Antoine Saverimoutou
email: antoine.saverimoutou@orange.com

Bertrand Mathieu
email: bertrand2.mathieu@orange.com

Sandrine Vaton
email: sandrine.vaton@imt-atlantique.fr

A 6-month Analysis of Factors Impacting Web Browsing Quality for QoE Prediction

Web browsing is one of the main Internet services, which has largely evolved over the last years. It now includes many different components (images in various formats, css, Javascript, Progressive Web Apps, etc.) and a web page is composed of many different resources provided by many servers located in various places. Knowing how web browsing content is delivered is crucial for better understanding the Quality of Experience (QoE) offered to end-users. Detecting the factors impacting this QoE is important to improve them but also to be able to predict the quality in order to anticipate degradations to it and propose remediations, which is the main goal of our study. For this, we present an analysis of a measurement campaign we have performed during 6 months (July 2018 -January 2019) on the Top 10,000 Alexa websites, which helps to identify the main factors which can impact the quality perceived by end-users. This set of factors has been confirmed by a machine learning process (decision tree), which gives as outputs the set of rules to predict the QoE. The evaluation of our decision tree-based model, on the Top 10,000-15,000 Alexa websites, never accessed before, shows that we can correctly predict the web browsing quality an end-user can get.

Introduction

Web browsing is a fast-paced and changing domain and needs to deliver a good experience to end-users since a plethora of applications and services are nowadays accessed through web browsers. The Web was originally designed to deliver static contents but has evolved dramatically towards dynamic web pages offering to end-users a built-in environment for education, gaming, video streaming or social networking [START_REF] Abbassi | Distributed content curation on the web[END_REF][START_REF] Snyder | Browser feature usage on the modern web[END_REF][START_REF] Sandinve | The 2018 Global Internet Phenomena Report[END_REF]. Large service companies (e.g Google, Mozilla, Apple, etc.) are following the trend of new technologies by enabling Virtual Reality, Virtual Machines or IoT (Internet of Things) applications embedded in the web browsers. They are delivered over HTTP/1.1 but also over new transport protocols such as HTTP/2 (Version 2 of the HyperText Transfer Protocol) [START_REF] Belshe | Hypertext Transfer Protocol Version 2 (HTTP/2), RFC 7540[END_REF], standardized in 2015, or QUIC (Quick UDP Internet Connection) [START_REF] Iyengar | QUIC: A UDP-Based Multiplexed and Secure Transport[END_REF][START_REF] Shade | HTTP/2 Semantics Using The QUIC Transport Protocol[END_REF] which is paving its way to standardization, as HTTP/3. Network operators or service providers need to offer the best possible QoS (Quality of Service) to their customers and for this, they deploy Content Delivery Networks (CDN) and proxy-based networking architectures, aiming to improve the web browsing delivery.

The question of measuring the quality when performing web browsing and the need to identify the main factors decreasing or enhancing it is then of prime importance. Measuring web browsing quality needs to be performed in a meticulous manner by taking into account the latest richexposed browsers' functionalities. Based on a large measurement campaign we performed over 6 months (July 2018 -January 2019), our first contribution provides an overview of today's web browsing delivery, including the number of resources, their location, the protocol used, etc. Secondly, we perform a detailed study of these parameters' impact on end-users' perceived quality, in order to highlight the factors which are the most critical for Web browsing. Thirdly, we use Machine Learning techniques to validate the identified factors and through a decision tree model, we identify the thresholds of these factors in order to automatically predict the end-users web browsing quality. Then, the identified set of rules of our model are applied on the Alexa top 10K-15K websites and our model proves to be efficient, predicting correctly more than 90% of measurements.

The paper is structured as follows: We first remind in section 2 the existing web metrics and related work meant to quantify and qualify web browsing. Through section 3 we introduce how our measurement campaign is performed, section 4 depicts how web browsing content is delivered to endusers and section 5 analyses the impacts of the parameters on web browsing quality. Section 6 presents our rules-based model to predict web browsing quality. We finally conclude in section 7.

Background and Related Work

Policies and processing algorithms used by web browsers to render web pages are all different. In order to bring uniform benchmarking indicators, standardization bodies such as the W3C, in collaboration with large service companies, have defined a set of web metrics to measure web page loading better. The Page Load Time 1 (PLT) is the time between the start of the request and when the entire web page has been loaded. The Resource Timing 2 provides information upon the downloaded resources unit-wise, such as the transport protocol used, size and type of object or some low level networking information. The Paint Timing 3 exposes the First Paint (FP) which is the time for a first pixel to appear on the end-user's web browser screen. The Above-The-Fold (ATF) 1 https://www.w3.org/TR/navigation-timing/ 2 https://www.w3.org/TR/resource-timing-2/ 3 https://www.w3.org/TR/paint-timing/ [START_REF] Grigorik | Above the fold time: Measuring web page performance visually[END_REF] exposes the time needed to fully load the visible surface area of a web page at first glance (making use of W3C information). The TFVR (Time for Full Visual Rendering) [START_REF] Saverimoutou | Web browsing measurements: An above-the-fold browser-based technique[END_REF] exposes the loading time of the visible portion of a website at first glance (without scrolling) by making use of networking information through the web browser's exposed HAR (HTTP Archive). The Speed Index and RUM (Real User Monitoring) expose a score representing the visible surface area occupancy of a web page. The Latency Amplification Factor (LAF) [START_REF] Avram | Latency amplification: Characterizing the impact of web page content on load times[END_REF] characterizes the content of a web page in terms of how it affects the page load time.

When end-users go to a website, third parties such as advertisement networks can impact web browsing quality [START_REF] Malloy | Ad blockers: Global prevalence and impact[END_REF][START_REF] Vattikonda | Empirical analysis of search advertising strategies[END_REF][START_REF] Pujol | Annoyed users: Ads and adblock usage in the wild[END_REF]. To measure QoE during web browsing sessions, several tools have been developed by the research community. FPDetective [START_REF] Acar | Fpdetective: dusting the web for fingerprinters[END_REF] uses a PhantomJS 4 and Chromium based automation insfrastructure, OpenWPM [START_REF] Englehardt | Online tracking: A 1-million-site measurement and analysis[END_REF] performs automated web browsing driven by Selenium5 while supporting stateful and stateless measurements and the Chameleon Crawler6 is a Chromium based crawler used for detecting browser fingerprinting. Fourth Party [START_REF] Mayer | Third-party web tracking: Policy and technology[END_REF] instruments the Mozilla-Firefox browser and Web Xray [START_REF] Libert | Exposing the hidden web: An analysis of third-party HTTP requests on 1 million websites[END_REF] is a PhantomJS based tool for measuring HTTP traffic. XRay [START_REF] Lécuyer | Xray: Enhancing the web's transparency with differential correlation[END_REF] and Ad-Fisher [START_REF] Datta | Automated experiments on ad privacy settings[END_REF] run automated personalization detection experiments and Common Crawl7 uses an Apache Nutch based crawler. Kaleidoscope [START_REF] Wang | Kaleidoscope: A crowdsourcing testing tool for web quality of experience[END_REF] is an automated tool to evaluate web features on a large scale focused on the web page style and page loading times. All these tools (among others) have largely contributed to the research field but when wanting to objectively quantify and qualify web browsing, there is a clear need to finely reproduce an end-user environment and usual interaction with the latest web browser functionalities, which they do not offer. We have thus developed our own tool highlighted in section 3 which offers fine-grained information through the latest implemented web metrics and data obtained from network capture and browser's offered HTTP Archive (HAR). We also collect information regarding the different remote web servers located all over the world, the downloaded resources' type, size and Internet protocol through which they are delivered at different stages of the web page loading process.

Web browsing includes 4 key actors, namely the enduser, the web browser, the network and the remote web server. While some research work [START_REF] Seufert | Studying the impact of HAS qoe factors on the standardized qoe model P[END_REF][START_REF] Sackl | Quantifying the impact of network bandwidth fluctuations and outages on web qoe[END_REF] describes how to qualify user-experience better, other studies investigate the impact of different Internet protocols on web browsing quality [START_REF] Cook | QUIC: Better For What And For Whom?[END_REF][START_REF] Lychev | How Secure and Quick is QUIC? Provable Security and Performance Analyses[END_REF][START_REF] Manzoor | How HTTP/2 is changing web traffic and how to detect it[END_REF][START_REF] Ruth | A first look at QUIC in the wild[END_REF][START_REF] Kakhki | Taking a long look at QUIC: an approach for rigorous evaluation of rapidly evolving transport protocols[END_REF][START_REF] Biswal | Does quic make the web faster?[END_REF]. Regarding the network, particular interest is given to the upload or download link when investigating bottlenecks [START_REF] Pujol | Back-office web traffic on the internet[END_REF][START_REF] Yang | Scout: A point of presence recommendation system using real user monitoring data[END_REF][START_REF] Anwar | Investigating interdomain routing policies in the wild[END_REF] and Naseer and al. [START_REF] Naseer | Inspectorgadget: Inferring network protocol configuration for web services[END_REF] propose a tool in order to analyze and better understand the actual configuration parameters employed by today's web servers. The PLT is the de facto used web metric but with the evolution of websites' characteristics, researchers ques-tion the versatility and objectiveness of this metric to measure end-user's QoE [START_REF] Bocchi | Measuring the quality of experience of web users[END_REF][START_REF] Kelton | Improving user perceived page load times using gaze[END_REF][START_REF] Varvello | EYEORG: A platform for crowdsourcing web quality of experience measurements[END_REF][START_REF] Gao | Perceived performance of top retail webpages in the wild: Insights from large-scale crowdsourcing of above-the-fold qoe[END_REF][START_REF] Enghardt | Web performance pitfalls[END_REF] emphasizing that what the user really sees on the screen should be primarily measured. Other research work proposes ATF browserbased measuring techniques [START_REF] Hora | Narrowing the gap between qos metrics and web qoe using above-thefold metrics[END_REF][START_REF] Saverimoutou | Web browsing measurements: An above-the-fold browser-based technique[END_REF] in order to measure the websites' visible portion loading time. When studying web browsing performance as a whole, different on-market web browsers, Internet protocols, network bandwidths and types of websites need to be taken into account. We have thus focused our research work by taking into account the mostly used on-market web browsers, studied the impact of different Internet protocols (HTTP/1.1, HTTP/2 or QUIC), used different types of residential network access (ADSL, Wi-Fi, Fiber) and assessed the Top 10,000 Alexa websites. Our measurements include all the web metrics defined by the W3C and the TFVR (Time for Full Visible Rendering) [START_REF] Saverimoutou | Web browsing measurements: An above-the-fold browser-based technique[END_REF].

