College London YouGov Covid 19 Behaviour T (henceforth, YouGov; countries shared across datasets: Brazil, Canada, Denmark, Finland, France, Ge Italy, Mexico, Netherlands, Norway, Spain, Sweden, UK, USA; YouGov: N=8,674, GCCR: N=3,96 publicly available at https://github.com/YouGov-Data/covid-19-tracker). Benchmarking shows the S1). The country-wise fraction of C19+ participants is correlated (r~0.45) when responses from the same calendar week are aligned (Figure S2). These findings are in line with other comparisons between crowdsourced versus representative health data, [START_REF] Kraemer | Crowdsourced data collection for public health: A comparison with nationally representative, population tobacco use data[END_REF] confirming that trends identified in crowdsourced data reasonably approximate population data. Because the GCCR cohort is not demographically balanced, it should not be used to estimate prevalence. However, the representative YouGov cohort indicates globally ~33% of C19+ individuals report smell loss (Table S1).

Statistical analyses

Statistical analyses were performed in Python 3.7.6 using the pandas, [START_REF] Reback | pandas-dev/pandas: Pandas 1[END_REF] scikit-learn, [START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF] and statsmodels [START_REF] Seabold | Statsmodels: Econometric and statistical modeling with python[END_REF] packages. The data and annotated code is included as supplemental material and will be publicly available on GitHub (http://github.com/GCCR/GCCR002) upon publication. Missing values for covariates used in prediction models of COVID-19 status and smell recovery were imputed as follows: binary features = 0.5, numeric features = median, categorical variables = "Missing". Prediction targets themselves were never imputed. Responses incompatible with model generalization (e.g., open ended questions) were excluded. A one-hot encoding was applied to all categorical variables to produce binary indicators of category membership. L1regularized logistic regression (penalty α=1) consistently produced sparse models with comparable crossvalidation accuracy and were therefore the prediction test of choice. Model quality was measured using receiver operating characteristic (ROC) area under the curve (AUC). Cross-validation was performed in 100 random splits of 80% training set and 20% test set, and ROC curves are concatenated over each test set. ROC curves are computed on predicted probabilities from each model, circumventing the high-cardinality bias of AUC. For single feature models, AUC is independent of most modeling details, including all rank-invariant decisions. To correctly compute p-values for model coefficients, the normalized data were standardized (mean 0, variance 1) and then coefficients back-transformed to normalized form after fitting.

Results

Chemosensory loss associates with COVID-19

A preregistered replication of our prior study 8 confirmed that reported smell, taste, and chemesthesis abilities drop significantly in both lab-tested C19+ participants and those diagnosed by clinical assessment (Figure S3, Table S2).

Next, we compared lab-tested C19+ and C19-participants. C19+ participants reported a greater loss of smell (C19+: -82.5±27.2 points; C19-: -59.8±37.7 points; p=2.2e-46, extreme evidence of difference: BF 10 =8.97e+61; Figure 2A,B; Table S3), taste (C19+: -71.6±31.8 points; C19-: -55.2±37.5 points; p=6.7e-26, extreme evidence of difference: BF 10 =6.67e+24; Figure 2C,D; Table S3) and chemesthesis ability (C19+: -36.8±37.1 points; C19-: -28.7±37.1 points; p=1.6e-07, extreme evidence of difference: BF 10 =3182; Figure 2E,F; Table S3). However, both groups reported a similar degree of nasal obstruction (Figure 2G,H; Table S3). Self-reported changes in smell, taste, and chemesthesis were highly correlated within both groups (C19+: 0.71<r<0.83; C19-: 0.76<r<0.87) and orthogonal to nasal obstruction changes (C19+: r=-0.20; C19-: r=-0.13).

Prediction of COVID-19 status from survey responses Binary (yes/no) and categorical reported symptoms suggested that COVID-19 is more strongly associated with chemosensory than with non-chemosensory symptoms, including fever, cough, and shortness of breath, cardinal symptoms currently highlighted by the US Centers for Disease Control and Prevention (CDC)(Figure 3A). Using AUC to assess prediction quality (Figure 3B), we found that self-reported smell ability during illness, reported on a continuous scale, was the most predictive survey question for COVID-19 status(AUC=0.71). Changes in smell as a result of illness (the difference between smell ability during and before illness) was similarly predictive (AUC=0.69). Changes in taste ability (assessed via rating) were the next most predictive features (AUC=0.64-0.65) (Figure 3B). Models fit to the same data but with shuffled COVID-19 status consistently produced AUC~0.5 for all features. The most predictive non-chemosensory symptom, sore throat (which was negatively associated with COVID-19) was substantially less predictive (AUC=0.58) than the top chemosensory symptoms. Nasal obstruction was not predictive (AUC=0.52). Responses given on a continuous scale were more predictive (AUC=0.71) than binary responses to parallel questions (e.g., Appendix 1, Question 13 versus 14, FigureS5) (AUC=0.60-0.62), likely because a continuous scale contains a greater amount of diagnostic information (Figure S4).

Next, we examined which simple multi-feature model would best predict COVID-19 status. As some questions have highly correlated responses, the question most complementary to "Smell during illness" is unlikely to be one that carries redundant information. Adding "Days since onset of respiratory symptoms" (DOS) to "Smell during illness" (Smell Only) produced the largest incremental gain in predictive performance (AUC=0.72, +0.01 versus the Smell Only model) (Figure 3C).

We directly compared the Smell Only+DOS model to other candidate models. The Smell Only+DOS model (Figure 3D) yielded an equal or higher AUC than the model including the three cardinal CDC symptoms (AUC=0.55) or the full model using 70 features (AUC=0.72). Because the Smell Only+DOS model exhibits the same AUC as the full model it strikes a good balance between model parsimony and predictive accuracy for C19+. However, the Smell Only model also offers reasonable sensitivity of 0.85 (at specificity=0.51, cutoff=13 on the 100-point VAS) and/or specificity of 0.75 (at sensitivity=0.51, cutoff=1) as desired. By sharp contrast, fever has a sensitivity of only 0.54 with specificity of 0.49 and dry cough has sensitivity of 0.52 and specificity of 0.46. 5D). An ODoR-19 response of 3 indicates a borderline risk (OR=1.2).

