Melanie Herschel
email: melanie.herschel@lri.fr

Ioana Manolescu
email: ioana.manolescu@inria.fr

DataBridges: Data Integration for Digital Cities

Keywords: H.4 [Information Systems Applications]: Miscellaneous; D.2.12 [Interoperability]: Data Mapping Algorithms, Design, Human Factors, Performance Open Data, Digital Cities, Data Integration, Data Mapping, Provenance

The European Union has created the European Institute of Technology (EIT), within which ICT Labs focuses on fostering exchange and new result creation in the sphere of Information and Communication Technology, across the areas of research, higher education, and industrial innovation. "Digital Cities of the Future" is an action line (or chapter) of EIT ICT Labs. Within this action line, we coordinated an activity called "DataBridges: Data Integration for Digital cities", whose aim is to produce, link, integrate and exploit open data in the Digital Cities data space, for the benefit of both citizens and administrations. In that context, DataBridges addresses many research challenges such as acquiring (or producing) Open City Data in RDF, data linking and integration, data provenance, and visualization.

This position paper describes our efforts within DataBridges to integrate City Data. We provide a brief introduction to EIT ICT Labs, its goals and structures, and how DataBridges fits them. We then detail the activity and some selected results from 2011 -2012, and plans for 2013. We conclude with some research challenges we plan to adress in the future.

DIGITAL CITIES IN EIT ICT LABS

EIT ICT Labs (http://eit.ictlabs.eu) is one of the first three Knowledge and Innovation Communities (KICs) selected by the European Institute of Innovation and Technology (EIT) to accelerate innovation on Information and Communication Technologies (ICT) in Europe. Its goal is to foster innovation across Europe and to facilitate entrepreneurial research to rapidly bring research results to market. Within the area of Information and Communication Technologies, EIT ICT Labs has identified several action lines that are considered key societal issues in a number of selected areas. One of these action lines is Digital Cities of the Future. DataBridges is one activity in this action line.

To achieve its goal of more efficient and effective innovation, EIT ICT Labs brings together universities, research institutes, and companies within Europe to work on education, research, and industrial applications. In a research context, so called activities, such as the DataBridges activity, are multi-partner projects that span several institutions within EIT ICT Labs and that bring addedvalue to existing research projects of each activity partner through collaboration, facilitated people and result exchanges, help during standardization efforts, etc.

More specifically, each activity has a set of partners, and each partner has some carrier projects, funded by their own organization, by the government, or other sources. On top of these research carrier projects, catalyst tasks, funded by EIT ICT Labs, create added-value, as defined by the overall goals of EIT ICT Labs. In our discussion of DataBridges, we will not make the distinction between carriers and catalysts and simply present the goals and results of our activity.

OVERVIEW OF DATABRIDGES

Digital cities are tremendous marketplaces for sharing and producing digital information. The overall goal of DataBridges is to empower both individuals and institutions to make the best use of available data leading to exploitable information. Our vision is enabling applications that serve both citizens and administrations, showcased by the following scenario that we will use as testbed for our contributions.

Goals

Let us first illustrate our goals based on an example application that servers as an overall testbed for the technologies developed in DataBridges. By linking data from social media and Open Data, we will create a data set that allows citizens to plan what may be called "the perfect evening", e.g., find a restaurant with outside dining -if weather permits -having their favorite dish on their menu with reasonable price and quality and a night club not too far away to go after dinner, both reachable by public transportation at the intended times of travel. This requires data on points of interests (POIs), geographical coordinates, menus, weather, comments on the restaurants, public transportation, etc. The same data set, together with searches issued by citizens can be further analyzed to In this context, our activity has the research goals detailed below. Further goals include building a community through a series of workshops, participating to standard developments, and deploying our research in real applications.

G1 -Rich data. Provide rich data using the W3C's Resource Description Format (RDF for short), by leveraging information available in heterogeneous formats (relational, text, Excel etc.) to be mapped to RDF and by semantically enriching City Data with this new data. We also consider context information (time, location, environmental aspects) collected by devices such as SmartPhones or sensors to further enrich the data sets.

G2 -High quality linked data. Produce high quality linked RDF data sets to the Open City Data domain. Indeed, high quality data is essential for analytical applications on which administrative decisions (resource planning, water management, power plant planning, etc. ) are based.

G3 -RDF warehousing and analytics. Develop technologies for scalable RDF data warehousing and analytics to actually perform Business Intelligence style analysis over RDF data.

G4 -User-centric applications. Design user-centric applications by using user-friendly visualization techniques, intuitive and effective information retrieval targeted towards City Data, and focused on user needs based on social data analysis and exploitation and recommendations.

The beneficiaries of our results will be citizens, administrations and companies, especially SMEs that will profit from gaining access to high quality Open Data.

Partners

Several partners were/are involved in the DataBridges activity, summarized in Table 1 together with the main areas each partner is involved in, and the goal their work contributes to (G1 -G4). The following discussions provide more details on this work.

ACTIVITY RESULTS (2011-2012)

The activity focus in 2011-2012 has been oriented on three main axes. First, we focused on producing Open Data and enriching it as a means to exchange information within a Digital Cities setting.

Finally, we considered issues raised by efficiently and effectively exploiting (sharing) such data.

Producing Open City Data

Open Data is a wide-encompassing movement around the idea that data should be freely shared and used to build added-value applications. From the bare data interoperability perspective, RDF is generally agreed upon as having sufficient expressive power and flexibility to serve as the format of choice for encoding Open Data.

A first task studied in this context concerned the production of Open Data for digital city contexts. Public administration data was one of the foremost applications of the Open Data movement, pioneered in the respective projects http://data.gov for the USA, and http://data.gov.uk in the UK. Information on the cultural attractions, public equipments and services, businesses available within cities etc. are naturally part of this effort. Producing Open Data from proprietary databases, however, takes significant effort, mostly because of the variety of native formats in which the data was produced and stored by the various stakeholders.

Best practices.The Aalto University partner, in collaboration with the City Region of Helsinki, had established the Open Data portal of the Greater Helsinki Area, http://www.hri.fi. Within DataBridges, Aaalto contributed from a software engineering perspective, a set of lessons learned on the most effective software tools and methodologies for producing Open Data out of proprietary databases.

Open City Data production. The DataPublica partner (http: //datapublica.fr) is a French start-up which has pioneered the Open Data field in France by being the first to establish a large catalog of the Open Data produced by the national French administration. In France, important areas such as health, education, police, justice etc. are entrusted to the central government, who collects numerous statistics, most of which have been collected by DataPublica. They proceeded by manually establishing a catalog of public administration data portals and then semiautomatically feeding them into DataPublica's warehouse as a collection of datasets. A dataset corresponds to a specific category of information aggregated at a specific level over a specific period of time. For instance, premature children born in each department of France in 2005 is a dataset, as well as fruit production per kind of fruit in Languedoc-Roussillon in 2000-2008. Such datasets are the natural unit at which public administration tends to gather and consolidate data. Unfortunately, most of these datasets come under the form of Excel tables, whose exploitation is not obvious. DataPublica is currently exploring a collaboration with a machine learning team (LIRMM, France) aiming at automatically extracting schemas from such Excel tables; conversion of the relations thus obtained into RDF would be the next step. For the time being, Dat-aPublica relies on Google's DSPL (DataSet Publishing Language, https://developers.google.com/public-data/) to organize its datasets. One advantage in doing so is the possibility to take advantage of Google's data visualization facilities.

Integrating and enriching data

Open data sets by themselves can be considered data islands, and one goal of DataBridges is to build bridges between these islands to contribute to the Linked Open Data (LOD) vision. Linked Open Data aims at combining Open Data sources, by encouraging the publication of connections between facts within or across data sources. In digital cities, LOD may be issued by companies and local organizations, but also by the governing bodies of the city or of the country, in the spirit of the US initiative data.gov, for instance. DataBridges considers linking Open Data, but goes even further by actually integrating and enriching City Data to obtain rich and clean data sets.

Data linking. University of Paris Sud researchers have addressed the linked aspect of the LOD concept, by seeking to connect data from structured tables, to concepts from a given ontology. The motivation is quite obvious. Once structured information has been extracted from the Web under the form of tables (such as can be found in the Google Fusion Tables project http://www.google. com/fusiontables, or the kind of tables extracted from Data-Publica's Excel datasets as explained above), we may try to analyze and interpret information at the granularity of a single cell, in order to relate the content of the cells to concepts and values from a known domain ontology. Doing so enables one to combine (join) the data from the table, with other data sources, in order to analyze or enrich it etc. The right ontology to use can be manually supplied; this works well when a large number of (similar) datasets are extracted from a well-known, focused source (e.g. "The Ministry of Agriculture Yearly Production Statistics"). Alternatively, one may automatically choose the ontology by comparing attribute names and/or the data values against a large ontology, such as DBPedia etc.