Measurement Campaign Setup

Our measurement campaign has been performed with the tool Web View [START_REF] Saverimoutou | Web view: Measuring and monitoring representative information on websites[END_REF]. Web View is a measurement platform whose infrastructure includes probes and a public visualization website 8 illustrating the obtained measurement results for a set of websites. The Web View probe is a userorientated measurement tool whose main objective is to perform automated web browsing sessions and measure representative information of web pages in order to better qualify and understand web browsing, both in terms of quality and delivery. Each test can be configured with several parameters, such as the web browser, the access network type on which the probe is connected, the list of websites to measure, the preferred transport protocol to get the contents (HTTP/1.1, HTTP/2, QUIC), etc. For this study, we deployed probes in France, three different desktop machines (CPU Intel Core i5 and 8 GB RAM) connected to three different user-representative residential network accesses, i.e ADSL (10Mbps down, 1Mbps up), Wi-Fi (200Mbps down, 160Mbps up) and FTTH Fiber (800Mbps down, 300Mbps up). The Wi-Fi network access (802.11 b/g/n, 2.4Ghz and 450Mbit/s max) is provided by a homebox, connected to the Fiber operational network. Our probes are located in the same room as the Wi-Fi homebox. ing HTTP/2 we deactivate QUIC but allow fallback to HTTP/1.1 (not all content-servers are HTTP/2-enabled); when requesting QUIC, we allow fallback to HTTP/1.1 and HTTP/2 for non-UDP web servers. We also define another mode, the QUIC Repeat. This mode favors 0-RTT UDP and 1-RTT TCP by firstly performing a navigation to the website, closing the browser, clearing the resources' cache but keeping the DNS (Domain Name System) cache and going once more to the website when we collect measurements. For all our measurements, the resources' cache is always emptied at the start and end of web browsing session, and we have defined a timeout of 18 seconds (this value has been set since it is a very long time to wait for a web page and after more then 18 sec, all end-users will have given up the browsing).

For every visited website, Web View probes measure and compute 84 parameters 9 . Amongst them, we have 4 different loading times, namely the First Paint (FP), Time for Full Visual Rendering (TFVR), the processing time (DOM time) and the Page Load Time (PLT). We also collect network traffic from which we investigate the corresponding DNS time for the main web page domain and through the collected HAR 10 we assess the overall number of objects downloaded and their MIME (Multipurpose Internet Main Extensions) type. From the exposed remote servers' IP address correlated to MaxMind GeoIP2 database 11 and RIPE NCC database 12 , we estimate the continents from which these resources are downloaded. Web View also identifies if the resources are delivered with secure connections or not, the number of domains serving the contents and the category of the website as referenced by Alexa. As per the different collected timings, which indicate a web page page loading progression through time, we also collect the number of resources downloaded in these periods of time as well as the Internet protocol through which they are delivered. After our 6-months campaign on the top 10,000 Alexa websites, we got 244 Million different measurements, representing the measurement of 9597 distinct websites. 403 distinct websites were discarded due to the small amount of web browsing results (these websites were either unavailable, blocked as per our geographic location or not responding in less than 18 seconds). Our dataset then includes measurements for websites having their main web page estimated to be in North America (NA) for 52.23%, in Europe (EU) for 28.44%, in Asia (AS) for 16.22%, and in South America (SA) for 1.10%.

Web Browsing Delivery Analysis

We present in this section an analysis of our collected measurements and highlight the main evolutions of web browsing delivery with regards to previous studies [START_REF] Butkiewicz | Characterizing web page complexity and its impact[END_REF][START_REF] Avram | Latency amplification: Characterizing the impact of web page content on load times[END_REF] which have identified that the category of a website (as per Alexa ranking), number of objects embedded in the web page and amount of web servers delivering content has an impact on web browsing quality. A part of our dataset is made public at https://webview.orange.com/public/dataset.zip.

What are these objects ?

The web pages are more complex nowadays and we wanted to evaluate their evolution, compared to what it was few years ago [START_REF] Butkiewicz | Characterizing web page complexity and its impact[END_REF][START_REF] Butkiewicz | Understanding website complexity: measurements, metrics, and implications[END_REF][START_REF] Ihm | Towards understanding modern web traffic[END_REF]. When a main web page is located in Europe, on average between 2 and 47 resources need to be downloaded for less than 25% of the websites and between 125 and 230 resources need to be downloaded for more than 75% of these websites. Since 2014, the amount of downloaded resources composing a web page has increased by 17% on average for the Alexa websites ranked 1-2000 and by 31% on average for websites ranked 5000-10000. Going deeper into the question from where the resources are downloaded, (Fig. 1a) presents the location of the servers. On average, when a main web page is located in North America, a mean number of 72 resources is downloaded , mainly from EU and from NA. When a main web page is in Asia, an average number of 78 resources is downloaded, about half from Asia, and the other half from NA and EU. The mean distributions help in profiling the overall location of the content servers for a European end-user. The Fig. 2 provides the complete distribution of downloaded resources for the 4 continents where the main home page is. The Fig. 2a shows that when a main web page is located in North America, for 50% of the measurements, more than 8 objects are downloaded from EU and 50 objects from NA. On the other hand, the Fig. 2d shows that when a main web page is in Asia, for 75% of the different measurements, up to 65 objects are downloaded from AS, 20 objects from EU and 27 objects from NA. We can observe that for a European end-user, many objects are downloaded from Europe, which is due to Content Delivery Networks. For main web pages located in Asia, we can detect that many resources are coming from Asia, which could impact the quality, because of the network delay.

Following the question about what these objects are, Fig. 1b represents the breakdown of the downloaded objects by content MIME types of 8 random websites belonging to different categories as referenced by Alexa. While images occupy most of the time the highest distribution type of resources, on average web pages (except Search-Engines category) are composed of 4 css, 5 scripts, 16 images and 2 xml. We identified that the average number of scripts and images has increased by 53% over the last 15 years from past studies [START_REF] Hernández-Campos | Tracking the evolution of web traffic: 1995-2003[END_REF] and by 7% from recent studies [START_REF] Butkiewicz | Characterizing web page complexity and its impact[END_REF]. Furthermore when paying particular attention to the different types of images, when using a Google-Chrome web browser and visiting a Google website, on average 80% of images are now in WebP format. Compared to studies conducted in 2011 -2014, Flash usage has been reduced by 61% as Adobe will remove all support for Flash in 2020. When performing our measurements, we also noticed an increasing PWA (Progressive Web Apps) usage of 6% between July 2018 and January 2019. On a wider scope, based on the Alexa websites Category listings available, 3921 websites were assessed. The News websites download on average 19.81% more resources, Kids and Teens websites have a significant greater fraction of Flash objects and Shopping websites make greater usage of JavaScript. When assessing the overall distribution of resources aggregated by MIME type, the Fig. 3 represents two different Alexa-referenced categories where a large variety of objects are incorporated in the corresponding web pages. We selected these 2 categories, since they represent a major part of the popular websites. The Fig. 3a shows that Entertainment websites make greater usage of images (in WebP, PNG or JPG format), followed by JavaScripts and Videos (which can be further decomposed in different formats, e.g WebM, Mp4 or Mp2t). The Shopping websites (Fig. 3b) also make use of a high number of images in different formats which greatly change all along the day thanks to JavaScript.

Who is delivering the resources?

In this analysis, we aimed to identify to which extent, when an end-user navigates to a specific website from a given location, the resources needed are downloaded from many servers located at various places. For this, we analyzed if the contents are downloaded from the same authoritative DNS name server of the main web page. Domains delivering contents and having the same authoritative DNS name server as the main web page are entitled Same-Origin domains and conversely Non-Origin domains. From our global measurements, when a main web page domain is in North America or Europe, irrespective of the preferred Internet protocol, contents are served on average by 2 Same-Origin domains and 7 Non-Origin domains and when the main web page is in Asia or South America, contents are delivered on average by 3 Same-Origin domains and 13 Non-Origin domains. Those Non-Origin domains represent specific services involved in the web page composition (e.g., Google services, such as advertisements or analytics) but also CDN nodes providing contents on behalf of the origin servers.

The average number of domains is not huge, but we can have some websites with many different domains involved in the delivery. As an example of such a complex website, Fig. 4 points out the servers delivering contents when browsing the website lemonde.fr, where the main web page is located in Europe and belongs to the News category. For this website, 352 resources are downloaded from 29 different domains, where 15 domains located in North America deliver 50 resources and 14 domains in Europe deliver 302 resources. The main web page as well as the 13 Same-Origin domains are hosted in Europe by Fastly serving 61.36% of

Do websites secure connections ?