Discussion

Self-reported smell loss was more common in C19+ than C19-participants, but present in both The use of a VAS to assess olfactory loss better predicted COVID-19 status than using a binary questi found that the best predictor of COVID-19-associated smell recovery, within the time frame captured survey (~40 days), was days since onset of COVID-19. th groups. stion. We ed by the The SARS-CoV-2 pandemic requires healthcare providers and contact tracers to quickly and reliably assess an individual's COVID-19 risk, often remotely. Thus, reliable screening tools are critical to assess a person's likelihood of having COVID-19 and to justify self-quarantine and/or testing recommendations. Indeed, some reports suggest that COVID-19-associated smell loss might be an indicator of disease severity. [START_REF] Paderno | Smell and taste alterations in Covid-19: a cross-sectional analysis of different cohorts[END_REF][START_REF] Yan | Self-reported olfactory loss associates with outpatient clinical course in COVID-19[END_REF] Current symptom criteria (e.g., fever, dry cough) are less specific than severe olfactory loss. Indeed, the value of our ODoR-19 tool lies in the high specificity of values ≤2 for indicating COVID-19 positivity, therefore representing a valuable addition to the current repertoire of COVID-19 screening tools. Those who receive a negative outcome from a COVID-19 viral test, yet report significant idiopathic smell loss, should be considered as high-priority candidates for COVID-19 re-testing.

Our online survey and sampling methodology likely selected participants with a heightened interest in smell and taste and/or their disturbances. This self-selection bias could be viewed as a limitation since the C19-group also showed chemosensory loss. However, finding difference between groups in a sample with a higher barrier for discriminating between C19+ and C19-supports the robustness of this tool when used in a typical clinical population; our collider bias analysis also suggests that our findings are likely conservative estimates (Figure S1, Table S1).

Our results suggest that chemosensory impairment has strong COVID-19 predictive value and is useful when access to viral testing is limited or absent. As with any self-report measure, veracity of self-reports cannot be guaranteed. However, the ability to screen individuals in real-time should outweigh this potential confound. [START_REF] Mermelstein | The Science of Real-Time Data Capture: Self-Reports in Health Research[END_REF] While objective smell tests are the gold standard for assessing olfactory function, [START_REF] Hummel | Sniffin' Sticks': Olfactory Performance Assessed by the Combined Testing of Odor Identification, Odor Discrimination and Olfactory Threshold[END_REF][START_REF] Doty | University of pennsylvania smell identification test: A rapid quantitative olfactory function test for the clinic[END_REF] they are costly, time-consuming to administer, and can require in-person interactions with potentially infectious patients. [START_REF] Doty | University of pennsylvania smell identification test: A rapid quantitative olfactory function test for the clinic[END_REF][START_REF] Oleszkiewicz | Updated Sniffin' Sticks normative data based on an extended sample of 9139 subjects[END_REF] By contrast, the ODoR-19 is free, quick, and can be administered in person or remotely. We cannot exclude that our C19-sample contains COVID-19 false negatives. [START_REF] Kucirka | Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure[END_REF] However, self-reported smell during illness distinguishes between C19+ and C19-, but not between randomly shuffled cases, suggesting that the difference between C19+ and C19-, even in a sample with over-represented chemosensory dysfunction, is substantial and can be captured via self-report.

Approximately half of the participants in the C19+ group recovered their sense of smell within 40 days from the onset of respiratory symptoms. This suggests the presence of at least two subgroups of patients: one that recovers quickly (<40 days, 40.9%) and another that may present a more variable time course of recovery (50.7%). Since these data are collected before the full recovery of all symptoms, we cannot offer a complete picture of recovery from olfactory loss in COVID-19-positive individuals, but they align with other early reports. [START_REF] Estomba | Patterns of smell recovery in 751 patients affected by the COVID-19 outbreak[END_REF] The COVID-19 pandemic will greatly increase the number of patients suffering from anosmia and other chemosensory disorders, [START_REF] Rawal | Prevalence and Risk Factors of Self-Reported Smell and Taste Alterations: Results from the 2011-2012 US National Health and Nutrition Examination Survey (NHANES)[END_REF] conditions that significantly affect quality-of-life, [START_REF] Smeets | Sense of smell disorder and health-related quality of life[END_REF][START_REF] Croy | Olfactory Disorders and Quality of Life--An Updated Review[END_REF] dietary behavior, [START_REF] Kershaw | Nutrition and taste and smell dysfunction[END_REF] cardiovascular health, [START_REF] Gallo | Associations of olfactory dysfunction with anthropometric and cardiometabolic measures: Findings from the 2013-2014 national health and nutrition examination survey (NHANES)[END_REF] and mental health. [START_REF] Croy | Olfaction as a marker for depression in humans[END_REF][START_REF] Malaty | Smell and taste disorders in primary care[END_REF] Thus, it is necessary to prepare healthcare providers to address the long-term needs of these patients.

Based on our results, we propose the use of the ODoR-19 tool, a quick, free, and effective smell-based screening method for COVID-19. This 0-10 rating scale accurately predicts COVID-19 in individuals without pre-existing smell and taste disorders (e.g., from head trauma, chronic rhinosinusitis [START_REF] Hummel | Smell and taste disorders[END_REF] ). ODoR-19 combines the utility of a continuous scale with the ease and speed needed for a screening tool. ODoR-19 is safe for remote administration during an illness with high viral spread and can precede and complement viral testing. This tool will improve screening for patients with limited or no access to medical care around the globe.

Supplementary Material Collider bias results in underestimation of an association between smell loss and COVID-19