Paris Sud has developed a geographic data type inference module, starting from geographical information from INSEE (http: //www.insee.fr), the French national statistics institute, and the DBPedia ontology. A second dataset which has been similarly semantically enriched consisted of museum and restaurant information.

RDF data integration.

The RDFGears project (http: //wis.ewi.tudelft.nl/imreal/u-sem/rdfGears/) at TU Delft allows the specification of workflows that extend and combine existing RDF datasets. RDFGears provides a language with formal syntax and semantics, an intuitive user interface and an efficient implementation based on algebraic dataflow optimizations. The platform can extract RDF data from external and internal data sources, and make use of a database back-end to store potentially large intermediate results.

Exploiting Open Data

Being able to navigate, search, and interpret the data is essential to extract valuable information from Open Data. RDF data management. Inria has focused on the efficient querying of LOD, or more generally, large RDF databases. A first contribution brought in this area concerns a framework for automatically selecting views to materialize in an RDF database in order to speed up the evaluation of a given workload of RDF queries [START_REF] Goasdoué | View Selection in Semantic Web Databases[END_REF]. A second contribution explores the possibility to host a large RDF database in a cloud setting, in particular within the Amazon Web Service (AWS) platform (http://aws.amazon.com) [START_REF] Bugiotti | RDF Data Management in the Amazon Cloud[END_REF].

User profile management. U-Sem [START_REF] Abel | U-sem: Semantic enrichment, user modeling and mining of usage data on the social web[END_REF] is developed within this action out of the European projects Grapple (http://www. grapple-project.org/) and ImREAL (http://www. imreal-project.eu/) and is building an infrastructure that allows to capitalize on social and personal data of citizens to allow for advanced user modeling, e.g., for personalization in digital City Data management applications. We focused our work mainly on Social Web datasets, in particular Twitter datasets, i.e., datasets of citizens tweeting via Twitter. It features a set of strategies that allow for semantic enrichment of Twitter messages (tweets) and relates tweets to external (Semantic) Web resources [START_REF] Abel | Semantic enrichment of twitter posts for user profile construction[END_REF]. It offers functionality such as entity recognition or discovery of relations between entities.

Facetted browsing.

A facetted browsing tool implemented by the DFKI partner has been applied on the dataset about museums and restaurants obtained as a result of Open Data linking (http://digitaleveredelung.lolodata. org:8080/DigitalCities/page/).

The interest of facetted browsing in this context is first, to allow users to explore the data by dimensions considered in the order that is most relevant to them, and second, to enable on-demand browsing through the heterogeneous, irregular data, ill-suited to more traditional search (based on fixed-schema forms).

Intelligent maps. A distinct DFKI project focused on integrating Open Data about POIs, into user-personalised, interactive maps. These are able to plan detailed trips within the city, finding objectives according to the user's specifications (e.g., children movie in the morning, restaurant at noon, park in the afternoon) and planning the trip while taking into account trip durations etc.

PERSPECTIVE FOR 2013

Our work plan for 2013 follows the same three research axes as in 2011-2012, i.e., data production, integration, and exploitation.

Producing Open City Data

In 2013, we shift our focus to exploit a large variety of sources to gather more Open Data, i.e., text, multimedia, relational, and streaming data. More specifically, the following efforts contribute to this goal.

Multimedia data.

LiveMemories (http://www. livememories.org) collects, analyzes, and preserves digital memories (audio, video, text, etc.) at a large scale for a city community, thus facilitating and encouraging the preservation of such community memories to ultimately enrich cultural and social heritage. LiveMemories has focused on achieving these goals for the region of Trento, and in the context of DataBridges, the produced data set will be further enriched with LOD.

Text data. Information extraction techniques allow to extract important information hidden in text, in our sample scenario, we are for instance interested in information hidden in user comments on restaurants, menus in pdf formats, news articles about closed roads, etc. This information will then be used to enriched City Data sets, hence, it will be mapped to RDF and linked to other RDF data we produce or that are readily available. The information extraction methods we will employ are (i) extensions of KnowledgeStore [START_REF] Cattoni | The knowledgestore: an entity-based storage system[END_REF], developed as part of the LiveMemories project to cover semi-supervised relation extraction and (ii) unsupervised information extraction engines developed in the Excitement (www.excitement-project.eu) and THESEUS Rad-Speech (http://www.dfki.de/RadSpeech/) projects.

POIs are of particular interest in the City Data domain. As part of COMMIT (http://www.commit-nl.nl), van Keulen et al. are investigating methods to automatically harvest POI information from the Web, an independent contribution to the generation of Open City Data.

Data streams. C-SPARQL [START_REF] Barbieri | C-sparql: a continuous query language for rdf data streams[END_REF] is a continuous extension of SPARQL, demonstrated to be effective in domains where a large number of heterogenous data streams (e.g., mixing sensor and social sensing) have to be processed to detect complex events. Politecnico di Milano investigated the Stream Reasoning concept, specified the C-SPARQL language, prototyped the C-SPARQL engine and deployed it in BOTTARI [START_REF] Celino | Bottari: Location based social media analysis with semantic web[END_REF], winner of the Semantic Web Challenge 2011. BOTTARI is an augmented reality mobile application that permits the personalized and localized recommendation of restaurants in Insadong (a district of Seoul) based on the temporally weighted opinions of the community. We will further study and apply the capabilities of C-SPARQL in processing streaming data in the DataBridges context.

Relational data. IBM CAS Italy intends to roll out an experimental semantic data integration platform named ONDA [START_REF] Cangialosi | Accessing data through ontologies with onda[END_REF], which is capable of integrating legacy relational sources by mapping them with a conceptual schema (ontology). Using ONDA in DataBridges has the potential of extending the type of data exploited by Digital Cities applications, as it adds relational data to the possible set of data sources.

Integrating and enriching data

Data linking and integration. Concerning data linking and integration, our efforts in 2013 will on one hand focus on extending or consolidating the contributions from previous years. More specifically, we will invest further effort in RDFGears to demonstrate its general applicability and to scale RDF data integration to large volumes of data. Linkage techniques as those developed at Paris Sud or [START_REF] Herschel | Scalable iterative graph duplicate detection[END_REF] will be used and adapted to link the POI data with social media pages in the COMMIT project.

Transformation and data quality.

The ultimate goal of DataBridges is to enable smart applications on top of Open City Data. One prerequisite for this is to use and produce high-quality data. Within DataBridges, we will first study processes lowering data quality in the City Data context to later on develop and apply appropriate data cleaning methods at scale. Sources of errors in result data are in general either errors in the data transformations leading to that data or errors in the data itself. For debugging purposes in this context, we will adapt why-and whynot provenance [START_REF] Herschel | Explaining missing answers to SPJUA queries[END_REF] techniques developed in the Nautilus project (http://nautilus-system.org) for relational data to RDF data and will implement these in RDFGears.

Data Contextualization. The Contextualized Knowledge Repository [START_REF] Homola | Contextualized knowledge repositories for the semantic web[END_REF], another contribution within the LiveMemories project, is an RDF repository that supports the management of context dependent data (i.e., data of which the validity depends on the context given by time, location, topic, agent, etc.). Context dimensions are important to represent time/location dependent data, domain specific data or subjective data. Clearly, contextual data is relevant in the Digital City domain, as context on weather and road conditions given to news articles on accidents or live reports on traffic jams can improve the interpretation and action upon these incidents (reduce speed limit when raining, etc.). In DataBridges, we will enrich our data sets with contextual data. In the DataBridges setting, such context data will for instance be produced by CrowdSearch, contributed by Politecnico di Milano, that represents a novel search paradigm that embodies crowds as first-class sources for the information seeking process. CrowdSearch aims at filling the gap between generalized search systems, which operate upon world-wide information -including facts and recommendations as crawled and indexed by computerized systems -with social systems, capable of interacting with real people, in real time, to capture their opinions, suggestions, and emotions.

Exploiting Open Data

In 2013, we plan to pursue our work on the faceted search component previously developed, as well as schematic maps and visualization (DFKI). We further continue our work on devising methods and algorithms for efficient and scalable RDF data warehousing (Paris Sud). Moreover, we intend to deploy BOTTARI (see Section 4.1) in Europe.