Compared to the previous studies [START_REF] Felt | Measuring HTTPS adoption on the web[END_REF][START_REF] Krombholz | i have no idea what i'm doing" -on the usability of deploying HTTPS[END_REF][START_REF] Michaelson | Rigorous statistical analysis of HTTPS reachability[END_REF] encryption has been largely adopted to preserve privacy. Indeed, web browsers nowadays favor it by adding by default https:// when a user requests a web page. Furthermore, since Google marked non-HTTPS websites as insecure in its Chrome browser in July 2018, HTTPS adoption has increased. However, from our Top 10,000 Alexa websites, only 36.02% of the websites deliver their contents in full HTTPS (100% HTTPS) and still 0.28% of the websites deliver their contents in full HTTP (100% HTTP, no resource composing the web page is delivered by an HTTPS server). In between, we have websites composed of resources received with HTTPS and HTTP. As per Fig. 5, on average, when the web page is located in North America, 12 resources are downloaded in HTTP and 70 resources in HTTPS and when the main web page is in Asia, 32 resources are downloaded in HTTP and 46 resources in HTTPS. While HTTP/2 and QUIC-enabled web servers mainly deliver contents in HTTPS through TLS 1.2 14 , some content servers still deliver resources in a nonsecure mode (HTTP). Those websites are mainly located in Asia.

Which Internet protocol do I receive?

Since the previous published papers analysing Web browsing, the new Internet Protocols, namely HTTP/2 and QUIC, have been promoted. We then wanted to evaluate the adoption of these protocols by web servers. We made measurements explicitly requesting these protocols and analyzed if the remote servers reply using them or if they fall back to another one. Fig. 6 and Fig. 7 show the results of those measurements, in terms of protocol distribution.

First and very logically, we note that when requesting When performing measurements requesting HTTP/2, we received all the resources in HTTP/2 (100% H2) only for 11.82% of the websites. The other 88.18% of the websites reply in an average protocol distribution of 52.04% HTTP/1.1, 46.75% HTTP/2 and 1.21% Server Push. We can then see that although standardized in 2015, HTTP/2 is not yet widely deployed and that it is not equivalent worldwide, more used for servers in NA or SA, whereas in Europe and Asia, HTTP/1.1 is still prevalent. When performing tests requesting QUIC, we see that QUIC is not deployed by many servers, but mainly Google ones. On average 97% of the different QUIC-enabled web servers are Google servers replying in QUIC v.43 when using Google-Chrome v.68 15 and the other 3% are non-Google web servers replying in QUIC v.37-38 when using Google-Chrome v.63 16 .

From our measurements, we analyze that 0% of the websites reply in full QUIC for all the resources (100% HQ) and that the responses are received at an average distribution of 6.12% QUIC, 50.57% HTTP/2, 42.1% HTTP/1.1 and 1.21% Server Push. QUIC responses are highest when the main web page is in North America (in particular from Google servers).

QUIC is natively used jointly with HTTP/2 for the first request, but offers a QUIC zero-RTT connection when connecting to an already known website. We then evaluate if the distribution is different with the QUIC Repeat mode. In this configuration, 7.21% of the websites fully reply in QUIC (100% HQ). The rest of websites, 92.79%, reply in a distribu- 15 android.com (90.6% HQ), google.com (52.6% HQ) or youtube.com (49.1% HQ) 16 facenama.com (81% HQ), sarkariexam.com (57.4% HQ) or digitalgrant.ru (55% HQ) To sum up this evaluation, HTTP/1.1 is still widely used by web servers, HTTP/2, although standardized in 2015, is deployed at a low pace, and QUIC is mainly used by Google web servers.

Factors impacting Web Browsing Quality

An indicator of end-users' perceived quality when performing web browsing is the time needed to load a web page (entirely or certain parts of it). This section points out the different factors impacting (decreasing or increasing) these loading times.

Impact of the requested protocol

We present in this section the impact of the Internet Protocol on the end-users' quality, measured either by the FP, TFVR or PLT. The TFVR provides the loading time of the visible portion of the web page and is tightly linked to the end-user's browser window size. Fig. 8 depicts the different visible portion of web sites (without scrolling the web page) upon different browser window sizes. For example, when the main web page is located in North America, with browser window size of 1920x1080, less than 50% of the measurements provide a visible portion less than 32.09%. The Page Load Time provides to an end-user the needed time to load the entire web page (visible and non visible parts).

The FP exposes the moment when a first pixel appears on an end-user's browser screen. The FP involves the download and rendering of the main web page html source code and on average involves the download of 2 objects from 1 domain for 99.98% of all measurements. The only difference among the measurements is in general the size of the html page itself, thus increasing the corresponding download time.

As shown in Fig. 9a, we can see that the FP value is very close for HTTP/1.1, HTTP/2 and QUIC. This can be explained since before FP, on average only 2 objects are downloaded in this time and all the benefits of the HTTP/2 protocol (multiplexing, header compression, server push func-tion) can not happen. Similarly, for QUIC, since the first request is sent using HTTP/2 and the number of objects very small, 99.76% of QUIC measurements receive HTTP/2 replies. But one can see that the FP is smaller for the QUIC Repeat mode, since in this case, resources are downloaded in 0-RTT from UDP-enabled web servers and in 1-RTT from TCP-enabled web servers. The First Paint value is a good indicator of the network state when performing web browsing since objects are mostly downloaded from the main web page domain itself thus decreasing the total DNS time.

Looking at Fig. 10 and Fig. 11 , we can see that the behavior for the TFVR and PLT is close to the one detected with the FP measurement, i.e.; there is no noticeable difference between HTTP/1.1, HTTP/2 and QUIC. Indeed, as depicted in Fig. 7, when requesting HTTP/2, the HTTP/1.1 Internet protocol is still largely used. Similarly, with our dataset, when requesting QUIC, about 50% of the measurements have a QUIC distribution less than 0.79%, and those websites fall back to HTTP/2. But for the QUIC Repeat mode, the TFVR is largely smaller, thanks to 0-RTT UDP and 1-RTT TCP. For example, when comparing QUIC Repeat versus HTTP/2, the TFVR loading time is reduced by 54.2% for a main web page in North America.

Impact of the number of domains

When performing web browsing, the resources are downloaded from various domains (as discussed in section 4.2, from Same-Origin and Non-Origin domains). The number of domains contacted increases proportionally to the DNS lookup times and thus the overall loading times (for the TFVR or PLT). When a web page is located in North America or Europe, on average, resources are downloaded from 9 domains (with an overall DNS time of 316.09 ms) and when the main web page is in South America, resources are downloaded on average from 16 domains (with an overall DNS time of 1715.85 ms). In general websites being served by large number of domains belong to the News and Shopping category. Fig. 12 illustrates the overall tendency regarding the number of domains through which resources are downloaded when requesting HTTP/2 and the corresponding loading time. This is represented as a heatmap where dark blue represents higher concentration of values and light blue lower concentration of values together with the corresponding Page Load Time. For instance, in Fig. 12a, there are plenty of websites having about 7-8 domains and offering a PLT of about 1500 ms, and very few websites having 30 domains for a PLT at about 3000 ms. When a web page is located in North America or Europe, the PLT loading time is good and time increases with the number of domains from which the resources are downloaded. When the main web page is in Asia, the PLT loading time increases proportionally to the number of domains (average of 3915 ms DNS time with the number of domains being 4) and the objects being mainly served in HTTP/1.1 increase blocking and waiting time.

Impact of ad blockers

Advertisement in web pages is nowadays naturally embedded and can decrease an end-user's QoE, regarding the overall loading time of a website. From our measurements, when using an ad blocker (Adblock Plus), on average, the number of downloaded resources is reduced by 11.02%. It is decreased by 9.72% when the main web page is in North America, by 8.43% in Europe, by 11.84% in South America and by 14.10% when the main web page is in Asia. Websites belonging to the Shopping, Kids and Teens and Games categories have the most significant decrease in the number of downloaded objects and the PLT for websites belonging to these 3 categories are on average decreased by 39.96%. Regarding other website categories, blocked objects are mainly characterized as social network contents (e.g Facebook, Twitter, LinkedIn, etc.).

Impact of the network access

End-users may be served by different network service providers, along with different bandwidths and data communication technologies (e.g ADSL, Wi-Fi, Fiber). Our measurements reflect observed web browsing quality where

ℎ ADSL < ℎ Wi-Fi < ℎ Fiber
The Fig. 13 depicts the observed PLT loading times when requesting HTTP/1.1 following different network access. For example, when the main web page is in North America, less than 80% of the measurements have a PLT less than 6901 ms for ADSL network access, PLT less than 6000 ms for Wi-Fi network access and PLT less than 4000 ms for Fiber network access, thus representing a reduction of the loading time of 42.03% when shifting from ADSL to Fiber network access. When requesting the QUIC protocol, as shown in Fig. 14, the end-user's QoE increases proportionally to bandwidth increase, independently of the main web page continent location. On average depending on the network access (ADSL vs Fiber), an end-user's PLT is decreased by 30.25%. Increased end-user QoE is tightly linked to the corresponding network access and requested protocol. When requesting HTTP/2 or QUIC, irrespective of the main web page location, web pages' loading times are decreased on average by 19.73% from ADSL to Wi-Fi, 16.02% from Wi-Fi to Fiber and 30.25% from ADSL to Fiber.

Impact of main web page location

Fig. 15 depicts the overall web pages' loading times, grouped by estimated continents location of the home page. The overall centered (median) time follows the rule < < . Being in France when performing those measurements, we noticed that the overall loading times for a European end-user are smaller for the websites located in North America and Europe than for those located in South America and Asia. The 25 ℎ percentile and 75 ℎ percentile are denoted by 1 and 3.

When the main web page is located in North America, less than 50% of the measurements have a PLT less than 2962 ms, 1 and 3 are respectively at 1753 ms and 4868 ms. The parameters inducing the outliers are due to high number of domains (mainly Non-origin), high amount of downloaded objects and objects served over HTTP/1.1. The extreme outliers are for those objects downloaded from South America and Asia. When the main web page is located in South America and Asia, the observed times are higher in general but the number of outliers reduced since more resources are downloaded from the same continent of the main web page domain and the upper limit closer to the defined timeout for every measurement.