As others have noted, [START_REF] Griffith | Collider bias undermines our understanding of COVID-19 disease risk and severity[END_REF] collider bias, resulting from selection or conditioning on variables involved in the analysis, may result in the distorted association between COVID-19 and candidate symptoms or patient attributes. In the present sample, it is likely that we have selected for both a higher probability of COVID-19 and a higher probability of smell and taste disorders than the population at large. However, rather than leading to an overestimation of the positive correlation between smell loss and COVID-19, collider bias is expected to lead to an underestimation of this correlation (Figure S1). If we consider the hypothetical scenario in which there is no association between smell loss and COVID-19 status in the general population, we would expect a distribution similar to that depicted in Figure S1A, where the correlation between the likelihood of smell change and likelihood of COVID-19 is r = 0. Based on our recruitment method, we expect that the participants who elected to complete the GCCR core questionnaire were likely to have COVID-19, smell loss, or both. We can simulate participant selection to reflect this hypothesis by censoring subjects which do not meet a fixed sum of smell loss and COVID-19 probabilities (i.e., the red dots are excluded from the calculation of the correlation; Figure S1B). As a result, the estimated correlation between smell loss and COVID-19 status originating from a population with r = 0 would be negative (Figure S1B). A similar scenario would manifest if the association between smell loss and COVID-19 status in the general population is positive (Figure S1C). Again, simulating the removal of participants with low likelihood of having COVID-19 and/or reporting smell loss would result in a bias of the estimated correlation towards more negative values (Figure S1D). This collider bias indicates that the positive correlation between smell loss and C19+ is underestimated in the present sample. Indeed, a direct comparison of the binary (y/n) smell loss questions in the two empirical samples yields an C19 odds ratio of 5.96 in the YouGov sample (Table S1) but only 4.89 for GCCR. Therefore, our analyses represent a conservative scenario for the prediction of C19+ and C19-based on chemosensory alterations. Column 1 includes the list of countries available in the YouGov database. Column 2 indicates the number of participants over the history of their survey up to July 3, 2020. Minimum N=1000 per time point. Column 3 reports the percentage of participants who reported C19+. Column 4 reports the percentage of participants who reported C19-. Columns 5-7 report the odds ratios using Smell Loss for either C19+ vs. C19-(Column 5, and as reported in Figure 5 based on the data of the GCCR survey), for C19+ vs. untested individuals (Column 6) and C19-vs. untested individuals (Column 7). Untested individuals in YouGov's survey are those who did not report to be sick but were contacted as representative participants of a country. Column 8 reports the probability of smell loss in the C19+ group. The first row indicates the Global average (across countries) weighted by sample size. The global odds ratio for Smell loss calculated from a binary question for the group C19+ vs. C19-is 6.72, which is greater than what we identify in the GCCR survey (OR for changes in smell (binary question) = 4.89). The OR for C19+ vs. untested individuals is 58 and lowers to 11 for C19-. This confirms that smell loss is also associated with other etiologies, but is not nearly as prevalent as in participants with C19+

How representative is the GCCR sample?

As with most COVID-19 studies, [START_REF] Seabold | Statsmodels: Econometric and statistical modeling with python[END_REF] the sample studied here is not representative of the general population. To better understand the extent to which this is the case, we computed a cross-correlation between GCCR and YouGov data. [START_REF]YouGov-Data/covid-19-tracker[END_REF] These data were aligned by weighting YouGov samples to achieve an identical survey date distribution to the GCCR samples. Specifically, GCCR survey dates were converted to a YouGov "week number" because YouGov surveys only weekly. The distribution of week numbers was computed for each country in the GCCR data. The YouGov data for the same country was then weighted by week number to match the corresponding GCCR distribution for that country. So, for example, if a country had 10 GCCR survey responses in week 1 of the YouGov survey period, and 30 in week 2 of that period, then the YouGov data in week 1 would be weighted at 25% and in the YouGov data in week 2 at 75%. This procedure was applied independently for each country, and the weights were used to compute a weighted mean COVID-19-positive rate for each country from the YouGov data. This was then directly compared against the raw COVID-19-positive rate for each country in the GCCR data. A lag (x-axis value in Figure S2) of 0 exactly refle above description. Other values of the lag indicate that the alignment was shifted: for example, a lag week means that the hypothetical GCCR responses above would be weighted 25/75 towards weeks 2 instead of weeks 1 and 2. Under the hypothesis that the COVID-19-positive rates in the two surve related, but may have different temporal dynamics, changing the lag allows these dynamics to be est

Sample description

Based on responses to question 7 of the GCCR survey ("Have you been diagnosed with COVID-19?", Appendix 1), participants can be split into six groups (see Figure 1). Participants who responded with Option 2 ("Yes -diagnosed with viral swab") or 3 ("Yes -diagnosed with another lab test") were classified as C19+; participants who responded with option 5 ("No -I had a negative test, but I have symptoms") were classified as symptomatic C19-; participants who responded with option 4 ("No -I was not diagnosed, but I have symptoms") were classified as C19 Unknown; participants who responded with option 6 ("No -I do not have any symptoms"), with option 7 ("Don't know"), or with option 8 ("Other") were classified as undefinable and excluded from the final analyses. To replicate our previous findings, [START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] we first compared individuals newly included in the GCCR dataset (responses from 14 May to 2 July, 2020, replication sample in Figure 1) with COVID-19 who were lab tested and those who were diagnosed by a clinician based on the self-reported quantitative changes in smell, taste, chemesthesis, and nasal obstruction (Figure S3). Participants with labtest confirmed C19+ did show slightly greater chemosensory deficits than did those diagnosed with C19+ clinically, but the difference was not clinically meaningful (smell: 4.4±28.6, p=2.7e-13) (Figure S3, Table S2).

We then focused our descriptive and predictive analyses of participants who received a positive (C19+) or a negative (C19-) lab test for COVID-19. We also computed descriptive and predictive analysis for the C19+ subsample who reported partial or full signs of recovery from their recent respiratory illness. Lastly, the unknown group was originally hypothesized as similar to the C19-group. Yet the ratings of smell ability during illness suggest that the majority of these participants has a smell profile closer to C19+ than C19-(Figure S4).

To maximize the validity of the COVID-19 diagnosis in our sample, we therefore excluded the C19 Unknown group from further analyses.

Replication of previous analyses

The replication of Parma et al. [START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] used the same Bayesian linear regression approach with Cauchy pr sqrt(2)/2]. This approach is appropriate for estimating the strength of the evidence in support of the alte hypothesis: the clinical assessment and the lab test C19+ groups show similar smell, taste, chemesthe nasal obstruction changes before vs. during the illness. The interpretation of the Bayes factors BF follo classification scheme proposed by Lee and Wagenmakers [START_REF] Lee | Bayesian Cognitive Modeling: A Practical Course[END_REF] and adjusted from Jeffreys [START_REF] Jeffreys | The Theory of Probability[END_REF] , which consid itive ck of lines ze prior [r = lternative hesis and llows the siders BF > 3 as moderate evidence, BF > 10 as strong evidence, BF > 30 as very strong evidence and BF > 100 as extreme evidence for H 0 or H 1 . In contrast to the prediction of the pre-registration, we found statistically significant differences between groups. However, the effect sizes are small and thus unlikely to be of practical importance.

Chemosensory characterization of C19+ and C19-

We asked how accurately COVID-19 status could be predicted from the survey responses. The data matrix had strictly non-negative values and was normalized (column-wise min=0, max=1) to apply regularization in an equitable fashion across features and give regression coefficients the same interpretation for each feature.