As a testbed combining the technologies developed by activity partners, we will also set up an actual application resembling the one described in the introductory example of Section 2.1.

CONCLUSION AND PERSPECTIVES

We have described the DataBridges activity, connecting partners from academia and industry to generate rich and high quality City Data, define methods for RDF data warehousing and analytics, and build user-centric applications for Smarter Digital Cities. The exploitation of City Data is still in its infancy in DataBridges, which is natural since we first need to have rich, high quality data sets to exploit. In the future, we plan on devising more advanced analytics, applications, visualizations, etc. to get from this raw-data to actual information of interest to the user.

Moreover, we will invest further efforts in making our methods scale to large volumes of data. Possible avenues of research include extending our work to the cloud or defining new algorithms and tools, e.g., for fully automatic linking, quality assessment, and visualization.

Table 1 :

 1 DataBridges partners and contributions

	Partner	Research topics
		2011 -2012
	Aalto U	Hackathon events, best practices for data production (G4)
	Bell Labs	Augmenting social data with semantics (G1)
	DataPublica Open City Data production (G1, G2)
	Inria	RDF data management (G3)
	TU Delft	User profile management (G4), RDF data integration (G1)
	DFKI	Intelligent maps (G4), facetted browsing on City Data (G4)
	KTH	Hackathons applied to City Data (G4)
	U Paris-Sud Data linking (G2)
		2013
	FBK	Enrichment with context data (G1)
	IBM	From relational data to Open City Data (G1)
	Pol. Milano C-SPARQL to process stream data (G1)
		Augmented reality mobile applications (G4)
	Siemens	Machine-learning based recommendation (G4)
	U. Twente	Point of Interest harvesting & linking (G1, G2)
	TU Delft	RDF data integration (G1), RDF data provenance (G2)
	DFKI	Schematic maps (G4), information extraction (G1)
	KTH	Hackathons applied to City Data (G4)
	U Paris-Sud RDF data management (G3)
		RDF data provenance (G2), Data quality (G2)
	help administration in optimizing public transportation, identifying
	where POIs should be favored for city development, etc.

Acknowledgements:

We thank all partners of the activity (past and prospective), namely the task leaders Jan Hidders (TU Delft), Luciano Serafini (FBK), Guido Vetere (IBM), Yi Hang (Siemens), Bernardo Magnini (FBK), Daniel Sonntag (DFKI), Chantal Reynauld (Paris Sud), Maurice van Keulen (U. Twente), Dominikus Heckmann (DFKI), Yi Hang (Siemens), Inessa Seifert (DFKI), Emanuele Della Valle (Milano), Hannes Ebner (KTH), François Goasdoué (Paris Sud), Geert-Jan Houben (TU Delft), Hakim Hacid, (Alcatel Lucent Bell Labs), Petri Kola (Aalto), as well as all those who have participated to the individual tasks.

This work is partially funded by EIT ICT Labs activity 10803 and the French national grant ANR-11-EITS-003.


Melanie Herschel
email: melanie.herschel@lri.fr

Hanno Eichelberger
email: hanno.eichelberger@student.uni-tuebingen.de

The Nautilus Analyzer: Understanding and Debugging Data Transformations

Keywords: H, 4 [Information Systems Applications]: Miscellaneous data provenance, query analysis 1, NAUTILUS ANALYZER OVERVIEW

When developing data transformations-a task omnipresent inapplications like data integration, data migration, data cleaning, or scientific data processing-developers quickly face the needtoverify the semantic correctness of the transformation. Declarative specifications of data transformations, e.g., SQL or ETL tools, increase developer productivity but usually provide limited or no means for inspection or debugging. In this situation, developers today have no choice but to manually analyze the transformationand, in case of an error, to (repeatedly) fix and test the transformation.

The goal of the Nautilus project is to semi-automatically support this analysis-fix-test cycle. This demonstration focuses on one main component of Nautilus, namely the Nautilus Analyzer that helps developers in understanding and debugging their data transformations. The demonstration will show the capabilities oft h i s component for data transformations specified in SQL on scenarios from different domains that are based on real-world data.

We provide an overview the Nautilus Analyzer, discuss components and implementation techniques, and outline our demonstration plan. The Nautilus website (http://nautilus-system.org) features a video, screenshots, and further details.

should return the same number of tuples (a realistic requirement for instance in a scenario where every department in the result of Q 1 needs to be associated with a manager in the result of Q 2 ). Should that constraint be violated, the developer suspects the error to be in Q 1 .H et h u sd e c l a r e st h er e s u l to fQ 2 over source instance D,denoted as Q 2 (D),as"trusted",meaningthattheextensionofQ 2 (D) should not change during the analysis-fix-test process. For analysis, the above specification translates to the questions "Why are there too many tuples in the result of Q 1 ?" (Why-question) or "Why are tuples missing from the result of Q 1 ?" (Why-Not question). 1 Essentially, when using Nautilus, a developer specifies whath e wants during analysis in the form of a debugging scenario,asillustrated above. The result of analysis is a set of explanations that provide guidance for the fix and test phases. These explanations take the form of provenance information, i.e., the Nautilus Analyzer relies on Why-provenance [START_REF] Cheney | Provenance in databases: Why, how, and where[END_REF] and Why-Not provenance [START_REF] Chapman | Why not? In SIGMOD Conference[END_REF][START_REF] Herschel | Explaining missing answers to SPJUA queries[END_REF][START_REF] Huang | On the provenance of non-answers to queries over extracted data[END_REF][START_REF] Tran | How to ConQueR why-not questions[END_REF] to generate explanations answering the respective types of questions. Without Nautilus, the traditional manual process requires the developer to identify "suspicious" spots of the transformation, to explicitly spell out changes, and to verify that the manual changes do not violate the constraints in the debugging scenario.

Continuing our example of Fig. 1, we observe that the requirement that both queries return the same number of tuples is not met. Therefore, the developer asks a Why-Not question on the result of Q 1 .T h ef o l l o w i n ga n s w e r sa r ep o s s i b l e :( i )R needs to contain one more tuple that joins with a tuple in T in order to return three tuples as well (instance-based explanation [START_REF] Herschel | Explaining missing answers to SPJUA queries[END_REF][START_REF] Huang | On the provenance of non-answers to queries over extracted data[END_REF]), (ii) the join between R and T is the operator filtering tuples from the result (query-based explanation [START_REF] Chapman | Why not? In SIGMOD Conference[END_REF]), or (iii) the query would produce the desired result if the join was replaced by a left-outer join (modification-based explanation [START_REF] Tran | How to ConQueR why-not questions[END_REF]). Let us denote these explanations as X IB , X QB ,a n d X MB ,respectively .

Explanations cover many different possibilities of why the desired result was (not) computed. Should, based on this information, the developer deem the query to be correct, he is done. Otherwise, if the returned explanations suggest that one of the queries is faulty, a subsequent fix phase becomes necessary. During the fix 1 Processing the scenario without trusting any table would result in alargersetofWhyandWhy-Notquestionstobeanswered. phase, Nautilus suggests changes to the queries in order to meet the requirements specified in the debugging scenario. To guide the fix algorithms, the developer, during analysis, may attach a Boolean annotation to some explanations,i d e n t i f y i n gt h e ma sb eing (ir)relevant. For instance, he may deem the query-based explanation described above as relevant, but may disagree witht h e modification-based explanation. This translates his belieft h a tt h e join operator is indeed the faulty operator, but that a left-outer join is not the solution to the problem. The Nautilus Analyzer exploits these annotations in subsequent steps in the same or future analysisfix-test iterations.

R A B C 1 2 3 4 5 6 7 8 6 T C D 1 3 2 6 6 8
Q 1 : SELECT A, D FROM R, T WHERE R.C = T.C AND R.C < 10 Q 2 : SELECT C FROM R WHERE C <10
As the number of explanations can become quite large, Nautilus also provides means on ranking these. Our ranking model takes into account both heuristics and explanation annotations, e.g., if the user marks the instance-based explanation above as interesting, similar and related explanations will be ranked higher.

The Nautilus Analyzer consists of two main components to implement the above functionality: a graphical user interface (GUI) to graphically and intuitively specify a debugging scenarioa n dt o interact with produced explanations and the explanation manager. Due to the lack of space, we focus the technical discussion on the explanation manager and refer interested readers to the project website for screenshots and a video demonstrating the GUI.

THE EXPLANATION MANAGER

The input of the explanation manager consists of a debugging scenario and optional developer-provided Boolean annotations. The explanation graph is the central data model that represents all explanations. Nautilus initializes and refines the graph based on interactions with the four main components of the explanation manager: (i) the explanation generator,( i i )t h eexplanation annotator, (iii) the annotation analyzer,and(iv)theexplanation ranker.