Impact of websites' referenced categories

Websites are all different among them composed of a wide range and number of objects. Following their content, they are referenced as belonging to different types of categories (e.g Alexa 17 , QuantCast [START_REF] Simpson | Interest Category Definitions[END_REF] or Web Filter 18 ). In our study, we make use of Alexa referenced category listing where websites are classified upon 17 different categories. The Fig. 16 shows the overall observed PLT for different categories. We can observe that the CDF is similar for many categories except for 2 of them (Adult and Education). For instance, for most categories, for 50% of our measurements, the observed PLT is about 2500-3000 ms. Websites belonging to the Computers, Reference and Shopping category have loading times being close among them (compared to websites belonging to the Recreation, Business and News category) mainly due to objects MIME types composing the web page. There is the biggest gap between the Adult and Education category. While for less than 75% of our measurements, websites belonging to the Adult category have a PLT less than 3054 ms, websites belonging to the Education category have a PLT less than 6816 ms. While these two categories download on average the same types of objects (mainly images and videos), from our analysis in section 4.4 and 4.1, the main difference which results in a higher observed PLT is that objects for Adult categories are mainly downloaded We can then see that the category can play a role in the web browsing quality, but it is limited and less than the sizes of downloaded objects and the protocol through which they are downloaded which contribute most of the time to a higher PLT value.

Impact of visible portion on its corresponding loading time

Our measurements have been performed following different web browsers' window sizes and the Fig. 8 has shown that depending on the main web page continent location and corresponding web browser window size, an end-user might have a larger overview of the web page without scrolling. We focus in this section on the impact of this visible portion on the time to load it. The default logical assumption would be that the time to load % of a web page would be proportional. The Fig. 17 (Heatmap : Dark green represents higher concentration of values and light green lower concentration of values together with the corresponding TFVR) depicts the concentration of websites following their visible portion (without scrolling the web page) and corresponding time to be loaded (TFVR). As discussed in section 4.4, websites having their main web page in North America share the particularity of having higher visible portions (mainly Search-Engines) and the Fig. 17a show that the corresponding TFVR ranges from 400 ms to 700 ms when the visible portion is 100% and ranges from 1200 ms to 1875 ms when the visible portion is between 11% and 20%. The Fig. 17d depicts websites whose main web page is in Asia, where we can see that these web pages have a visible portion massively ranging from 13% to 17% with loading times between 2700 ms and 5000 ms (apart from a limited number of web pages having a visible portion of 100% and TFVR between 1815 ms and 2104 ms).

The TFVR is not proportional to the visible portion since different actors contribute to this loading time (type and number of objects in the visible portion, the network download time, etc.).

Impact of time of day

Our measurements have been performed round the clock all day long, since following different times of the day a web- site might be visited by a larger number of end-users, and thus increasing the time needed for the servers to deliver different objects. Furthermore, the overall network state to reach the remote servers might be overloaded (e.g peak hours versus off-peak hours). We thus assess in this section the impact of the time of day on the perceived page load time. The Fig. 18 depicts a set of websites' loading times at different times of the day. While some websites' loading time stay relatively the same all day long (e.g wikipedia.org), other websites depending on their category (thus content) may have their inner structure change several times per day (e.g News: chinatimes.com or Entertainment: twitch.tv). Our measurements have been performed in Europe, and we can notice that the website chinatimes.com average PLT increases drastically at 04H CET (12H in Asia) and 08 CET (16H in Asia). We can thus suppose following these times of the day in Asia that the website has a higher visit rate in Asia, and an European end-user experiences the side effects. In overall, we can notice that between 12H CET -14 CET or 20H CET -22H CET, the average loading times perceived by end-users also increase which is most of the time due to the network state.

Web pages loading times are impacted following different times of the day mainly due to their visit rate (web servers impacted) and network state (overloaded during peak hours).

Parameters FP TFVR PLT Main web page location ✓ ✓ ✓ Requested protocol ✓ ✓ Round Trip Time ✓ ✓ ✓ Website category ✓ ✓ ✓ Number of objects ✓ ✓ ✓ Main HTML page size ✓ ✓ ✓ Size of objects ✓ ✓ Types of objects ✓ ✓ Visible Portion ✓ Number of domains ✓ ✓ Use of ad blocker ✓ ✓ Time of day ✓ ✓ ✓ Network access ✓ ✓ ✓ Table 1
Parameters influencing web browsing quality

The corresponding loading times might also fluctuate depending upon the website category since the number of downloaded objects (and MIME type and size) might increase.

Which parameters can influence Web Browsing quality?

Based on our statistical measurements analysis, the Table 1 represents the different parameters to be taken into consideration when investigating the FP (First Paint), TFVR (Time for Full Visual Rendering) and PLT (Page Load Time) loading times. As per our geographic location (Europe), main web pages located in South America and Asia bring higher loading times (compared to main web pages in North America and Europe). The network access and requested protocol are tightly linked to an increased quality. The number, size and type of objects and the main HTML page size have a strong link with the website's Alexa-referenced category listing (e.g Search Engine category websites are composed of small number of objects of small size, News category websites are longer in scrollHeight, thus composed of many objects and served by a large number of domains, etc.). Finally, other factors like the time of day or the visible portion can have an impact on the quality but at a lower degree.

Machine Learning Techniques to predict web browsing quality

With our statistical analysis, we are able to know the factors impacting the web browsing quality, but we can not manually identify the thresholds or the values leading to a good or a bad quality. For this, we have applied different Machine Learning techniques on our dataset, namely Clustering (and BiClustering), Covariance or Density estimation, Principal Component Analysis (PCA) and Decision Trees. As identified in the previous sections, the factors influencing QoE are linked together and decision tree is revealed to be the most adequate non-parametric supervised learning method. In our study, decision tree rules are detected from 40% of the entire dataset that we denote by training dataset and the other 60% is used to validate the rules, denoted by validation dataset. Indeed, defining rules with only the Top 500 or Top 1,000 Alexa is not efficient due to the diversity of the websites structure in the Top 10,000. The decision tree enables us to predict the estimated end-user quality by applying decision rules which are computed from our dataset.

Decision Tree based on satisfaction degrees

Several studies regarding the MOS (Mean Opinion Score) during web browsing sessions [START_REF] Hobfeld | Speed index: Relating the industrial standard for user perceived web performance to web qoe[END_REF][START_REF] Bocchi | The web, the users, and the MOS: influence of HTTP/2 on user experience[END_REF][START_REF] Bocchi | Measuring the quality of experience of web users[END_REF][START_REF] Hora | Narrowing the gap between qos metrics and web qoe using above-thefold metrics[END_REF] provide visible web page loading times. Table 2 presents the different degrees of satisfaction based on end-user sociological perceived feelings [START_REF] Nielsen | Web response times[END_REF] coupled to these MOS studies. Instant response indicates a high responsiveness from the visited web page, Seamless response indicates that the end-user is happy with the overall experience, Average response indicates that the end-user feels the delay for the web page to load acceptable, Critical response indicates that the end-user strongly feels the bad side effects of the long web page loading and Bad response indicates that the end-user is not happy at all and can give up the browsing. Based on the time values mentioned for these satisfaction degrees and the values of the TFVR for our measurements, we hereafter expose the corresponding sets of rules obtained from Decision Trees applied to our training dataset for the 5 classes. The full obtained decision tree is represented by 184 different nodes.

Instant response. Our measurements do not have any TFVR loading time lower than 100 ms and make this class unnecessary.

Seamless response. 24.32% of the training dataset has a TFVR in this range and the main impacting factors for this classification are the main web page located in North America or Europe, a low RTT value for the main web page, QUIC Repeat as requested protocol and a Fiber network access.

Average response. 30.67% of our training values belongs to this class and the main factors are the main web page located in North America or Europe, the requested Internet protocol being HTTP2, QUIC or QUIC Repeat, a Wi-Fi network access, the use of an ad blocker and RTT value between 18.65 ms and 101.5 ms.

Critical response. 43.78% of the training measurements have a TFVR in this class, impacted by the main web page located in Europe, South America or Asia, the visible portion between 22.76% and 41.39% (these web pages have a larger scroll height), the objects served by various content servers mainly located in Asia and Wi-Fi or ADSL as network access.

Bad response. 1.23% of the training dataset is classified as Bad response, mainly because of the use of Wi-Fi network access and HTTP/1.1 as requested protocol.

Once this decision tree has been built based on the training dataset, we evaluated it with the validation dataset. The Table 3 depicts the obtained classification confusion matrix, where the diagonal represents the percentage of measurements correctly predicted. We can see that 84.79% of the predictions are good, but more than 15% of the validation dataset is wrongly predicted. Looking at the matrix, the val-

Decision Tree based on estimated satisfaction degrees from clustering

Having seen that the proposed classification of the satisfaction degrees from literature is not optimal for current Web Browsing experience, we decided to define a new one, based on our huge dataset of web browsing measurements on the top 10,000 Alexa web sites. We thus look for the best satisfaction degrees using clustering (K-Means) to identify the different classes. The KMeans algorithm divides a set of samples into disjoint clusters , where each cluster is described by the mean of the different samples in the cluster itself. In order to determine the value , we use the Elbow method where we assess the percentage of explained variance (ratio between-group variance to the total variance) as a function of the number of clusters. As seen in Fig. 19, the value 5 seems to be the good Elbow Criterion, and using it, we can identify the 5 clusters representing the different satisfaction degrees, illustrated in Table 4. The first and second satisfaction degrees from Table 2 have been merged into one degree and the Critical response satisfaction degree is distributed and split into two degrees. The estimated satisfaction degrees illustrated in Worse response. 12.09% of the training dataset has a TFVR in this range with the main factors being the main web page located in South America or Asia, the number of downloaded resources between 45 and 85 (mainly downloaded from Asia), RTT value greater than 185.5 ms and the number of domains from Asia greater than 4.