Compared with the main text, models with similar AUC values (but with non-zero coefficients for additional, likely spurious features) were obtained for smaller values of α, and inferior results for larger ones (which contained fewer or no non-zero coefficients). Quantitatively similar AUC values were obtained for other models predicting COVID-19 status using multiple features including ridge regression and random forest, but L1regularized logistic regression consistently produced sparser models with comparable cross-validation accuracy. Each logistic regression model included an intercept term and one or more normalized features. Each model attempted to predict, using the value of the response to a single question (and an additive constant), whether a subject reported a C19+ or C19-status. Coefficients in a logistic regression model can be interpreted as changes in odds, or as odds ratios when two values are compared. Each ROC curve -constructed using predictions on holdout test sets and concatenated over these test sets --summarizes the tradeoff between sensitivity (fraction of C19+ cases correctly identified) and specificity (fraction of C19-cases correctly identified) as the threshold value for the predictor is varied.

Value of using a scale rather than a binary response to detect C19+

We quantified the information entropy for each survey question used the following standard equation: Iൌ Prediction of recovery from COVID-19-associated smell loss

We applied the same predictive modeling framework used in Figure 4 to try to predict smell recovery i participants. In other words, we asked which survey responses predicted that a subject would fall i Recovered Smell rather than the Persistent Smell Loss cluster, given both smell loss during the disea C19+ status. The only predictive feature of any practical significance was "Days Since Onset" of resp symptoms (AUC=0.62), indicating that those who experienced their first respiratory symptoms less rece more likely to have Recovered Smell (Figure S6A). Adding additional features to the model provided improvement (AUC=0.65 for the optimal model), but overall it was difficult to predict whether a C19+ par would exhibit Recovered Smell or Persistent Smell Loss based on the data available (Figure S6B). Ta includes the means and SD by recovery group for C19+ and C19-participants.

is more soliciting orrespond ked on a ins lf-reports for the full y in C19+ ll into the ease and espiratory cently are d modest articipant Table S3 Figure

Figure 1 .

 1 Figure 1. Flow diagram showing the demographics of participants included and excluded in the present analyses. Participants included in the prediction of COVID-19 status are highlighted in blue. Participants included in the smel recovery models are highlighted in green. Participants included in the replication of our prior work are highlighted in orange. N = number of participants; yo = age in years; W = women; M = men. Gender percentages do not include participants who answered "other" or "preferred not to say". Participants described in the green boxes are a subse those described in the boxes listed above them.

  positive association between smell loss and C19+ (FigureS1, Table

Figure 2 .Figure 3 .

 23 Figure 2. Chemosensory ability and nasal obstruction in C19+ and C19-participants. Self-reported smell (A,B (C,D), chemesthesis (E,F), and nasal obstruction (G,H; formulated as "How blocked was your nose?") before an respiratory illness in C19+ (darker shades) and C19-(lighter shades) participants. Ratings were given on 0-10 analog scales. Left panels (A,C,E,G) show mean values. Right panels (B,D,F,H) show distributions of the change (during minus before). Thicker sections indicate relatively more subjects (higher density of responses). The thic horizontal bar indicates the median, the shaded area within each violin indicates the interquartile range. E represents the rating of a single participant. ,B), taste and during 100 visual ge scores thick black Each dot

Figure 4 :Figure 5 .

 45 Figure 4: Smell loss, recovery, and time course. (A, B) Joint distribution of smell loss (during minus before illness and smell recovery (after minus during illness ratings) for C19+ (A) and C19-(B) participants. Darker color indicate higher probability density; the color map is shared between (A) and (B); dashed lines are placed at a third of the wa across the rating scale to aid visualization of the clusters. Severe smell loss that is either persistent (lower left) or recovered (upper left) was more common in C19+ than C19-. n indicates the number of participants in each panel. indicates the percentage of participants of the given COVID status in each quadrant. (C) In C19+ participants who their sense of smell (Recovered Smell + Persistent Smell Loss), the degree of smell recovery (right y axis) increas ~30 days since onset of respiratory symptoms before plateauing; the degree of reported smell change (left y axis) vary in that window of observation. Solid lines indicate the mean of the measure, the shaded region indicates the 9 confidence interval. s ratings) tes a way el. % o lost ased over s) did not e 95%

Figure S1 .

 S1 Figure S1. Collider bias leads to underestimation of the positive correlation between smell loss and COVID-19-positiv (A) Hypothetical scenario depicting no relationship between smell change and likelihood of COVID-19 positive status. Bl indicate individual potential subjects, each of whom has a latent likelihood of COVID-19 and of smell loss. (B) Hyp scenario depicting the emergence of a negative correlation between smell change and likelihood of COVID-19 positiv following a baseline lack of correlation, if participants with greater smell loss and/or COVID-19 positive are prefe included in the sample. Red dots indicate subjects not observed due to this selection bias; subjects observed remain (C) Hypothetical scenario depicting a positive relationship between smell change and likelihood of COVID-19 positive st Hypothetical scenario depicting the emergence of a negative correlation between smell change and likelihood of CO positive status following a positive baseline correlation, if participants with greater smell loss and/or COVID-19 pos preferentially included in the sample. tive status. Black dots ypothetical itive status eferentially in in black. status. (D) COVID-19 ositive are

Figure

  S2 depicts the country-wise correlation in participants with a positive COVID-19 test results fraction between the two datasets, as a function of the lag between GCCR survey date and YouGov date. The country-wise C19+ fraction is correlated (r ~ 0.45) when responses from the same calenda are aligned, but diminishes outside of that window, showing both surveys capture a similar withintemporal component of the epidemic.

Figure S2 .

 S2 Figure S2. COVID-19 status in the GCCR cohort is correlated with a representative YouGov sample.

Figure S3 .

 S3 Figure S3. This figure describes a pre-registered replication of Parma et al, 2020 and includes only new data c between May 14th and July 3rd 2020 via the GCCR survey. (A-D) Changes in smell (A), taste (B), chemesthesis nasal blockage (D) during versus before in COVID-19-positive individuals (Groups 1, 2 and 3, see Figure 1). All s had a COVID-19-positive status either via lab test (darker shades) or via clinical assessment (lighter shades Principal component analysis shows that smell, taste, and chemesthesis changes in both the lab test (E) and assessment (F) groups) were orthogonal to blocked nose changes, i.e., the three chemosensory changes wer correlated across subjects whereas blocked nose changes were mostly uncorrelated. collected is (C) and ll subjects es). (E-F) nd clinical ere highly

∑

  ሺെ כ ݈݃ ଶ ሺ ሻሻ evaluated over the n response options. Re-binning to mimic new scales was achieved by dividing response values by a constant and rounding to the nearest integer. Relative mutual information was calculated by computing the mutual information between survey response and COVID status based on the following standard equation: ܫܯ ൌ ∑ ∑ ሺ כ ݈݃ ଶ ሺ ೕ ೕ ሻሻ ଶ where survey response options are indexed with i and the C19+/C19-status (two possible values) are indexed with j, and then dividing by the entropy available from that same C19 status distribution, calculated using the first equation. Results indicate that soliciting responses on either a continuous 100-point scale or a downsampled 10-point numeric version of the scale i informative about symptoms themselves and about COVID-19 status (given the symptoms) than s binary responses (Figure S5).