Explanation generator. Given a debugging scenario, the explanation generator returns a set of explanations. In general, a debugging scenario S = {Q, D, Q(D), E,C} includes a set of SQL queries Q to be analyzed, the source instance D,th es e to fq u e ryre s u ltsQ(D), the set of tuples (existing or missing) to be explained, denoted as E,a n das e to fc o n s t r a i n t sC.I n o u rc u r r e n ti m p l e m e n t a t i o n , C is limited to trusting or minimally affecting tables in D or query results in Q(D).T h i sd e fi n i t i o no fad e b u g g i n gs c e n a r i on a t u r a l l y extends its definition in [START_REF] Herschel | Explaining missing answers to SPJUA queries[END_REF]. In our example, the debugging scenario is defined by

Q = {Q 1 , Q 2 }, D = {R, T }, Q(D)={Q 1 (D), Q 2 (D)}, E = {missing tuple from Q 1 (D)},andC = {trust on Q 2 (D)}.
The debugging scenario translates to a (set of) Why or Why-Not provenance questions. Each question is then processed usingoneor more of the implemented algorithms. These include Why-Not [START_REF] Chapman | Why not? In SIGMOD Conference[END_REF], ConQuer [START_REF] Tran | How to ConQueR why-not questions[END_REF], Missing-Answers [START_REF] Huang | On the provenance of non-answers to queries over extracted data[END_REF], Artemis [START_REF] Herschel | Explaining missing answers to SPJUA queries[END_REF], and lineage [START_REF] Cui | Tracing the lineage of view data in a warehousing environment[END_REF] 2 . The result of each algorithm is generalized as a set of explanations, which form the input to the explanation graph initialization. In our example, we return the Why-Not explanations mentioned in the introduction.

Explanation graph. To initialize the explanation graph, we conceptually proceed as follows. Please note that our implementation does not strictly follow this descriptions for efficiency reasons.

Conceptually, we first consider a logical operator tree representation, the query tree,ofthequeriestobeanalyzed,e.g.,ofQ 1 .Ea c h computed explanation results in an explanation node that connects to nodes in the query tree via edges directed from the explanation 2 The authors of [START_REF] Chapman | Why not? In SIGMOD Conference[END_REF][START_REF] Tran | How to ConQueR why-not questions[END_REF] kindly provided us with their code, which significantly facilitated the re-implementation and integration into our Nautilus Eclipse plugin. As for lineage, we reuse the lineage capabilities of the Trio system [START_REF] Widom | Trio: A system for data, uncertainty, and lineage[END_REF]. node to query tree nodes. The edge between a query-based explanation (e.g., X QB )simplyconnectstheexplanationnodetothequery tree node that represents the identified culprit operator, whereas a modification-based explanation (e.g., X MB ) connects to the query tree nodes affected by the modification. As for instance-based explanations, we create equivalence classes based on what we call "join patterns". To illustrate these, let us assume that no trust lies on Q 2 (D) and hence, T is subject to modification. Then, three possible patterns for instance-based explanations exist: (i) insert into R and T s.t. the join is satisfied, (ii) insert to R s.t. the inserted tuple joins with an existing tuple in T ,and(iii)inserttoT s.t. the inserted tuple joins with an existing tuple in R.T h i sr e s u l t si nt h r e ee q u i v alence classes which we denote C 1 , C 2 ,andC 3 .E x p l a n a t i o n sw i t h i n an equivalence class only differ w.r.t. the existing tuples involved and convey the same information. Thus, we regard these as equivalent. For each equivalence class, we create a class node in the graph. The class node connects to each query tree node that either represents a source table to which an insertion applies or that represents an unfulfilled join. Individual instance-based explanations appear in the explanation graph as nodes that simply connect to their respective class node. Considering all three types of Why-Not explanations of our example, at this point of conceptual processing, we obtain the graph shown in Fig. 2(a) (omitting the individual instance-based explanation nodes for conciseness).

In a second step, we abstract from the query nodes and directly connect explanations with weighted edges. The weight corresponds to explanation similarity. To determine the similarity of two class nodes, we compute the Jaccard coefficient of query tree nodes reached from the pair of compared explanations. For all remaining pairwise combinations of explanation types, the Jaccard coefficient is solely based on the inner query nodes (i.e., source tables are no longer considered). Fig. 2(b) shows the resulting explanation graph (individual instance-based explanations, again omitted, are all considered equivalent, so their similarity within an equivalence class equals 1, and their similarity across equivalence classes isequalto the similarity of the respective equivalence classes). Please ignore the dashed edges for now. Explanation annotator. Annotations are either user defined or input by other Nautilus components 3 .T h eg o a lo ft h ee x p l a n a t i o n annotator is to link annotations provided by a developer through the GUI to explanations in the explanation graph. We support two user-annotations, i.e., "relevant explanation" and "irrelevant explanation". A developer provides these for an explanation if he decides that the inspected explanation provides valuable information in the context of the debugging scenario or not, respectively. Notet h a t not all explanations need to be annotated by a developer. For those not explicitly annotated, we assume an annotation of "unknown" or, as discussed next, derive an annotation using the explanation annotation analyzer.

Internally, the Nautilus Analyzer translates annotations to probabilities that indicate the probability that an explanationp i n p o i n t s the problem to be detected by the developer. A user-annotation of "relevant explanation" translates into a probability of 1whereas "irrelevant explanation" corresponds to a value of 0. Whenever no annotation (user-or system-provided) is available, a default probability (currently 0.5) is assigned to an explanation.

Annotation analyzer.

Based on annotations collected by the explanation annotator, the explanation annotation analyzer derives further probabilities for explanations similar to the annotated ones. To this end, it spans a Bayesian network [START_REF] Pearl | Probabilistic Reasoning in Intelligent Systems: Networks of plausible inference[END_REF] over the explanationgraph as follows: user-annotated explanations serve as source nodes that provide the prior probabilities. Starting from these nodes,weth e n traverse the explanation graph in breadth-first, only following edges whose attenuated similarity is above a given threshold .T h ea ttenuated similarity of an edge e on a path of length k starting at au s e r -a n n o t a t e de x p l a n a t i o ni sc a l c u l a t e da s 1≤i≤k sim i × k-1 , where 0 ≤ ≤ 1denotesanattenuationfactor.W ecomputeconditional probabilities for all explanations reached during this traversal procedure. The details on the conditional probability functions are out of the scope of this paper, essentially, we have defined a function for each combination of source and target explanation type. The functions either return a Boolean or a real-value.

Fig. 2(b) shows an example of the network we span assuming X QB (blue dashed edges) and an explanation in C 1 (purple dashed edges) are annotated as irrelevant and relevant, respectively, = 0.7 and = 0.8. Intuitively, the fact that the query-based explanation is invalidated also invalidates the modification-based explanation on the same query operator, e.g., we propagate a probability of 0t o X MB .H o w e v e r ,C 1 being relevant indicates that the join operator is after all relevant and hence a probability of, e.g., 1 3 propagates X MB whose probability is then re-evaluated.

Explanation ranker. From a user perspective, it is crucial that he does not drown in an overwhelmingly large number of explanations and that the analysis of few explanations is rewarding, i.e.,r e l evant explanations are soon detected. To this end, Nautilus incorporates an explanation ranker. Initial ranking is based on pre-defined heuristics but as the developer provides annotations, the ranking adapts to these to favor explanations with higher probabilities.

Ranking heuristics for Why-provenance include ordering explanations by (i) number of tuples involved, (ii) similarity, and (iii) diversity. Whereas the first criterion is self-explanatory, let us briefly explain the rationale behind the other two heuristics. Similarity will sort similar explanations next to each other (calculated based on shared tuples and query operators involved) whereas diversity aims at presenting different explanations consecutively. Intuitively, similarity is a good choice for eager annotators whereas diversity represents a better choice for lazy annotators. For Why-Not explanations, we additionally consider ranking based on side-effects an instance-based or modification based explanation may have (i.e., changes in query results if tuple insertions or query modifications were actually performed, which they are of course not during analysis). In our example, an initial ranking of instance-based explanations based on diversity for instance returns as top-3 an explanation from C 2 ,anexplanationfromC 3 ,andanexplanationfromC 1 .

In summary, the explanation ranker module is still in a preliminary state compared to the remaining components, as the current ranking schemes only represent a first step towards a unified ranking of explanations (without the user specifying the function). The research on explanation ranking is an ongoing effort and we plan on incorporating more sophisticated ranking algorithms in thefuture.