Poor response. 6.02% of the training measurements lead to a poor quality, mainly because of the main web page located in Asia, HTTP/1.1 used as requested protocol, a high number of downloaded objects and the use of an ADSL network access.

We evaluate the accuracy of our decision tree with the rest of our dataset (i.e., 60% of our measurements) and the Table 5 depicts the obtained classification confusion matrix when applying the obtained rules on this validation dataset. We can see, looking at the diagonal representing the percentage of measurements correctly predicted, that our rules can efficiently predict 94.17% of the dataset. Only 5.83% of the validation dataset is wrongly predicted, which is 2.61 times less when compared to the rules-based model obtained in section 6.1. We can then think that this model is better.

Accuracy of our rules-based model

In order to verify the correctness of the obtained decision tree from section 6.2, we have performed in February 2019 measurements on the Alexa websites ranging from rank 10,000 to 15,000. The dataset sums up to the measurement of 4861 never-assessed before distinct websites, which represent 2.7 Million different measurements. The Table 6 illustrates the obtained classification confusion matrix for these measurements when applying our rules-based model. Among our measurements, 6.51% of the measurements provide a Good response (low RTT and small number of objects), 21.09% for Fair response (low RTT and main web page in Europe), 35.02% for Moderate response (large number of domains serving contents from Europe and Asia), 17.04% for Worse response and finally 10.04% of the measurements yield a Poor response (high HTTP/1.1 reply distribution and objects mainly delivered from South America and Asia). In short, 90.4% of the overall new dataset was correctly predicted. This is a bit less than with the validation dataset, but it is related to websites which have never been accessed before, in contrast to the validation dataset, composed of websites being also in the training dataset. We can then conclude that our rules-based model can efficiently predict web browsing quality for any website for an end-user located in France. We have presented in this paper the results of the analysis based on our measurements, performed by the probes in France (Europe). Our model has been proved to be correct for this configuration but it has to be adapted to other situations, e.g., end-user in another location. Indeed, as seen in Fig. 1a and Fig. 2, when visiting different websites having their main web page located in specific continent-geographic locations, resources are downloaded from different continents (e.g for a European end-user visiting a web page in Asia, resources are downloaded from Europe, North America and Asia), which impacts the quality. As depicted in Table . 1, the impact of parameters to be re-assessed will be the Round-Trip-Time (network path taken through different Asynchronous Systems), number and types of objects (mainly due to advertising), the number of domains (specifically the location of web servers delivering content), time of the day and corresponding network access (the throughput offered by network operators from different regions are different).

Conclusion and Future work

In this paper we have shown the importance of taking into account diverse parameters to better quantify and qualify web browsing. We have used all the web metrics identified by the W3C or the research community and real onmarket web browsers together with user-representative residential network access. Our study has brought light on the wide range of factors which can improve or decrease an enduser's web browsing quality. The identified impacting factors are then used as inputs to a rule-based model obtained from Decision Tree, in order to efficiently predict the web browsing quality. Our measurements have been performed in France and an ongoing and future work is focused on performing measurements from different locations and using different network providers all around the world, to confirm the impacting factors to be taken into consideration, to refine the estimated satisfaction degrees for end-users' perceived web browsing quality and to validate our model. The obtained loading times contributing to the different estimated satisfaction degrees will also be assessed through a MOS study with real end-users.

Acknowledgement

This work is partially funded by the French ANR Bot-tleNet project, No ANR-15-CE25-0013-001.

  (a) Resources' location (b) MIME type of resources

Figure 1 :

 1 Figure 1: Downloaded resources

Figure 2 :Figure 3 :

 23 Figure 2: Aggregated distribution of resources

Figure 4 :

 4 Figure 4: Website lemonde.fr content servers

Figure 5 :

 5 Figure 5: Objects downloaded in HTTP or HTTPS

  (a) Requesting HTTP/2 (b) Requesting QUIC (c) Requesting QUIC Repeat

Figure 6 :

 6 Figure 6: Received protocol distribution upon request

Figure 7 :

 7 Figure 7: Average received protocol distribution

Figure 8 :

 8 Figure 8: Visible portion of website

Figure 9 :Figure 10 :

 910 Figure 9: First Paint loading time

Figure 11 :Figure 12 :Figure 13 :

 111213 Figure 11: PLT loading time

  (a) ADSL network access (b) Wi-Fi network access (c) Fiber network access

Figure 14 :Figure 15 :

 1415 Figure 14: PLT when requesting QUIC

Figure 16 :

 16 Figure 16: PLT for different websites' categories

Figure 17 :

 17 Figure 17: Impact of visible portion on TFVR

Figure 18 :

 18 Figure 18: Loading time of different websites at different times of the day

Table 2

 2 Satisfaction degrees Loading Times (ms) User satisfaction degrees, based on[START_REF] Nielsen | Web response times[END_REF]

	Instant response	< 100
	Seamless response	100 -1000
	Average response	1000 -3000
	Critical response	3000 -10000
	Bad response		> 10000
				Actual class
	Predicted class	Instant	Seamless	Average	Critical	Bad
	Instant	0	0.15%	0	0	0
	Seamless	0	22.21%	1.60%	0	0
	Average	0	1.96%	29.01%	8.07%	0
	Critical	0	0	0.04%	32.35%	0.01%
	Bad	0	0	0.02%	3.36%	1.22%

Table 3

 3 Classification confusion matrix of the validation dataset based on user satisfaction degrees ues identified in Table2and the real TFVR values, we can say that the Instant Response class is unnecessary (no measurement in this class) and that the prediction error rate for the Critical response class is important (about 10%) because the loading time range is too wide.

Table 4

 4

	are more uniformly
	distributed, compared to

Table 2 .

 2 We hereafter describe the impacting factors leading to the classification of one measurement into one class, learned from our training dataset by the decision tree model, represented by 176 different nodes.Good response. 23.32% of the training dataset has a TFVR in this range and the main factors are the main web

	Figure 19: Explained variance
	Estimated satisfaction degrees	Loading Times (ms)
	Good response	< 1232
	Fair response	1232 -3486
	Moderate response	3486 -6715
	Worse response	6715 -9281
	Poor response	> 9281

Table 4

 4 Estimated satisfaction degrees derived from our measurements page located in North America or Europe, a low RTT and number of domains serving contents from North America or Europe, with replies delivered over the QUIC protocol.Fair response. 27.31% of the training measurements are classified in this class and the main impacting factors are namely the main web page located in North America, a RTT value less than 105.5 ms, a Wi-Fi or Fiber network access, resources downloaded from North America, Europe or Asia in HTTPS, together with a low number of domains serving contents.Moderate response. 31.26% of our training dataset leads to Moderate response, mainly based on the main web page located in Europe or Asia, the requested Internet protocol being HTTP/1.1, HTTP/2 or QUIC, the visible portion of the websites less than 13.25% and objects delivered from Asia or South America.

Table 5

 5 Classification confusion matrix of the validation dataset based on estimated satisfaction degrees

				Actual class
	Predicted class	Good	Fair	Moderate	Worse	Poor
	Good	6.51%	0.16%	0	0	0
	Fair	0.76%	21.09%	0.72%	0	0.02
	Moderate	0.01%	1.96%	35.02%	0.12%	0.01%
	Worse	0	0	3.60%	17.74%	1.03%
	Poor	0	0	0	1.21%	10.04%

Table 6

 6 Classification confusion matrix to validate the accuracy of our rules-based model, based on estimated satisfaction degrees

http://phantomjs.org/

https://www.seleniumhq.org/

https://github.com/ghostwords/chameleon

https://commoncrawl.org/

https://webview.orange.com

https://webview.orange.com/monitoringParameters

HTTP Archive

http://www.maxmind.com

https://stat.ripe.net/

https://webview.orange.com/d/UyIIcrUmz

Now TLS 1.3 is deployed but at the time of the measurements, it was still TLS 1.2

https://www.alexa.com/topsites/category

https://fortiguard.com/webfilter/categories


Jean-Pierre Belaud
email: jeanpierre.belaud@ensiacet.fr

Nancy Prioux
email: nancy.prioux@ensiacet.fr

Claire Vialle
email: claire.vialle@ensiacet.fr

Caroline Sablayrolles
email: caroline.sablayrolles@ensiacet.fr

Big data for agri-food 4.0: Application to sustainability management for by-products supply chain

Keywords:

The bioconversion of lignocellulosic biomass is a promising method for the production of bio-energy, biomolecules and biomaterials. Pretreatment of the lignocellulosic biomass is an essential step in this process. The choice of pretreatment process is a difficult one, and there are currently no clear criteria on which to base this choice. This project, with its sustainability and agri-food perspective, used environmental impacts to assess the various processes and their panels of technologies. The approach developed integrates big data, to improve sustainability management in supply chain design, with the aim of valorising agricultural waste. In five main steps, this approach combines concepts from industry 4.0, sustainability and the agri-food industry. We apply this approach to a case study in the domain of agricultural waste valorisation: the pretreatment of lignocellulosic biomass in the rice supply chain.