Figure

  Figure S5. (A)Relative information available from the distribution of responses to the two primary "Smell" survey questions. Binary refers to the yes/no question about symptomatic smell loss. A relative information of 1 would cor to a question whose response is perfectly informative about COVID-19 status. By contrast, a similar question aske numeric scale (0-100, the original scale; or a hypothetical 10-point scale obtained by rounding responses) contains substantially more information due to the resolution of the scale. A 10-point scale may be familiar from clinical self of pain. (B) The relative mutual information about COVID status contained in the survey response is also higher fo numeric scale or the hypothetical 10-point scale than for the binary question.

  Figure S6. COVID-19 recovery. Similar to Figure 1, but self-reported smell (A,B), taste (C,D), chemesth (E,F), and nasal blockage(G,H) during and after respiratory illness in C19+ (darker) versus C19-(lighter) (A,C,E,G) mean values during and after respiratory illness, respectively. (B,D,F,H) Change (after minus as a distribution over subjects. thesis er). s during)

  Recovery from smell loss was modest (approximately half the initial average loss) in C19+ participants with full or partial resolution of respiratory symptoms. Overall, self-reported, post-illness olfactory ability was still lower for C19+ (39.9±34.7) than C19-(52.2±35.2, p=2.8e-11, FigureS6A). However, the mean recovery of smell (after illness relative to during illness) was greater for C19+ (30.5±35.7) than C19-(24.6±31.9, p=0.0002,

	Recovery from smell loss
	Figure S6B). A similar but smaller effect of COVID-19 status on recovery was observed for taste (Figure S6C,
	D), while little to no association with COVID-19 was observed for recovery of chemesthesis (Figure S6E,F) or
	nasal obstruction (Figure S6G,H). When illness-induced change in olfactory function (during minus before
	illness) and recovery of olfactory function (after minus during illness) were evaluated, we identified three
	respondent clusters: those self-reporting no loss of smell (Intact Smell), those reporting recovery from smell
	loss (Recovered Smell), and those reporting smell loss without recovery by up to 40 days (Persistent Smell
	Loss, Figure 4, Table S3). Intact smell was reported by only 8.5% of the participants in the C19+ group but by
	27.5% in the C19-group (p=3.8e-31). A greater proportion of C19+ participants were included in both the
	Recovered Smell group (C19+: 40.9%, C19-: 33.3%; p=4.9e-10) and the Persistent Smell Loss group (C19+:
	50.7%, C19
	ramer's V)
	ponse and
	in red are
	io < 1). (B)
	ranked by
	w greater
	talic) were
	(in AUC)
	ays Since
	. A model
	his feature
	ing). "Full"
	ilar to the

-: 39.2%; p=5e-5; Figure

4A, B

). C19+ participants in both the Recovered Smell and Persistent Smell Loss clusters reported a similar extent of olfactory loss, irrespective of time since respiratory symptom onset. By contrast, the rate of self-reported smell recovery increased over time, with a plateau at 30 days (Figure

4C

). Finally, DOS was the best predictor (AUC=0.62) between the Persistent Smell Loss and the Recovered Smell groups (Figure

S6A

, Table

S3

).

Simple screening for COVID-19: the Olfactory Determination Rating scale in COVID-19 (ODoR-19) (136)

Our results indicate that a continuous rating of current olfactory function is the single best predictor of COVID-19 and improves the discrimination between C19+ and C19-over a binary question on smell loss. For example, the Smell Only model can reach a specificity of 0.83 at the low end of the VAS (sensitivity=0.36, cutoff=0). We propose here a numeric variation of the rating scale (0-10), the ODoR-19, that can be administered in person or via telemedicine to improve early COVID-19 screening for individuals without preexisting smell and/or taste disorders. Responses to the ODoR-19 scale ≤2 indicate high odds of COVID-19 positivity (4<OR<10, Figure

Table S1 .

 S1 Comparison with the representative YouGov database shows that the GCCR sample underestimates the positive correlation between smell loss and COVID-19 positive status.

		N	% C19+	% C19-	OR (C19+ vs C19-)	OR (C19+ vs Not Tested)	OR Tested) (C19-vs Not	p(Smell Loss | C19+)
	Global	10939 5	0.51	3	5.96	49.7	8.33	0.33
	Brazil	4167	0.91	5.4	8.39	33.9	4.04	0.47
	Canada	5524	0.47	3.4	7.76	71.6	9.22	0.35
	Denmark	5839	0.36	5.7	9.35	77.8	8.32	0.33
	Finland	5927	0.32	1.9	1.85	27.8	15.1	0.21
	France	9820	0.46	1.7	7	57.9	8.27	0.29
	Germany	9468	0.49	2.1	4.56	94.2	20.7	0.37
	Italy	9790	0.33	2.8	9.17	50.8	5.54	0.22
	Mexico	5840	0.24	3.5	8.91	27.6	3.1	0.21
	Netherlands	3822	1.2	2.7	2.73	16.1	5.92	0.3
	Norway	5794	0.86	4.4	5.82	51.2	8.8	0.32
	Spain	9789	0.37	3.5	5.81	43.5	7.48	0.33
	Sweden	9741	0.44	2	3.69	38	10.3	0.35
	United Kingdom	13565	0.15	1.2	23.1	108	4.66	0.5
	United States 10309	1.2	4.8	4.61	54.7	11.9	0.34

Table S2 .

 S2 Differences between lab-tested and clinically-assessed COVID-19-positive participants on changes in smell, taste, chemesthesis and nasal blockage. Δ indicates the mean difference in change between lab-test and clinically-assessed COVID-19-positive subjects, while σ indicates the standard deviation. D indicates effect size (Cohen's D). p indicates p-value from a Mann-Whitney U-test.