DEMONSTRATION PLAN

The Nautilus system, and hence its Analyzer component, is implemented as an Eclipse plugin in Java 1.5. Using the Eclipse platform has two significant advantages: first, it allows us to seamlessly integrate the Nautilus components in a platform with which developers are already familiar with; second, it comes with arich set of plugins that we can leverage. For instance, the demonstration we propose makes use of and extends the Data Tools Platform (http://www.eclipse.org/datatools/)a n dt h eZ e s tG r a p h Visualization Toolkit (http://www.eclipse.org/gef/zest/). Note that the Nautilus Analyzer significantly extends the Eclipse plugin we demonstrated for Artemis [START_REF] Herschel | Artemis: A system for analyzing missing answers[END_REF], an algorithm we developed to generate instance-based explanations for Why-Not provenance [START_REF] Herschel | Explaining missing answers to SPJUA queries[END_REF]. Screenshots and a video are available on the project website.

At the conference, we will demonstrate the Nautilus Analyzer using several debugging scenarios and at least three use cases. The first use-case reuses the sample CRIME scenario provided by the Trio system(http://infolab.stanford.edu/trio/). The second scenario comes from the government domain using publicly available data about US Congressmen(http://bioguide.congress.gov), Spendings(http://usaspending.gov), Earmarks (http://earmarks.omb.gov), etc.. The third scenario relies on IMDB movie data (http://www.imdb.com). Note that the last two scenarios are based on real-world data. Whereas the CRIME scenario will be used as introductory scenario. the queries demonstrated on the real-world datasets will be inspired by our previous data integration and data cleaning experiences on these data sets. The query complexity will go beyond the toy queries shown in this paper and will include aggregation and negation when applicable. Through these scenarios, we will demonstrate the functionality of all components described in this paper and show how the Nautilus Analyzer successfully allows developers to understand and debug complex SQL queries.

Figure 1 :

 1 Figure 1: Sample data and queries (note: |Q 1 | = 2 = 3 = |Q 2 |).

Figure 2 :

 2 Figure 2: Creating the explanation graph

At the current development stage, the Nautilus Analyzer onlysupports user-defined annotations and annotations derived by the annotation analyzer. In the future, the fixing and testing components of Nautilus may contribute further annotations.

Acknowledgements. This research is partially funded by the Baden Württemberg Stiftung. We thank the students having contributed to the visualization and annotation analyzer components as well as Adriane Chapman and Quoc Trung Tran to have shared their programs with us.


H Hidalgo

A.-L Thomann

T Lecas

J Vulliet

K Wittmann-Teneze

D Damiani

E Millon

P Brault

Optimization of DC reactive magnetron sputtering deposition process for efficient YSZ electrolyte thin film SOFC

Keywords: Anode-Supported SOFC, Thin films, magnetron sputtering, YSZ, electrolyte

Yttria-stabilized zirconia (YSZ, ZrO 2 :Y 2 O 3 ) thin films were deposited by reactive DC magnetron sputtering with a high deposition rate from a metallic target of Zr/Y in an argon/oxygen atmosphere. Plasma parameters and composition analysis of the gas phase reveal that the sputtering process in the "compound" mode is reached for a 2.5 sccm oxygen flow rate. Deposition onto silicon in "metal" mode at a flow rate close to the transition, allows obtaining at very high deposition rates (> 10 µm.h -1 ) a compact columnar stoichiometric crystallized YSZ film.

When deposited on NiO-YSZ commercial anode, the obtained coatings show the same properties. In spite of the complexity of the substrate (roughness and porosity), a compact and conformed layer was formed. Annealing treatments in air or hydrogen do not significantly alter the structure of the layers. Electrochemical test at 850°C with a screen-printed LSM (LaSrMnO 3 ) cathode exhibits a satisfying gastightness (OCV=900 mV) and a maximum power density of 350 mW.cm -2 .

Introduction

Fuel cells are one of the most effective techniques to convert fuel (hydrogen, syngas…) into electricity. Among the different technologies [1], solid oxide fuel cells (SOFC) exhibit a high tolerance to fuel impurities and the possibility to combine production of electricity and heat [2]. The most common ceramic used as SOFC electrolyte is yttria stabilized zirconia -xYSZ, (ZrO 2 ) 1-x :(Y 2 O 3 ) x -which presents an optimal ionic conduction in the chemical composition range: 8YSZ-12YSZ [3]. Nevertheless, high working temperatures (800-1000°C) are needed to ensure sufficiently high internal conductivity, but leads to thermal or mechanical degradation of the cell materials.

An effective way to increase the reliability and to lower the cost of SOFC systems would be to reduce the size of the cell and particularly the electrolyte thickness. In that aim, µ-SOFC devices were developed [4] with an electrolyte thickness of about 1 µm.

In order to ensure that the total internal resistance of a fuel cell is sufficiently small, the ASR (area specific resistivity: ratio between the thickness and the conductivity of the layer) of the electrolyte must be lower than 0.15 .cm 2 [5].

As at intermediate temperature (600°C), the electrolyte layer conductivity has to be higher than 10 -2 S.cm -1 , the classical 100 µm-thick 8YSZ layer can not be used. In this range of temperature, the thickness of an YSZ electrolyte layer should be lowered down to 10 µm.

Conventional firing ceramic processing techniques such as screen printing, and tape casting lead to prepare 10-100 µm thick YSZ electrolyte layers. Nevertheless, there are works showing that screen-printing of YSZ electrolytes for SOFC is possible with thicknesses of around 10 micrometers and below [6][7]. Among the alternatives, vacuum thin films technologies are advanced methods that allow thin layer (0.1-20 µm) synthesis, avoiding a high temperature sintering step (>1000°C) [8]. Plasma magnetron techniques [START_REF] Wasa | Thin Film Materials Technology: Sputtering of Compound Materials[END_REF] are very weakly pollutingneither toxic gases nor liquid emissionsindustrial methods. By these last techniques, uniform thin films can be obtained over large areas at quite low temperature (<600°C). Properties of the films can be monitored in wide ranges by the choice of the deposition conditions.

Previous investigations have shown that dense, gastight, electronic insulating and ionic conductor YSZ thin film can be successfully deposited by DC magnetron sputtering from metallic targets [START_REF] Briois | [END_REF]11] or by RF magnetron sputtering of a ceramic YSZ target [12]. Ni-YSZ anode [13] and LSM cathode [14] can also be deposited by these techniques.

In the present work, a study of the plasma according to the target voltage and the oxygen flow rate is first made to evidence and control the metal and compound sputtering modes. 10 μm-thick YSZ films are grown on (100) silicon single crystal and commercial NiO-YSZ anode substrates. Deposition conditions and substrate nature which influences the film properties are studied and discussed. Finally, the fuel cell performance of YSZ films showing the most promising properties are evaluated through electrochemical tests.

Experimental

Magnetron sputtering device

Magnetron sputtering experiments were performed in a stainless steel (Ø=15 cm; h=13 cm) deposition chamber (Figure 1). Before deposition, the chamber was evacuated down to 10 -4 Pa using a turbo molecular pump; the deposition pressure was 1.2 Pa. Argon (Air Liquide 99.9995%) and oxygen (Air Liquide 99.9995%) flow rates were monitored by two mass flow controllers and entering the chamber by the same tube. A vacuum-melted metallic zirconium-yttrium target (82/18 wt%, NEYCO 99.5%) was clamped to a 2"planar magnetron (Thin Film Consulting ION'C-2") powered by a direct current power supply (Advanced Energy Pinnacle+). The current intensity was set at 1 A, which corresponds to a specific power density close to 12 W.cm -2 . This device was optimized to enhance the deposition rate by using a 5 cm-long chimney to avoid deposition everywhere in the chamber. The samples were fixed with silver paste on a substrate holder located 7 cm away from the cathode.

During deposition, the surface temperature of the sample was not regulated. In this study, only the influence of the oxygen flow rate will be considered.