Introduction and background

Since the beginning of the 1970s, human influence on the Earth and its resourcesthrough the economic, scientific and technological development of our industrial societyhas steadily increased, resulting in an ever-greater impact on the environment. Awareness of these ecological problems has sparked new ideas for more eco-friendly development. The Brundtland report (1987) marked the start of "sustainable development", with the first definition of this term as "development that meets the needs of the present without compromising the ability of future generations to meet their own needs." Sustainable development thus has three main objectives: economic efficiency, social fairness and environmental sustainability. It requires profound changes in the way we think, design and use resources, and in economic and social structures for consumption and production patterns. Life-cycle thinking can help to improve environmental performances, and social and economic benefits can be derived from approaches taking the full life-cycle of the agricultural supply chain into account. Indeed, this approach can be used to minimise the impact in some areas, whilst preventing further impacts in other areas. Sustainable development requires the stable simultaneous consideration of economic, environmental and social aspects [START_REF] Hardaker | Guidelines for the Integration of Sustainable Agriculture and Rural Development. 2. The Concept of SARD[END_REF]. A new business model for more sustainable development has recently emerged: the circular economy (CE) [START_REF] Mathews | Progress towards a circular economy: the drivers and inhibitors of eco-industrial initiative[END_REF]. This model helps to reconcile economic, environmental and social aspects. [START_REF] Ghisellini | A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems[END_REF] have reviewed scientific articles on CE and have discussed the origins of CE and the principles and limitations of CE models. In France, the circular economy was included in the nine areas of the SNTEDD (National Ecological Transition Strategy for Sustainable Development) proposed by the French government in 2015 [START_REF] Belaud | A circular economy and industrial ecology toolbox for developing an eco-industrial park: perspectives from French policy[END_REF]. The term "ecological transition" is used to describe the shift towards more sustainable development. This transition is a major societal concern and will require broad knowledge and skills [START_REF] Reichman | Challenges and opportunities of open data in ecology[END_REF] from many different sciences [START_REF] Palmer | Ecological science and sustainability for the 21st century[END_REF].

The digital transformation of current societies has led to a new era in industry, which can be described as "industry 4.0", which may facilitate the ecological transition. Industry has changed considerably since its beginnings in the 18th century. Four industrial revolutions are now considered to have occurred.

Industrial revolution 1.0 corresponds to the introduction of machinery powered by the local generation of steam, which uncoupled production from the limitations of human manual effort. Industry 2.0 corresponds to the period after 1870 in which "scientific management" or Taylorism introduced the division of labour, assembly lines and the use of electric energy machines. The 20th century, with the development of electronics, computing and robotic manufacturing, ushered in the era of industry 3.0, which focused on quality and cost performances. Automation has provided opportunities to optimise manufacturing processes and improve productivity through the design of more flexible, ergonomic and safer machinery. Industry 4.0, based on the technological concepts of cyber physical systems (CPS) [START_REF] Babiceanu | Big data and virtualization for manufacturing cyberphysical systems: a survey of the current status and future outlook[END_REF], internet of thing (IoT), big data and an internet of services [START_REF] Kagermann | Recommendations for Implementing the Strategic Initiative Industry 4.0 -Final Report of the Industry 4.0 Working Group[END_REF], is an umbrella term for the technologies and concepts of value chain organisation, which facilitates the development of "smart factories". One of the core principles of industry 4.0 is data management, from collection to analysis, and the integration of information technologies, manufacturing and operation systems as a way to acquire data in a more timely, rapid and flexible manner [START_REF] Brettel | How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4.0 perspective[END_REF]. This transformation from industry 3.0 to industry 4.0 is often referred to as the "digitalisation process" or "digital transition". Big data and related methods and tools form one of the pillars supporting this transformation [START_REF] Chen | Data-intensive applications, challenges, techniques and technologies: a survey on big data[END_REF].

Big data are information assets with a high volume, velocity and variety [START_REF] De Mauro | A formal definition of big data based on its essential features[END_REF], making them difficult to manage with common tools [START_REF] Hampton | Big data and the future of ecology[END_REF]. Big data technologies can be applied to the CE and industrial ecology. [START_REF] Ming | Big data and industrial ecology[END_REF] explored the possible contribution of big data to industrial ecology through several examples combining these two domains. Knowledge engineering (KE) is a technique from knowledge-based systems that can now be applied to big data. Life-cycle thinking would clearly benefit from the combination of the huge amounts of data now available with KE techniques for their exploitation. This would make it possible to obtain additional and surrogate data in situations in which specific data cannot be collected, rather than having to rely on default and missing values. This approach requires a set of hypotheses. The main goal of KE is to structure knowledge into formal representations for exploitation by computers. This structuring of data is particularly important when handling large amounts of data, which push standard statistical software to its limits [START_REF] Snijders | Big data": big gaps of knowledge[END_REF]. KE methods used a standardised vocabulary to structure the experimental data and their meaning. This structuring may be based on an ontology representing the experimental data of interest [START_REF] Noy | Sematic integration: a survey of ontology-based approaches[END_REF]. Ontologies are knowledge representation models that can be used to link data and to provide automated tools for reasoning [START_REF] Doan | Principles of Data Integration[END_REF]. Once the data have been structured into ontologies, they can be homogenised and used to define and calculate criteria for the assessment of processes [START_REF] Liao | Semantic annotation for knowledge explication in a product lifecycle management context: a survey[END_REF]. However, only a few studies have explored the application of this approach in this domain. [START_REF] Belaud | A circular economy and industrial ecology toolbox for developing an eco-industrial park: perspectives from French policy[END_REF] designs intensive data and information systems to manage sustainable development in the frame of eco-industrial area. [START_REF] Cooper | Big data in life cycle assessment[END_REF] used big data to complete the background data. The background system consists of all other processes interacting directly with the foreground system. "Dataintensive life-cycle assessment" [START_REF] Bhinge | Data-intensive life cycle assessment (DILCA) for deteriorating products[END_REF] uses KE-based approaches to adapt life-cycle assessment (LCA) methods, incorporating technological developments that may modify LCA results for a given product over time. Finally, big data and KE can be used to represent the life cycle of a product or service [START_REF] Zhang | LCA-oriented semantic representation for the product life cycle[END_REF], with all the intermediate flows, emissions and extractions, in ontology-based LCA.

Europe generates more than 700 million tonnes of agricultural waste annually [START_REF] Pavwelczyk | EU Policy and Legislation on recycling of organic wastes to agriculture[END_REF]. The projected increase in the world's population will undoubtedly be accompanied by increases in waste production and its impact on the environment. In addition, human activities are decreasing the availability of agricultural land, with inevitable impacts on agricultural systems. New agricultural technologies should facilitate sustainable intensification, the "best" approach for the future of agriculture [START_REF] Garnett | Sustainable intensification in agriculture: premises and policies[END_REF]. However, this intensification will lead to more waste of products and resources [START_REF] West | Leverage points for improving global food security and the environment[END_REF]. According to [START_REF] Horton | An integrated theoretical framework to enhance resource efficiency, sustainability and human health in agri-food systems[END_REF], the parametrisation of waste in agriculture is a major challenge in attempts to achieve sustainability. We can identify two classes of waste: waste from inputs, such as water or fertiliser, and waste due to the incomplete conversion or processing of materials in the supply chain, from crop production to food consumption. This second class of agricultural waste includes lignocellulosic by-products, corresponding to the essential structural compounds of the cell walls of lignified plants. Lignocellulosic biomass is one of the most abundant and cheapest renewable resources on Earth. Its bioconversion is a promising approach to the production of bioenergy, biomolecules and biomaterials. This process involves enzymatic hydrolysis of the biomass to release glucose. The lignocellulosic biomass has four main components: cellulose, hemicellulose, lignin, and phenolic acids. Only two of these components, cellulose and hemicellulose, can be hydrolysed to generate glucose. For overall sustainability, the processes used to generate these bioresources must be sustainable. Assessments of sustainability are increasing being incorporated into processes in the agro-food domain (Food SCP Round Table European Commission, 2012;[START_REF] Raymond | Improving food systems for sustainable diets in a green economy[END_REF]. Wolferd et al. explored smart farms through farm management, farm processes, network management organisation and network management technology [START_REF] Wolfert | Big data in smart farming -a review[END_REF]. A few studies since 2010 have focused on climate change and big data in the domain of agriculture [START_REF] Pivoto | Scientific development of smart farming technologies and their application in Brazil[END_REF]). An integrated theoretical framework developed by [START_REF] Horton | An integrated theoretical framework to enhance resource efficiency, sustainability and human health in agri-food systems[END_REF] considered the agri-food supply chainfrom land to peopleby integrating big data and data for all actors with any influence on agri-food businesses. A generic agri-food ecosystem template was created, including the key actors, external influences, components and impacts.

We focused on the valorisation of agricultural waste and byproducts, considering the ways in which big data could support the management of sustainability. The pretreatment of lignocellulosic biomass before its enzymatic hydrolysis is essential, to ensure good yields. Various pretreatment methods have been studied in detail over the last 30 years. However, it remains difficult to choose between these different processes in terms of the available biomass and product quality, and criteria are lacking to guide this choice. We studied the relationships between product quality and biomass pretreatment. Focusing on sustainability, we compared the various pretreatment processes and the technologies involved in terms of their economic and environmental impacts. Environmental impact can be assessed in various ways. We used the life-cycle assessment (LCA) method, because this approach is truly systemic and based on life-cycle thinking. LCA evaluates environmental aspects and potential environmental impacts throughout the life cycle of a product or process [START_REF] Iso | Environment Management -Life Cycle Assessment -Principles and Framework[END_REF]. We initially extracted relevant information from heterogeneous sources for the analysis of pretreatments for lignocellulosic biomass valorisation. Our approach combines concepts from industry 4.0, sustainability and the agrifood industry. In particular, it incorporates big data, to improve sustainability management in supply chain design for the valorisation of agriculture wastes and outputs of agri-food applications. In the next section, we provide an overview of our research approach and the associated detailed workflow. In section 3, we apply this approach to a case study: sustainability management for four agrifood processes, guiding decisions relating to the supply chain and technologies in rice production. We then discuss the conclusions of this study and future perspectives.