		Smell	Taste	Chemesthesis	Change in
		Change	Change	Change	Nasal Blockage
	Δ	-4.4	-3.4	0.37	3.9
	σ	0	0	37	33
	seΔ	-0.048	-0.037	0.0041	0.043
	D	-0.15	-0.1	0.01	0.12
	p	2.70E-13	2.00E-06	0.38	4.00E-09
	Change means the rating "before" illness minus the rating "during" illness on the 0-100 visual-analog
	scale.

Acknowledgements

The authors wish to thank all study participants, patients, and patient advocates that have contributed to this project, including members of the AbScent Facebook group. The authors wish to thank Micaela Hayes, MD for her input on the clinical relevance of this project, Shannon Alshouse and Olivia Christman for their help in implementing the survey, Sara Lipson for her support, and the international online survey research firm YouGov for providing data gathered with the Imperial College London YouGov Covid 19 Behaviour Tracker.

Funding Deployment of the GCCR survey was supported by an unrestricted gift from James and Helen Zallie to support sensory science research at Penn State. Richard C. Gerkin is supported by NINDS (U19NS112953) and NIDCD (R01DC018455). Paule V. Joseph is supported by the National Institute of Nursing Research under award number 1ZIANR000035-01. PVJ is also supported by the Office of Workforce Diversity, National Institutes of Health and the Rockefeller University Heilbrunn Nurse Scholar Award. Vera V. Voznessenskaya is supported by IEE RAS basic project 0109-2018-0079. Mackenzie Hannum is supported by NIH T32 funding (DC000014). Masha Niv is supported by Israel Science Foundation grant #1129/19.

Conflict of interest

Richard C. Gerkin is an advisor for Climax Foods, Equity Compensation (RCG); John E. Hayes has consulted for for-profit food/consumer product corporations in the last 3 years on projects wholly unrelated to this study; also, he is Director of the Sensory Evaluation Center at Penn State, which routinely conducts product tests for industrial clients to facilitate experiential learning for students. Since 2018 Thomas Hummel collaborates with and received funding from Sony, Stuttgart, Germany; Smell and Taste Lab, Geneva, Switzerland; Takasago, Paris, France: aspuraclip, Berlin, Germany. Christine E. Kelly is the founder of AbScent, a charity registered in England and Wales, No. 1183468. Christophe Laudamiel has received fundings from scent related institutions and corporations, however for work totally unrelated to the field of the present study.

APPENDIX 1 GCCR core questionnaire

The core questionnaire of the Global Consortium for Chemosensory Research (GCCR) has been deployed in Compusense Cloud in 32 languages. The questionnaire was published previously [START_REF] Parma | More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis[END_REF] and also appears in the NIH Office of Behavioral and Social Sciences Research (OBSSR) research tools for COVID-19. [START_REF]COVID-19 BSSR Research Tool[END_REF] Responses to the GCCR core questionnaire in 23 languages were collected between April 7 and July 2, 2020 and included in the final dataset, on which we conducted the analyses reported in this paper.


Mirna Adriani

Yeow Wei Choong

Ba Hung Ngo

Laurent D'orazio
email: laurent.dorazio@univ-bpclermont.fr

Dominique Laurent
email: dominique.laurent@u-cergy.fr

Nicolas Spyratos
email: nicolas.spyratos@lri.fr

Optimized Data Management for E-Learning in the Clouds Towards Cloodle

Keywords: H.2.4 [Systems]: Distributed databases, Parallel databases; H.3.4 [Systems and Software]: Distributed systems Algorithms, Design Data Management, Cloud Computing, Big Data, Optimization, E-Learning

Cloud computing provides access to "infinite" storage and computing resources, offering promising perspectives for many applications, particularly e-learning. However, this new paradigm requires rethinking of database management principles in order to allow deployment on scalable, easy to access infrastructures, applying a pay-as-you-go model in which failures are not exceptions but rather the norm. The GOD project aims to provide an optimized data management system for e-learning in the cloud by rethinking traditional database management techniques, extending them to consider the specificities of this paradigm.

INTRODUCTION

Cloud computing [START_REF] Armbrust | A view of cloud computing[END_REF] aims to tackle increasing needs of computing and storage resources, in a userfriendly way and has attracted increasing interest, in particular by major IT companies (Google, Microsoft or Amazon). Clouds thus enable to envison data management at an unexpected scale and in diverse contexts. In particular, clouds are quite promising for e-learning, referring to educational technology for learning and teaching.

However, clouds' performance usually relies on brute force (i.e. using more nodes and more powerful nodes) thus leading to high cost and suboptimal resource management. Performance optimization has been studied for years in databases using methods such as indexing, view materialization or cache. These methods could also help improve the performance in the cloud. In particular, materialized views and semantic cache could enable to rewrite queries so that to reuse results from previous requests. However, existing solutions, relying on well known languages (SQL, Xpath, etc.) or data models (relational, object, etc.), are not suited to cloud data management, which uses key-value stores or analysis systems based on large scale distributed file systems.

The GOD project aims to propose Cloodle, an optimized cloud version of the Moodle e-learning environment. The main contributions of Cloodle are an e-learning system relying on a cloud data back end, an adaptable semantic cache to be used with rewriting rules, cost-and preference-aware query processing.

The remainder of this paper is organized as follows. Section 2 motivates the GOD project. Then Sections 3 and 4 detail sophisticated caching strategies while Sections 5 and Section 6 focus on cost-and preference-aware query processing. Finally, in Section 7, we conclude the paper.

CONTEXT AND MOTIVATION

E-learning is a term used to include all forms of educational technology for both learning and teaching. This approach in learning and teaching is particularly suitable for distance learning since technologies can provide the solution when the environment is as such that the source of knowledge (i.e. information) and the learners are separated by time or distance, or both.

The e-learning technologies have been evolving alongside with the advancement of the World Wide Web. As more and more courses are provided online through the Internet, one can study without attending classes. Learners are able to complete their studies whenever and wherever without having to travel or to participate at specific time e.g. classes according to the timetable provided. In the United States, over a third of all students enrolled in postsecondary education took an online course for credit in fall 2011 [START_REF]Babson survey research group[END_REF]. This new model requires a massive data management system.

One of the widely used e-learning platforms for institutions of higher learning is Moodle 1 . This platform is an Open Source Course Management System where developers are allowed to make modifications based on their needs with certain terms and conditions. Moodle provides blended learning opportunities as well as platforms for distance learning courses. Many universities and colleges use Moodle as the online learning system in their daily learning and teaching activities.