Process and characterizations of thin films

The film thickness and the surface morphology were determined by scanning electron microscopy (SEM, Carl Zeiss supra-40 FEG-SEM) by using the secondary electron mode detection. The film composition was studied by energy dispersive X-ray spectroscopy (EDS, Bruker XFlash Detector 4010 coupled with the SEM) and by Rutherford Backscattering Spectroscopy (RBS) using a 2 MeV He ion beam from a Van de Graaf accelerator (facility of CEMHTI laboratory, Orléans). RBS is used to evaluate the global content of heavy elements in the thin films (zirconium and yttrium). However, the molar mass of the two elements are too close to distinguish Zr and Y contribution by RBS. EDS is thus used to estimate the ratio between zirconium and yttrium. The concentration of oxygen is measured by Nuclear Reaction Analysis (NRA) from the 16 O (d, α) 16 N reaction. The calibration is carried out using a Ta 2 O 5 reference sample (International Standard Reference). RBS and NRA spectra were simulated with the SIMNRA software [15]. The control of phases and their preferred orientation regarding the surface substrate were performed by -2 X-Ray Diffraction (XRD) using the Cu K radiation (λ=1.54056 Å) in the 25°-62° 2 range.

Analysis of the reactive phase composition was carried out by Optical Emission Spectroscopy (OES) above the substrate holder using an optical fiber through the porthole at the entrance of argon gas (Figure 1). A spectrometer BWtek BCT110E (1 nm resolution) records the emission spectrum of the plasma in the range 300-900 nm.

The electrochemical properties were studied on single cell assembly according to Figure 2. The experimental device allows feeding the cell with water and hydrogen diluted with nitrogen at the anode side of the cell, and an air supply at the cathode side. Hydrogen causes the reduction of nickel oxide in the cermet during early operation of the cell. Finally, specific wires dedicated to voltage measurements (no current flow) were used as electrical connectors. The anode side was sealed contrary to the cathode one since the enhancement of unused oxygen was not under investigation. A load (200 g.cm -2 ) was applied to the cell to ensure a proper electrical contact. The temperature of the system was raised to 850 °C at a rate of 0.5 °C.min -1 under nitrogen atmosphere. Hydrogen was then gradually introduced up to 12 NmL.min -1 .cm -2 to ensure the reduction of the cermet and an air flow of 12 NmL.min -1 .cm -2 is set. Prior to the characterization of the cell as a function of various parameters, a quick test at 850 °C was performed in order to check the system (integrity of the cells, full reduction of cermet, gastightness...).

Whereas a commercial NiO-YSZ plate will be used as anode, two types of cathodes were tested:

(a) 2 µm-thick porous platinum deposited by screen painting at room temperature and annealed at 900°C for 30 min: this platinum reference cathode is only an electronic conductor; the oxygen dissociation occur only at the contact line between the platinum surface and the YSZ electrolyte one;

(b) 38 µm-thick porous double layer lanthanum strontium manganite (LSM): this real SOFC cathode is composed by a composite YSZ(50%)/LSM(50%) porous layer (32µm, electrolyte side) on a pure LSM layer (16µm, current collector side) which requires a 1300°C sintering step of all the cell. LSM (purchased to Fuel Cell Materials) has the composition La 0.8 Sr 0.2 MnO 3 . Contrary to platinum, LSM is a mixed ionic electronic conducting ceramic which leads to oxygen reduction reaction on all the surface of the LSM porous layer [16].

Results and Discussion

Plasma analysis

In order to characterize the deposition conditions, it is interesting to study the behavior of the plasma when the experimental parameters (oxygen flow rate) are modified.

The plasma light emission was observed by the lateral window used for OES. In the absence of oxygen, the plasma is confined within the chimney near the cathode and only illuminates the exposed face of the substrate holder. The introduction of a low oxygen flow induces a change in the plasma color. For high oxygen flow rates, the plasma completely changes and is no longer localized in the chimney vicinity, but fills the entire reactor. The oxygen flow thus plays a major role on the expansion of the plasma emitting zone.

While the process is current regulated, the voltage varies with plasma experimental conditions. Figure 3 presents the evolution of the cathode voltage and of the pressure measured in the chamber with respect to the oxygen flow rate while maintaining the same pumping speed. When the oxygen flow rate increases, the target voltage undergoes a small increase up to a critical point. At the same time, the pressure remains stable in the chamber. For a flow rate of 2.5 sccm, the voltage sharply rises and the pressure reaches a value slightly lower than that measured in the absence of the plasma (dotted line). Beyond 2.5 sccm, the voltage increases linearly.

The pressure undergoes the same increasing as it would do without plasma. For a decreasing oxygen flow rate, a transition of the same type occurs but at much lower flow rate value (0.8 sccm) and the values before the cycle are retrieved. A similar hysteresis behavior has already been described by Venkataraj [17] in reactive sputter deposition of YSZ for the same oxygen flow rates. This highlighted hysteresis is characteristic of a reactive sputtering process [18]. Initially, oxygen which has a very strong chemical affinity for metals is introduced in an amount sufficiently low that it is completely gettered on the reactor walls, the sputtered target and the freshly deposited metallic atoms. So the measured pressure remains unchanged. The gas phase, mainly consisting in argon, is unchanged: the plasma characteristics and the target voltage do not evolve. This is known as "metal" sputtering mode. When the oxygen amount is increased, this behavior remains until the metallic vapor / surface atoms cannot getter all the oxygen any more. Beyond, oxygen can completely cover the metallic sputter target and poisons it, forming insulating (ZrO 2 and Y 2 O 3 ) oxides. Thus the sputter voltage increases in order to maintain the current and the unadsorbed oxygen causes a sudden increase of the pressure. Theses conditions are known as "compound" sputtering mode. As the secondary electron emission coefficient is larger for the zirconium oxide than for pure metal [18], the measured voltage for a given current is expected to be higher in absolute value and the efficiency of the sputtering is strongly reduced.

Reducing the oxygen flow, the "compound" mode is maintained down to the point for which all the gettered oxygen is removed from the sputter target surface to return to "metal" mode.

An optic fiber collects the signal from the plasma 1 cm above the substrate holder. Four neutral excited species (Ar, Zr, Y and O) were detected respectively at 750 nm, 481 nm, 407 nm and 777 nm and the Ar + excited ion at 488 nm. No molecular species have been detected. Figure 4(a) presents the evolution of the 750 nm argon line intensity as a function of oxygen flow rate. The two sputtering modes are easily observable: in "metal" mode, the argon intensity is weak, whereas in "compound" mode, intensity is ten times larger. It is known that the upper level of this argon line is populated by electronic collisions from the ground state level. The evolution of this line is therefore representative of the energetic state of the plasma, i.e. the electron energy distribution function.

Thus, the intensity ratio between lines of the other elements and the Ar (750 nm) one allows, in a first approximation, to get rid of the evolution of plasma parameters (electron density and electron temperature) in different experimental conditions. Figure 4 (b,c,d) represents the evolution of the intensity ratios (Zr/Ar and Y/Ar, O/Ar and Ar + /Ar) for all the detected species with respect of the oxygen flow rate. In the "metal" mode, the ratios of zirconium and yttrium tend to increase with the introduction of oxygen up to the transition. On the contrary, in "compound" mode, no lines were detected. The ratio of atomic oxygen follows a different trend: atomic oxygen is not detected in "metal" mode. After the transition, its value sharply increases and then linearly with the oxygen flow rate. When the oxygen flow rate decreases, the atomic oxygen ratio decreases linearly until the transition beyond which atomic oxygen becomes undetectable. This evolution is similar to that of the pressure in the chamber and shows that, in "metal" mode, atomic oxygen concentration is very low in the gas phase, unlike in the "compound" mode. From Figure 4d, it appears that Ar + ions are only detected in "compound" mode. This proves that -as can also be deduced from the increase of the 750 nm argon line intensity (Figure 4a) and the delocalization of the plasma far from the target -the plasma created in "compound" mode is very different from that obtained in mode "metal". It is more energetic around the substrate holder.

The plasma analysis clearly evidences both sputtering modes. For an oxygen flow rate below 2.5 sccm, the plasma is localized near the cathode. Metallic elements are sputtered away from the target and atomic oxygen is not detected in the gas phase: the sputtering is in "metal" mode. For a oxygen flow rate beyond 2.5 sccm, in "compound" mode, the plasma is not confined near the target but fills the whole chamber. Atomic oxygen is present in the gas phase while the metal species (Zr and Y) become undetectable; in this mode, the surface of the metallic target is fully oxidized and the argon ions sputter ZrO and YO [19]. Unfortunately, ZrO and YO molecular bands (respectively at 482.8 nm and 613.2 nm) could not be detected by our optical detection system.

Deposition on silicon

The properties of the YSZ films deposited by magnetron sputtering have been studied by using (100) silicon wafer substrates which are more suitable for surface analyses.