Big data for the agriculture by-product supply chain

Materials and methods

Big data can be used at various levels of sustainability management. One of the challenges in by-product valorisation in the agricultural supply chain is designing the "best" process. The by-product valorisation supply chain includes several operational stages, from biomass choice to waste disposal, and it passes through various transformation stages and upstream/downstream processes. Each stage can be defined with impact methods and indicators relating to three areas: economic, environmental and social. Once the various stages have been described, the researcher can choose the biomass and the most appropriate process with the decision support tool, which takes into account all the indicators in each area. The main goal of this approach is to analyse the different valorisation systems and provide support for group-based decisionmaking. The link between the decision support system and the various data makes it possible to treat various types of data whilst maintaining a high-throughput for big data processing. The various data used are listed in Fig. 1. Public web data are available to anyone with a web browser and include weather data, world prices for raw materials and impact factors for LCA methods (ReCiPe for example). Corporate data are data obtained from companies at any stage of their activity (from the setting up of the company until its closure). In the case of agricultural by-product valorisation, the "company" is a biorefinery, and each biorefinery has its own transformation process data. Field data are data describing biomass quality (cellulose content) and mass, and moisture content, for example. Technological data relate to the technologies used in valorisation processes, such as cutting and milling technologies, inputs, rotation speeds and energy values, for example. LCA databases are databases widely used in LCA, such as EcoInvent and Gabi. Scientific databases are databases of scientific articles. In our example (Section 3), the articles were obtained from Web of Science and Science Direct.

We have three main axes of interest (Fig. 1). The workflow to support the link between big data and sustainability assessments for valorisation of the agricultural by-product supply chain is detailed in Section 2.2. This approach was adapted to the agri-food industry by making use of concepts from industry 4.0 and sustainability management. In particular, we retained the Big Data pillar from Industry 4.0 and sustainability assessment from sustainability management. Fig. 1 illustrates one path of digital transformation based on the integration of big data into the agrifood industry. The valorisation of the agricultural by-products supply chain can be split into five elements: lignocellulosic biomass, transformation processes and technologies, inputs and outputs, products and wastes, and upstream and downstream processes. Each of these categories can be described with heterogeneous data and can influence another category. For example, the type of biomass can influence the type of transformation processes. All data are of importance and influence the social, economic and environmental indicators. In the sustainability of agricultural by-product valorisation, the goal is to integrate all the data into process design, but this is very difficult. For example, for environmental assessments complying with life-cycle thinking, process data are required. Obtaining such data is time-consuming and requires expensive experiments. Alternatively, data can be obtained from scientific publications and other sources, with the automatic or manual use of big data. It is, indeed, possible to make use of these data and, therefore, to obtain foreground data for sustainability analysis, whereas background data are generally available from the LCA database.

The integration of sustainability assessment into the Agri-Food Industry will facilitate the ecological transition. Our approach is divided into five major steps (Fig. 2): goal, data architecture, The collection and extraction of data from structured databases require specific tools, such as data queries (SQL requests) or online analytical processing (OLAP). This substep is more complicated for unstructured data from the web. We extract the various webpages containing the data of interest. The metadata associated with these webpages can be used for their classification and to provide access to their content. For example, the definition of the MARC format in the early 1960's normalised documentary resource metadata. Thanks to Linked Web development including RDF (Resource Description Framework, a standard from W3C) in particular, it is possible to use SPARQL to request the RDF. The extraction process generates a structured table and differs according to the format of the webpages: API, HTML or pdf. Extraction may be automatic, semi-automatic or manual. Data can be extracted automatically from webpages with an API format. However, the extraction of the relevant data from scientific articles requires a reading guide, which is generated from ontologies and pretreatment expert analysis. This type of extraction is, thus, semiautomatic. In the data enrichment and storage substep, the extracted data are stored in relational database management systems (DBMS). Enrichment is the process of adding data to the DBMS. These data come from experts or models. The models are empiric numerical simulations of unit operation type, thermodynamics and energy for the control of flows, transformations and transfers.

The data curation substep involves the cleaning up, addition and deletion of data for the management of volume and value. Following this curation, a second, more accurate and accessible database can be generated. Curation saves time in subsequent substeps and prevents incorrect interpretation during the data analytics substep and the sustainability analysis step. The data analytics substep depends on the goal, the data domains and the decision-makers. For types of analysis are possible: descriptive analysis (what happened?), diagnostic analysis (why did it happen?), predictive analysis (what will happen?) and prescriptive analysis (how can we make it happen?). Descriptive and diagnostic analyses make use of a number of different methods and algorithms, such as summarisation, standard deviation, linear or non-linear dependences, factor analysis and classification methods (decision tree induction, Bayesian networks, k-nearest neighbour classifier). Some of these methods are visual, and data visualisation may therefore be included in the data analytics substep. Data visualization may also be achieved by plotting the raw data as a simple or interactive graph.

Detailed workflow

Fig. 3 shows the different steps supporting the approach described in Fig. 2. In the first step, goal, system boundaries must be clearly defined, and life-cycle thinking (LCT) is recommendedfor this purpose. LCT encourages a "from cradle to grave" or "from cradle to cradle" approach. In the CE model, the part of the life cycle in which the product is used is a key element for progress towards ecological transition. "From cradle to gate" approaches are oftenpreferred because the integration of downstream elements into sustainability analyses can be tedious and difficult. In particular, scientists and engineers often find it impossible to take the behaviour of end-users or consumers into account in their models. System boundaries have a strong effect on the assessments subsequently performed. For example, it must be specified whether the upstream biomass supply chain is taken into account. Once the goal and scope have been defined, the supply chain, technologies and transformation processes must be described. This description must be as complete as possible in terms of the process operations, the study location, the various inputs and outputs, and the type of energy used, for example. These details ensure the pertinence of the data collected. The last substep is choosing whether to study economic, social or environmental items. Studies may deal with one, two or all three of these areas. For complete sustainability management, all three areas should be included, but social elements are often removed from the analysis due to methodological limitations and time and cost concerns.

In the second step, Data Architecture, the five numbered substeps on Fig. 2 are similar to common steps in big dataanalysis. The data to be used must first be chosen. This requires several questions to be addressed: Where do I find the data? What kind of data? What are the uncertainties on the data? What data do I already have? What degree of data automation do I need? The list of different data in Fig. 1 is not exhaustive, and other types of data can be added, depending on the goal of the project. In this step, KE methods can be useful for collecting data. The data can be extracted in various ways (e.g. CSV, SQL, HTML, XML). Moreover, the integration of big data into agricultural supply chain valorisation does not always involve automatic processing. For semi-structured bases, such as scientific databases, experts from the domain concerned must select and verify the data exported. For structured bases, such as LCA databases, the data are automatically exported to the decision support system. The data enrichment and storage substeps differ between studies of different complexities or with the degree of automisation. Data curation involves the cleaning up of the data, the addition of expert data and the deletion of abnormal data. Finally, data analytics and the data visualisation depend on the goal, the data and the decision-makers.

The third step, sustainability analysis, involves choosing the impact methods, the indicators and the dispersion methods in accordance with each area of sustainability management. This choice is based on the two previous steps. The most important question is whether the methods used are appropriate for the limits and the data chosen. The choice of method depends on the location at which the study takes place, the type of data and the limits of the study. It is important to check that all the hypotheses of each method are satisfied before applying the method. Each method belongs to a particular category and has its own limitations. The method may be based on a single criterion (carbon assessment) or multi-criteria (LCA), qualitative or quantitative, "product"-oriented or "organisation"-oriented.

Sustainability visualisation is the fourth step. Different types of visualisation may be used, depending on the group-baseddecision-making process, the goal of the study or the choice of analysis method, but this visualisation should never be ignored [START_REF] Belaud | Collaborative simulation and scientific big data analysis: illustration for sustainability in natural hazards management and chemical process engineering[END_REF]. The last step is Decision. This step guides group-based decision-making for selection of the biomass, the agricultural supply chain (transformation operations and upstream/downstream processes) and technologies. Decisions may be taken manually or with the assistance of decision support tool, such as ELECTRE, or PROMETHEE. Mixed group-based decisionmaking methods, such as Delphi-SWOT [START_REF] Tavana | A hybrid Delphi-SWOT paradigm for oil and gas pipeline strategic planning in Caspian Sea basin[END_REF], will be implemented in the future development of this approach. The approach is illustrated with a case study in the next section: an analysis of four agricultural supply chains in the environmental area relating to rice straw valorisation processes in France.

Case study: valorisation of the rice straw supply chain

Pretreatment of lignocellulosic biomass

The lignocellulosic biomass has four main components: cellulose, hemicellulose, lignin, and phenolic acids. Cellulose and hemicellulose can be hydrolysed to generate glucose. Lignin and phenolic acids are responsible for the recalcitrance of cellulosic materials, the crystallinity of cellulose and the particular surface and porosity characteristics of matrix polymers. Biomass pretreatment is therefore essential, to decrease crystallinity, to increase the specific surface area and porosity and to extract the major constituents. Various pretreatment methods have been studied in detail over the last 30 years. Each of these pretreatment methods, whether mechanical, physical, chemical, physicochemical, biological or a mixture of various types, has advantages and disadvantages. Various factors have been used to compare the performance, efficiency or environmental impact of these pretreatment processes: environmental factors, energy consumption and energy efficiency, for example [START_REF] Barakat | Eco-friendly dry chemomechanical pre-treatment of lignocellulosic biomass: impact on energy and yield of the enzymatic hydrolysis[END_REF][START_REF] Chuetor | Innovative combined dry fractionation technologies for rice straw valorization to biofuels[END_REF][START_REF] Zhu | Woody biomass pre-treatment for cellulosic ethanol production: tehcnology and energy consumption evaluation[END_REF]. Biomass pretreatment process studies used a cradle-to-gate approach [START_REF] Jacquemin | Life cycle assessment (LCA) applied to the process industry: a review[END_REF], extending from the pre-milling of the biomass to its enzymatic hydrolysis (Fig. 4).