The main goal of the GOD project is to supply Cloodle, a cloud version of Moodle relying on an optimized data management system. The first task of this project consists in substituting the MySQL backend currently used in Moodle with a cloud data management system with respect to the data and the queries to be processed. Several systems exists. Some of them consists on large scale solutions providing a simplified query interface (usually a subset of SQL), like Amazon SimpleDB [3] or DynamoDB [START_REF]Dynamodb[END_REF]. Less scalable but fully relational approach are also available, such as Amazon RDS [START_REF]Rds. web page[END_REF] and SQL Azure [START_REF]Sql azure. web page[END_REF]. Data intensive analysis tools such as Pig [START_REF] Olston | Pig latin: a not-so-foreign language for data processing[END_REF], Hive [START_REF] Thusoo | Hivea petabyte scale data warehouse using hadoop[END_REF], SCOPE [START_REF] Chaiken | Scope: easy and efficient parallel processing of massive data sets[END_REF] or Jaql [START_REF] Beyer | Jaql: A scripting language for large scale semistructured data analysis[END_REF], relying on massively parallel execution environment like MapReduce [START_REF] Dean | Mapreduce: Simplified data processing on large clusters[END_REF], or its open source version Hadoop [START_REF]Apache. Hadoop[END_REF] have been developped. Cloud data management system's performance usually relies on brute force, using more nodes or/and more powerful nodes. This leads to high cost and suboptimal resource management. Performance optimization has been studied for years in databases. In this project, we particularly aims at studying four techniques: semantic caching 3, rewriting 4, cost-based optimization 5 and querying with preferences 6.

SEMANTIC CACHING

Semantic caching [START_REF] Keller | A predicate-based caching scheme for client-server database architectures[END_REF][START_REF] Dar | Semantic data caching and replacement[END_REF] manages a cache as a set of query results. When a query is posed, it is decomposed into two disjoint queries: the probe query retrieves the result already present in the cache and the remainder query retrieve missing objects.

Semantic caching has been studied in several contexts: distributed databases [START_REF] Keller | A predicate-based caching scheme for client-server database architectures[END_REF][START_REF] Dar | Semantic data caching and replacement[END_REF], web [START_REF] Chen | Xcache: a semantic caching system for xml queries[END_REF][START_REF] Chidlovskii | Semantic caching of web queries[END_REF][START_REF] Lillis | Cooperative xpath caching[END_REF] and grid computing [START_REF] Orazio | Semantic cache for pervasive grids[END_REF]. These solutions differ in several ways. The cache can thus store query results [START_REF] Dar | Semantic data caching and replacement[END_REF] called semantic regions or semantic segments, objects to be strongly [START_REF] Keller | A predicate-based caching scheme for client-server database architectures[END_REF] or independently [START_REF] Orazio | Semantic cache for pervasive grids[END_REF] associated to and possibly shared by predicates. Some of them focus on a specific data structure like XML [START_REF] Chen | Xcache: a semantic caching system for xml queries[END_REF], [START_REF] Lillis | Cooperative xpath caching[END_REF]. Efficient research, via signature files has been proposed for keyword based and conjunctive queries. In a previous work, we deployed P2P semantic caches, called CoopSC [START_REF] Vancea | Cooperative database caching within cloud environments[END_REF] to highlight potential money savings in the cloud, in addition to traditional time and bandwidth consumption re-1 https://moodle.org/ ductions. All these techniques are complementary to our project and can be reused to provide finely tuned caches.

Using a cache framework, such as ACS [START_REF] Orazio | Adaptable cache service and application to grid caching[END_REF], GOD data management system will rely on tunable semantic caches, particularly, two adaptable main functionalities: query analysis and query evaluation. Query analysis refers to the semantic process of comparing submitted query with the content of the cache to deduce semantic overlap or mismatch, and identifies one or more cache entries to be used to answer a submitted query. Query evaluation consists of operators (selection, projection, order, group by, etc.) to locally evaluate queries on the cache content.

REWRITING

The problem of optimizing semantically related queries has received a lot of attention during the last decades. Main references are presented into a wide survey on this topic [START_REF] Halevy | Answering queries using views: A survey[END_REF]. Our approach differs from these solutions in two main ways. First, to the best of our knowledge, because [START_REF] Laurent | Rewriting aggregate queries using functional dependencies[END_REF] is the first approach to OLAP query rewriting in which the content of the cache is optimized. Second, our approach is meant to take into account specificities of NoSQL systems (considering not only the well known relational model) and their associated query languages.

Another approach to optimizing query processing is to pre-compute queries, also known as materialized views, store the corresponding answers in a cache and reuse them in the query evaluation process. In [START_REF] Laurent | Rewriting aggregate queries using functional dependencies[END_REF], we have proposed novel solutions to answer queries using views in a common formalism based on the partition semantics [START_REF] Spyratos | The partition model: A deductive database model[END_REF]. Every OLAP query Q is associated to a partition Π(Q), whose blocks are the sets of tuples that are grouped together by the GROUP BY clause in Q when processing aggregates. Given a query Q, the set of all queries Q associated with the same partition as Q can be characterized using the functional dependencies that hold on the database. In addition, partitions allow to compare queries and such comparisons can be expressed using functional dependencies. Contrary to all other approaches, using such theoretical result in our solution, the answer to Q is not stored , but it is augmented by (1) attributes occurring in the closure of the attributed set of the answer to Q and (2) attributes storing aggregate values not occurring in Q. First, this makes it possible to rewrite as many queries as possible using a given query Q with limited extra storage. Then a provided actual rewriting method enable to easily answer these queries. Finally, these rewriting techniques can be used to efficiently manage the cache content, storing mutually non redundant queries.

However, none of them takes into account properties such as elasticity and pay-as-you-go.

COST MODELS AND OPTIMIZATION

GOD will consider deploying e-learning systems in public clouds and a pay-as-you-go model. This model leads to consider data management in general, and optimization particularly, from a novel point of view. Recent approaches [START_REF] Dash | An economic model for self-tuned cloud caching[END_REF][START_REF] Kantere | Predicting cost amortization for query services[END_REF][START_REF] Upadhyaya | How to price shared optimizations in the cloud[END_REF] have addressed the challenge of selecting the optimizations to implement and the way to price them in a data shared environment. These solutions studied the optimization in a shared environment at a higher level than we do in the GOD project. Indeed, cloud providers such as Amazon, Google, and Microsoft, supply a pool of resources, like hardware (CPU, storage, networks), development platforms or services. Each provider offers different pricing and services. Then this pricing model may be consider from different points of view.