The conditions of deposition, for studying the influence of oxygen flow rate on the investigated films, have been chosen from the electrical characterization of the plasma. Four conditions were selected: one without oxygen, two in "metal" mode (1 and 2 sccm) and one in "compound" mode (3.5 sccm). The argon flow rate is maintained at 4 sccm. In "metal" mode, the deposition rate (Figure 5) reaches 15 µm.h -1 ; which is higher than reported in literature [20,21] because of the high power density applied on the target (12 W.cm -2 ) and the close vicinity to the substrate.

The increase of oxygen flow rate until 2 sccm tends to enhance the deposition rate. This trend has already been observed in magnetron sputtering of various metals in oxygen atmosphere and is usually attributed to the so called gettering effect [22,23]. Indeed, before the transition, i.e. before the target gets fully poisoned, the growing film adsorbs oxygen atoms and the deposited thickness is higher than for a pure metallic film [19,24].

However, this phenomenon can be not significant for ZrO 2 deposition [25]. In the "compound" mode, on the contrary, the deposition rate becomes very low (0.8 µm.h -1 ) and which is not suitable for the deposition of a few micrometer thick films required for the SOFC application.

The morphology of the films was studied by surface and fracture cross-section (Figure 6) SEM observation. The coating obtained without oxygen presents very fine columns. On the surface, no visible structures are identified.

The addition of 1 sccm of oxygen induces a significant change in the film morphology: in cross-section, the film exhibits a more compact structure while the surface is composed of grains smaller than 100 nm. By increasing the oxygen flow rate, the structure becomes columnar with a larger column width. The surface topology is characterized by angular-shape grains of about 200 nm width. Film obtained in "compound" mode (3.5 sccm) is not thick enough to determine its cross-section morphology by SEM. On the surface, the structure appears smoother and numerous small grains are distinguishable.

Figure 7 presents the evolution of the elementary composition of the thin film with respect of the oxygen flow rate. The yttria substitution rate of zirconia (xYSZ) is obtained from the proportion of Zr and Y in the thin films and the oxidation level () of xYSZ is calculated from the ratio of oxygen amount in the film to the oxygen amount expected if the metallic elements were fully oxidized from the following equations:

100 ] [ ] [ 2 ] [     Y Zr Y x Equation 1 x O x x O         2 ] [ 3 ) 1 ( 2 ] [ Equation 2
For the film obtained in pure argon atmosphere, the x parameter reaches 13.6 which corresponds to the value expected in the sputtered particles fluxcalculated with the TRIM/SRIM software [26] for the bombardment of the Zr/Y target by 300 eV argon ion (x sputt =13.6).

In "compound" mode deposition, the x parameter of the coating is lower (x 3.5 sccm =9.7) than in oxygen free deposition expressing a lowered yttrium content. This value is in agreement with the x parameter calculated from the measured target composition (x target =9.9).

As the standard free energy of formation of yttrium oxide (-1206 kJ.mol -1 @323K) is lower than that of zirconium oxide (-1037 kJ.mol -1 @323K) [START_REF] Lide | CRC Handbook of Chemistry and Physics[END_REF], the yttrium atoms will be preferentially oxidized compared to zirconium atoms. The sputtering yield being lower for yttria than for metallic yttrium, in presence of oxygen, the particle flux sputtered from the target contains less yttrium atoms, whereas the zirconium quantity remains almost constant. This results in lowering of yttrium content in the film.

When the target is fully oxidized (in "compound" mode), oxidized species (ZrO and YO) are sputtered [19].

Their sputtering yields are weak and close to each other: the composition in the metallic element of the target is thus kept in the film.

It is important to note that, whatever the oxygen flow rate used, the composition of films is in the range 8YSZ-12YSZ for which the theoretical ionic conduction of cubic YSZ is optimal [3].

Regarding the oxidation level (Figure 7b), the films are found totally metallic in the absence of oxygen. They become fully oxidized for a oxygen flow rate above 2 sccm. At 1 sccm, the oxygen amount adsorbed at the surface of the growing film is low and a 30% lack of oxygen is measured.

Figure 8 includes X-ray diffraction diagrams in -2 geometry of thin films deposited in "metal" mode. The film in "compound" mode was not thick enough to obtain a usable signal in this XRD configuration.

When the deposition process occurs in pure argon atmosphere, only two sharp peaks at 30.9° and 33.9° are detectable. They can be attributed respectively to the ( 0 1 10 ) and (0002) diffraction planes of an hexagonal solid solution of yttrium in zirconium (Zr:Y). The film presents a strong (0002) preferred orientation. In the case of substitution solid solution, the Vegard's rule [START_REF] Vegard | [END_REF] allows to calculate the yttrium content in the solid solution Zr:Y from the position of diffraction peaks. Pure zirconium (JCPDS #05-0665) and yttrium (JCPDS # both crystallize in the close-packed hexagonal arrangement and form a complete solid solution [29]. Taking into account the lattice parameters of the pure metals [29], Vegard's rule gives a composition of 76/24 (x 0sccm_XRD =13.6) which is what has been measured on the deposited film by chemical analysis (EDS, RBS). The mean grain size determined by the Scherrer formula from the (0002) XRD peak is estimated at about 50 nm.

For 1 sccm of oxygen, only one peak (33.8°) remains on the diffraction diagram. Furthermore, its intensity is 400 times lower than the peak observed at this angle for the film synthesized without oxygen, and its integral width is 16 times larger: the film presents a lower ordered structure. This peak can be indexed either as the (0002) plane of the Zr:Y solid solution, or as the (002) plane of the tetragonal zirconia (JCPDS #42-1164). Due to its large width and the absence of any other peaks, it is not possible to determine which phase is present; both of them could co-exist.

For 2 sccm of oxygen, the diffraction pattern is completely different. The four well-marked peaks (30.0°, 34.9°, 50.0° and 59.5°) can be attributed to respectively (111), ( 200), ( 220), (311) planes of the substitution solid solution that can be formed between cubic zirconia (JCPDS #49-1642) and cubic yttria (JCPDS #43-0661). From the lattice parameter evolution as a function of the yttria content [30], the Vegard's rule for the solid solution ZrO 2 :Y 2 O 3 allows to deduce that the film is composed of 11% yttria doped zirconia (x 2sccm_XRD =11), which again is in agreement with results from chemical analysis (EDS, RBS) of the films.

The elementary composition of the films was determined by 3 independent methods: (1) calculation estimation from the knowledge of the sputtering process; (2) direct measurements by coupling analysis techniques (NRA, RBS and EDS); (3) estimation by the Vegard's rule from XRD measurement. It is important to note that for the three studied samples, the three methods lead to the same value of metallic element (Zr and Y) content.

The raise of 32% of the deposition rate observed in "metal" mode (Figure 5) can be compared with the 28% increase of the molar volume from the Zr:Y metallic solid solution to the 11YSZ oxide solid solution due to the incorporation of oxygen atoms in the Zr:Y lattice.

The oxygen flow rate is a very important parameter to monitor the properties of YSZ deposited by magnetron sputtering from a metallic target: it should be high enough to oxidize all the metallic sputtered particles reaching the substrate and to stabilize a well-ordered crystalline oxide phase but it must not exceed the value at the transition in order to avoid entering the "compound" regime where the deposition rate is very low. In the intermediate range (at 1 sccm), oxygen incorporated in the film impedes the formation of an ordered solid solution and does not allow the crystallization of a stable oxide phase.

The film deposited on silicon wafer with a oxygen flow rate of 2 sccm fulfills to all the requirements for SOFC electrolyte application [3]: the composition is in the range 8YSZ-12YSZ (11YSZ); the material is crystallized in the cubic phase; no porosity was detected by SEM observation (the electrolyte must be gastight); the deposition rate allows the synthesis of a 5 µm thick layer in a reasonable time (20 min).

Coating on commercial anode

In the optimized experimental conditions determined on silicon, a 1 μm thick coating was achieved on a real commercial anode. This substrate is a NiO-YSZ composite which has a surface pores of about 1 μm in diameter and a marked roughness (R max = 2 μm). This anode is composed of two layers: (1) a 200 μm thick porous holder layer for the mechanical strength of the anode and the good flow of gasescalled ACC (anode current collector)and (2) a denser layer (porosity: 5%)called AFL (anode functional layer)with a higher nickel loading for a better catalysis of the hydrogen oxidation, and a better conduction of released electrons. The first use of the anode in high temperature reducing atmosphere leads to the reduction of NiO into Ni. It leads to a gaseous release of H 2 O and a volume reduction of NiO grains. These two phenomena may be a damaging source for the electrolyte layer.

The behavior of the YSZ film deposited on this anode must be tested in reducing, but also oxidizing atmosphere.