The goal of this case study was to use the approach described above and its associated workflow to help researchers to choose a sustainable process for the valorisation of rice straw pretreatment. The preliminary results obtained are presented here. The LCA method was applied to the environmental domain for the sustainability analysis. Future studies will focus on the economic area, and will make use of the life cycle costing (LCC) method. This study was a cradle-to-gate. The system boundaries were set at the pre-milling and enzymatic hydrolysis steps. The transport of the biomass from the field to the firm was taken into account. The core hypotheses were: (i) a pre-pilot-scale process is studied (ii) rice straw is considered to be free agricultural waste with no environmental impactall impacts of the rice crop are attributed to the part used for food (iii) the energy is French mixed electricity (v) the site of the study is France, and the field and the firm are a known distance apart. The functional unit chosen was "the production of 1 g of glucose". Glucose yield was required for the expression of flows per functional unit. Four different processes for treating rice straw were studied. These processes consisted of combinations of the four transformation operations shown in Fig. 4: (a) RSP1 (rice straw process 1), with one operation: pre-milling. (b) RSP2, with two milling operations: pre-milling + ultrafine milling. Like the previous process, this is a mechanical process. (c) RSP3, with three operations: pre-milling + physicochemical treatment + pressing and separation. (d) RSP4, with the four operations in the sequence shown in Fig. 4: pre-milling + physicochemical treatment + pressing and separation + ultrafine milling.

The last step of the transformation process is enzymatic hydrolysis treatment, which was the same for all four systems studied. The only data from this hydrolysis used were glucose yield and the amount of buffer, which is dependent on biomass quality (more buffer required for lower biomass quality).

Results

The general workflow (Fig. 3) is illustrated for the case study in Appendix A. The first step of the workflow (goal) is specified in Section 3.1.

In the data architecture step, data from various big data sources were collected and extracted to complete the assessment and to create decision support for the researcher: public web, field data, corporate data, LCA databases and scientific databases (Appendix A). The scientific databases included articles published on the four rice straw processes. Fifteen articles were selected by biomass industrial engineering experts. The LCA database usedwas Ecoinvent, which contains background data and information about the uncertainties on these data. The field database contained all the flow data, for both input and output, for the operation of the process, together with information about the technologies used for individual operations. Data were extracted from these databases in the form of CSV files. These CSV files were then used for the second substep the enrichment and storage of all data, corresponding to add data. The milling unit operation was identified for each article, but the energy data were missing, hampering the sustainability analysis (third step). Internal empirical models created by rice straw pretreatment experts were available and were used to enrich the data.

The third substep, data curation, was performed manually by sustainability engineering experts. Two curations were performed with the 15 rice straw articles. The material balance was not checked in two of the 15 articles selected due to lack of information. All the data from these two articles were, therefore, deleted. The availability of energy data for unit operations was also very patchy. We therefore decided to remove the energy data presented in some of the articles, to make it possible to upgrade all articles and avoid bias. This upgrade allowed to compare the different industrial path for the next steps (sustainability analysis, sustainability visualisation and decision).

After this curation, the fourth substep was data analytics. The 13 articles described 39 experiments. We decided to create one experiment from each article to facilitate subsequent calculations, analysis, visualisation and decision making. This experiment was an "average experiment" from each article, by calculating mean values for all the data in each article. These mean values were then expressed per functional unit, to ensure data consistency (Fig. 5 gives an example). These 13 "average experiments" were grouped together into four types of process: RSP1, RSP2, RSP3 and RSP4.The data visualisation substep generated the inventory tables used for the next step. At the end of the data architecture step life-cycle inventories for each process (RSP1, RSP2, RSP3 and RSP4) were generated.

The third step, sustainability analysis, uses the ReCiPe 2016 method to assess environmental impact and the Monte-Carlo method to calculate dispersion. The ReCiPe impact factor database (RIVM, 2018) and the Monte-Carlo method are available from the public web. The LCA database used was Ecoinvent (EcoInvent Life Cycle Inventory Database, 2017). The pedigree matrix was obtained from scientific articles and was completed for background data in Ecoinvent. We chose to use the LCA method and associated ISO standards. The data for the foreground system, resulting from step 2, and the background data, extracted from Ecoinvent, were mixed to calculate the environmental effects of each input and output. The ReCiPe method was then used to calculate 18 midpoint indicators and three endpoint indicators.

The fourth step, sustainability visualisation, provided a visualisation of these environmental indicators (Figs. 678). The different types of figure show the impact assessment at the midpoint or endpoint level. A standard representation of midpoint indicators is available in Figs. 7 and 8 presents a common representation of endpoint indicators. These interactive visualisations supported decision-making for the last step, decision, concerning the "best" pretreatment process for rice straw valorisation. The endpoint method uses three indicators: human health, ecosystems and resources. Results are expressed per functional unit, making it possible to compare different processes. RSP1 and RSP3 had the greatest impact on the three indicators considered. This impact can be explained by the pre-milling operation for RSP1. The production of 1 g of glucose with this pretreatment requires both a large amount of biomass and lots of buffer for the enzymatic hydrolysis. The use of such a large amount of buffer has a very high impact. In RSP3, the pressing and separation operation had the highest impact. Thus, two very different processes were found to have high endpoint indicators. With this visualisation, it was not easy to identify the process with the least environment impact, because the differences between RSP2 and RSP4 were minor.

The visualisation of midpoint indicators can be more relevant for decision-making (Figs. 7 and8). RSP2 appeared to have a lower impact than RSP4 for all but three indicators: marine eutrophication, terrestrial ecotoxicity and agricultural land occupation. At this point, the decision-making group, could either make a decision directly or refer to additional studies comparing RSP2 and RSP4. However, biorefinery chains are innovative and few data relating to biorefineries have been published to date. Further studies are therefore required.

The team of data scientists and researchers in industrial and sustainability engineering applied the five steps to test the approach. Results were obtained with a newly created research tool coupling a well-known LCA application (Simapro @ ) and a Microsoft Excel-VBA application. This tool supports all the steps of the proposed approach except the decision step, with decisions taken by a decision group. This approach could be improved for the case study presented. Each new article about rice straw pretreatment will provide additional process data, thereby improving the sustainability analysis. Thermodynamic models are then required to complete the data from scientific publications, particularly for the calculation of energy consumption for the process operations. The case study could also be broadened by the inclusion of the economic area in future developments of this research tool.

Conclusion and outlook

We have designed an approach integrating industry 4.0 into supply chain design to improve sustainability management for the valorisation of agricultural waste. This five-step approach combines methods and tools from big data and sustainability assessment. The goal, limits and hypotheses of the study are first specified. This makes it possible to determine which data are required for the second step: data architecture. Based on big data architecture, five substeps of the data architecture step have been defined: data extraction, storage, curation, analysis and visualisation. These five substeps provide all the data required for the next step: sustainability analysis. The sustainability visualisation step can then generate results through various dynamic and in-depth visualisation techniques. Finally, the decision step involves either a group-based decision-making process or a semi-automatic decision method. Three areas of sustainabilityeconomic, environmental and socialcan be assessed. In the case study presented, we applied this approach to the assessment of four pretreatment processes in the agro-food industry. This approach ruled out two processes as having too great an environmental impact. Additional studies would be required to enable the decision group to identify the "best" of the remaining processes. Various possibilities for improving this approach and case study could be explored: (i) addition of specific data sources, methods and visualisations for the economic and social areas, to improve sustainability data inventories and assessment methods; (ii) progress towards the automation of data extraction for step 2. This would make it possible to save time and to add new sources of data more easily; (iii) from our feedback with the Excel-VBA research tool, development of a complete ergonomic computing framework supporting the approach. This would encourage stakeholders to adopt this approach and would facilitate decision-making through the implementation of collaborative decision-making techniques, such as Delphi-SWOT; (iv) the design of models for calculating energies; (v) the generalisation of this principle and the development of a library of business and domain-specific models from agro-food process engineering. These models could be used to check and validate the data in the data architecture step. Controls could include, for example, an advanced material balance or energy analysis; (vi) the development of data dispersion propagation and automatic qualitative explanation systems for stakeholders. These advances will help to refine the method, to render it more general and more accurate.

Declaration of Competing Interest

None.

Fig. 1 .

 1 Fig. 1. Big data and sustainability assessment for agricultural by-product valorization.

  sustainability analysis, sustainability visualisation and decision. Each step has its own substeps and passage to and fro between the various steps is recommended. The second step of the proposed methodology concerns the construction of the big data architecture: data collection and extraction, data enrichment and storage, data curation, data analytics and data visualisation[START_REF] Levin | NIST Big Data Public Working Group: Reference Architecture Subgroup[END_REF].There are five substeps in the construction of the data architecture: data collection and extraction, data enrichment and storage, data curation, data analytics and data visualization. Chen et al. (2012) compared these substeps to Business Intelligence and Analytics. The data come from various heterogeneous sources and can be structured or unstructured. The tools commonly used for ETL (extraction, transformation and loading) in data management and warehousing are unsatisfactory in this context. Specific methods and tools are therefore required.

Fig. 2 .

 2 Fig. 2. The various steps in the approach.

Fig. 3 .

 3 Fig. 3. Workflow of approach.

Fig. 4 .

 4 Fig. 4. Presentation of the by-products valorisation processes.

Fig. 5 .

 5 Fig. 5. Example of data visualisation from an "average experiment".

Fig. 8 .

 8 Fig. 8. Visualisation at midpoint method (2/2).

Fig. 7 .

 7 Fig. 7. Visualisation at midpoint method (1/2).

Fig. 6 .

 6 Fig. 6. Visualisation at endpoint level.