One of our preliminary prior work provided cost models, inspired from AWS's offer, for materialized views in the cloud [START_REF] Nguyen | Cost models for view materialization in the cloud[END_REF]. In our simplified model, the total cost C for cloud data management is:

C = Cc + Cs + Ct. (1)
In this model, Cc, Cs and Ct are the sum respectively of computing costs, storage costs and data transfer costs, depending on several parameters such as the size of the data set, the pricing model, the type and number of nodes or the storage time.

In this project, we will first propose a uniform cost model for data management in the cloud, taking into account the pricing model of the main providers (Amazon, Microsoft and Google) and extending it with their specificities (licences, internal data transfers, etc.).

The GOD project will then consider cost-based optimization in two distinct cases. The first one is about general resources management, to provide the accurate level of resources (type and number of virtual machines) for the elearning platform to supply a given level of quality of service. The second one will focus on query processing in presence of semantic caches and rewriting rules.

We will use multi-criteria optimization to enable users specifying objectives: (1) minimizing the response time given a fixed budget, (2) minimizing the cost given a deadline or (3) finding a trade-off between time and cost. Exact solving methods, like Mixed Integer Programming (MIP) techniques, and metaheuristics, like GRASP, will be considered to solve mono-objective problems. Metaheuristics like NSGA-II will be studied to solve bi-objective problems.

PREFERENCES

An e-learning system usually consists of electronic catalogue. An electronic catalogue is simply a table describing a set of items though their attributes. Users query the table in search of items of interest. However, more often than not, the answer set of a query contains several hundreds or thousands of items, most of which might be of no interest to the user. One solution to this problem is to present the items of the answer set in a decreasing order with respect to user preferences.

We call preference based query, or simply preference query, an ordinary query together with a set of preferences expressed by the user online, together with the query. Our main objective is twofold: (1) present an approach to the specification and evaluation of preference queries; (2) describe a user-friendly interface that allows a user to express a preference query over tabular data, and receive an answer set that is ordered in decreasing order of preference.

The specification and evaluation of preference queries has received considerable attention in the past several decades, mainly in the areas of decision support and databases. The use of preferences for ranking alternative choices has been around in decision making and social choice theory since the 1950s (see [START_REF] Tsoukiàs | From decision theory to decision aiding methodology[END_REF] for a comprehensive survey). However, the use of preferences in ranking query answers is quite recent and their embodiment in a query language presents a Serial Make Color Mileage Price Year number of difficult problems (see [START_REF] Andréka | Operators and laws for combining preference relations[END_REF], [START_REF] Chomicki | Preference formulas in relational queries[END_REF] and [START_REF] Holland | Situated preferences and preference repositories for personalized database applications[END_REF] for some influential papers). We note here that the use of preferences is also related to an important recent concept in databases, namely to the so called "skyline queries" [START_REF] Börzsönyi | The skyline operator[END_REF]. One issue related to preference queries is the kind of preferences that a user can express. The nature of preferences can be either quantitative or absolute (e.g. I like BMW 80%, I like VW 70%, . . . ) difficult to express by casual users but easy to compute, or qualitative (e.g. I like BMW more than VW) easy to express by casual users and easy to infer by a machine. The persistence in time of preferences can be long term, discovered by the system (unobtrusively, from query logs) or declared explicitly by the user and stored in the so called user's profile, or short term, expressed online explicitly by the user. The nature and persistence in time are orthogonal features of preferences. In this work we focus on short term, qualitative preferences (i.e. online preferences). In what follows, we sketch our approach through an example.

Consider the electronic catalogue T of an Internet company selling used cars (shown in Table 1). Each line of this table corresponds to a car (i.e. each tuple is the metadata record of a car), and the table might contain several thousands or even tens of thousands of tuples.

Consider now a usual query Q = VW ∨ BMW over T with preference P = Red → Black (meaning "Red is preferred to Black" as a color).

The system may output first a table T Red containing all red cars which are either VW or BMW and then a table T Black containing all black cars which are either VW or BMW.In that case, the user can "consume" the answer one "piece" at a time, in descending order of preference and stop "consumption" at any time.

One advantage of doing so is that the system can adapt to preferences. For example, if the preference Red → Black is replaced by Black → Red then T Black will be shown first, followed by T Red . To be able to support such an interaction with the user, we can follow one of the following two approaches:

1. First compute the answer to Q and then use the preference P to partition the answer set into the tables T Red and T Black to be shown to the user in that order.

2. First use the preference P to rewrite Q into two queries Q Red and Q Black so that their answers are the tables T Red and T Black .

The first approach is commonly used in the literature. In our work we propose to use the second approach, in the spirit of [19] [30].

CONCLUSION

In this paper, we presented data management in the GOD project. We introduced e-learning in the cloud. Then we presented techniques to optimize data management, particularly caching strategies and rewriting rules, cost-based optimization using cloud-aware cost models and querying with preferences. In the near future, we will provide more details about the prototypes and results on the project Web site [START_REF] Team | God. web page[END_REF].

ACKNOWLEDGMENTS

Thanks to all the colleagues in CTU, ETIS, HELP University, LIMOS, LRI and University of Indonesia all the interesting discussions and for their extremely insightful remarks for improving this paper. This work is supported in part by the Ministère des Affaires étrangères and the CNRS (under grant STIC-Asie-12-GOD).

ADDITIONAL AUTHORS

Additional authors: Bruno Bachelet (Université Blaise Pascal, email:

bruno.bachelet@univ-bpclermont.fr), Christophe Duhamel (Université Blaise Pascal, email: christophe.duhamel@univ-bpclermont.fr),

Tao-Yuan Jen (Université de Cergy-Pontoise, email:

jen@ucergy.fr), Claudia Marinica (Université de Cergy-Pontoise, email: claudia.marinica@u-cergy.fr), Thuong Cang Phan (Université Blaise Pascal, email: cang@univbpclermont.fr), Gilbert Ooi Sin Cheak (HELP University, email: gilbert.ooi@helpcat.edu.my), Romain Perriot (Université Blaise Pascal, email: romain.perriot@univbpclermont.fr), Tran Thi To Quyen (Can Tho University, email:

tranthitoquyen@cit.ctu.edu.vn) and Loïc Yon (Université Blaise Pascal, email: loic.yon@univbpclermont.fr).

Table 1 :

 1 Table T, an e-catalogue for the sale of used cars (e.g. Autoreflex: http://pro.autoreflex.com/)

	1	BMW Black	35000	3800	2002
	2	Honda	Blue	63000	2900	2000
	...	...	...	...	...	...