Indeed, in the operating conditions, deposited side of the coating undergoes reducing atmosphere, whereas open side is in contact with air atmosphere.

Thin film analysis first shows that the thickness of the film deposited on the anode is the same as that on silicon.

The cross-section structures are also preserved: dense columns are present on the rough substrate that follows the morphology of the surface. The angular-shaped grains already evidenced on silicon (Figure 6) are also observed on the NiO-YSZ substrate (Figure 9). EDS and XRD analyses show that the elemental composition and crystallinity of the film are the same as for Si supported film. This indicates that the sticking coefficient of the metallic elements is quite the same on both substrates. Moreover, the oxidizing process taking place on the substrate surface is not significantly modified by the morphology or nature of the substrate.

On large-scale SEM images (not shown here), one can note that the porosity of the substrate is locked by the film. Moreover, as reported by Wang [31] and Coddet [32] on AFL coated anode substrate, no obvious intercolumn porosity is evidenced.

Annealing in air was performed at 600°C for 90 min. Neither peelings nor cracks of the coating were observed. The surface of the film is slightly smoother than before annealing. A sintering of the columns took place inducing densification of the film (Figure 9). Neither modification of elemental composition nor of the crystallinity are observed.

Annealing in hydrogen (5% H 2 in Ar) was performed at 700°C for 24 h. The reduction of NiO grains of the substrate led to the increase of the porosity in the "active" layer of the anode. The behavior of the film during H 2 treatment is rather the same as for air treatment, although it might be more stressful due to NiO volume reduction and H 2 O outgassing. Nevertheless, neither cracks nor peelings are detected and the morphology is unchanged.

All the above observations prove that YSZ films synthesized by reactive magnetron sputtering in optimum deposition conditions (2 sccm oxygen flow rate) would be good candidates for SOFC electrolyte layer. To check this assumption, electrochemical tests have been performed on film deposited onto a NiO-YSZ anode.

Fuel cell tests

5 µm-thick YSZ electrolyte was deposited by magnetron sputtering on the H.C. Starck commercial NiO/YSZ anode in the optimal deposition conditions. The two SOFC configurations with the different cathodes (platinum and lanthanum strontium manganite) as described in the experimental section are represented in the Figure 2.

The 900°C electrolyte layer (Figure 10a) presents a classical compact columnar structure, the 1300°C annealing (Figure 10b) leads to a sintering of the columns to grains with the formation of closed porosity between the grains. Neither cracks nor crossing porosity are observed. These cells were evaluated by their I-V characteristics at 800°C and 850°C (Figure 11). Air was used at the cathode side, whereas hydrogen diluted in nitrogen is introduced at the anode side.

Whatever the tested configurations, the voltage measured in open circuit (OCV) is always close to 1 000 mV (Table 1). These values are slightly lower than the state-of-art (1 110 mV) probably due to tiny leak in the electrolyte. Nevertheless, it is high enough to test the behavior of the electrolyte under charge. The close values of OCV found for both (900 and 1300°C) annealing treatments indicate that they do not provide any enhancement of the gastightness of the electrolyte.

The efficiency of the cell was strongly dependent on the test temperature (Table 1). A rise of 50°C led to increase the maximum power (Pmax) and the current density (j@700mV) obtained at 700 mV by 50%. For Pt cathode, the performances of the cell are low at 850°C and no recordings were done at lower temperature. The use of LSM cathode enhanced the efficiency by doubling the maximum reached power (Pmax) and a corresponding fourfold increase in power density. This phenomenon is due to the increasing of the Three-Phase-Boundaries (TPB) number by changing the cathode: for platinum, they are located only at the point of contact between platinum and electrolyte whereas, for real LSM cathode the mixed porous LSM/YSZ layer allows to delocalize the reduction reaction all along the cathode surface in contact with the gas.

The area specific resistivity (ASR) deduced at 700mV for Pt cathode cell (Table 1) is too high for a use as SOFC. ASR is strongly promoted by the high temperature operation and become closer to the target value of 0.15 .cm -2 [5].

These results are slightly lower than those of Coddet et al. [32] with a YSZ magnetron sputtering SOFC: they obtained a current density of 800 mA.cm -2 at 700mV for a 6 µm thick YSZ electrolyte but with a even more efficient cathode made in LSC (lanthanum strontium colbaltite). Nevertheless, it can be compared with thin film electrolyte technologies SOFC (100-1000 mA.cm -2 ) but remains lower than SOFC obtained by traditional firing methods (1000-1400 mA.cm -2 ) and has efficiency close to commercial H.C. Starck ESC2 fuel cell (500 mA.cm -2 @700mV). On the other hand, Nédélec et al. [33] have reached a current density of 1750 mA.cm -2 and an ASR of 0.17 for a 2 µm DC magnetron sputtered YSZ. Also they used an improved LSCF cathode and add RF biasing of the substrate for getting denser electrolyte. All these improvement can allow better performances than reached in the present study.

Conclusion

Yttria stabilized zirconia coatings were obtained by reactive magnetron sputtering from a metallic target of Zr/Y in an argon/oxygen atmosphere. Plasma parameters and composition of the gas phase analyses show the presence of a transition from "metal" to "compound" sputtering mode for a flow rate of 2.5 sccm of oxygen.

Deposition on silicon shows the need to work in "metal" mode at a flow rate close to the transition to obtain a stoichiometric crystallized YSZ with very high deposition rates (> 10 µm.h -1 ) compared to those reported in literature. Although these deposits are columnar, they appear compact according to SEM observations. Coatings on NiO-YSZ anode commercial exhibit the same properties: in addition, the film follows the complexity of the substrate (roughness and porosity) to form a compact and covering layer. Annealing treatments on air or hydrogen atmosphere do not significantly alter the structure of the layers. SOFC complete cell were designed in commercial NiO-YSZ anode supported configuration: YSZ electrolyte was deposited by magnetron sputtering, whereas cathode (platinum or lanthanum strontium manganite) was screen painted. A LSM-based cathode is more efficient than a Pt one. All the configurations are gastight enough to be analyzed. At 850°C, the maximum power reaches 350 mW.cm -2 with an ASR of 0.6 .cm -2 which are comparable to commercial device.

Fig. 1 Fig. 4

 14 Fig. 1 Magnetron sputtering device Fig. 2 SOFC test device Fig. 3 (a) Target voltage and (b) chamber pressure versus the oxygen flow rate. Square [triangle] symbols correspond to an increasing [decreasing] oxygen flow rate. The full [empty] symbols correspond to the "metal" ["compound"] mode behavior. The dotted line on the pressure curve corresponds to the pressure without plasma Fig. 4 Influence of oxygen flow rate on the 750 nm argon line intensity (normalized by its maximum) and on the intensity ratio of each species; the full [empty] symbols correspond to an increasing [decreasing] oxygen flow rate.

Fig. 5 Fig. 6 Fig. 9 FiguresFig. 1

 5691 Fig. 5 Deposition rate according to the oxygen flow rate Fig. 6 Surface and cross-section morphology of the YSZ films according to oxygen flow rate Fig. 7 (a) Substitution rate (x) of xYSZ and (b) oxidation level () of deposited YSZ thin films versus oxygen flow rate Fig. 8 XRD pattern of thin films according to oxygen flow rate. 11YSZ (circle) , t-ZrO 2 (arrow), Zr:Y 76/24 solid solution (diamond) peaks position are reported Fig. 9 Surface and cross-section morphology of the coating on anode after deposition, after air annealing and after H 2 reduction Fig. 10 SEM cross section observation of SOFCs assemblies: (a) platinum cathode based cell and (b) LSM cathode based cell Fig. 11 SOFC test of a magnetron sputtering YSZ electrolyte: (a) measured voltage and (b) calculated power density in a function of current density

Fig. 2

 2 Fig. 2

  Fig. 5

  Fig. 10

Table Captions Table 1 OCV

 Captions1 , maximum power and area specific resistivity (ASR) and current density at 700mV of tested cells

	Tables
	Table 1
		OCV	Pmax	j@700mV	ASR@700mV
		[mV]	[mW.cm -2 ]	[mA.cm -2 ]	[.cm -2 ]
		800 °C	850 °C	800 °C	850 °C	800 °C	850 °C	800 °C	850 °C
	YSZ/Pt	-	977	-	120	-	113	-	2.06
	YSZ/LSM	985	995	239	350	267	404	1.31	0.62

Acknowledgements

The authors wish to acknowledge the "Région CENTRE" for financial support (APR ELECTROLYTE).