Towards a Service-Based Framework for Environmental Data Processing

Transcription

1 Towards a Servce-Based Framework for Envronmental Data Processng Ivan Madjarov AxMarselle Unversté, CNRS, ENSAM, Unversté de Toulon,LSIS UMR 796, 13397, Marselle, France Alexandra Grancharova BulgaranAcademy of Scences, Insttute of System Engneerng and Robotcs, P.O.Box 79, Sofa 1113, Bulgara Juš Kocjan Jozef Stefan Insttute, Department of Systems and Control, Jamova 39, 1000 Ljubljana, Slovena Unversty of NovaGorca, School of Engneerng and Management, Vpavska 13,5000 Nova Gorca, Slovena Bogdan Shshedjev TechncalUnversty of Sofa, Department Programmng and Computer Technologes, 1000 Sofa Bulgara Abstract Scentsts are confronted wth sgnfcant data management problems due to the huge volume and hgh complexty of envronmental data. An mportant aspect of envronmental data management s that data, needed for a process, are not always n the adequate format. In ths contrbuton, we analyze envronmental data structure, and model ths data usng a semantc-based method. Usng ths model, we desgn and mplement a framework based on Web servces for transformaton between massve envronmental text-based data and relatonal databases. We present a mappng model for envronmental data transformaton to be used n the scenaro devoted to the methodology for development of stochastc models for predcton of envronmental parameters by applcaton of Gaussan processes. Keywords Scentfc data; Envronmental data; Web servces; Data ntegraton; Stochastc model; Gaussan process; Metadata I. INTRODUCTION Nowadays, a sgnfcant part of a scentst's work s dedcated to accessng, vsualzng, ntegratng and analyzng data from a wde range of heterogeneous sources because scence s more and more data-drven. On the other hand, scentst's actvtes, scentfc nstruments and computer smulatons produce more and more data from dfferent doman, e.g. physcs, astronomy, meteorology, ar polluton and so on. Scentsts process these data and generate new data based on the results of the processes. Edtng and updatng of data also generates data. Produced data are schema-less, sem or fully structured persstng n dfferent repostores [5]. Accordng to some sources [], the data volumes are approxmately doublng each year. Furthermore, scentsts need to know based on whch collecton of data they have produced a specfc result. An mportant problem that arses here s the data provenance and the data versonng that can be expressed by the queston: What data n whch verson a specfc result was obtaned. So, data requre new methods of organzaton for scentfc analyss. It s obvous that scentsts need a data structurng and a storng organzaton for data management and processng. The exstng scentfc tools are mostly focused on data processng and vsualzaton, and data management s largely left to the user [3]. Many of scentfc data are tradtonally stored n ASCII format,.e. text fle. The ASCII text s a recognzed standard for data exchange (e.g. nput/output) supported by scentfc nstruments and smulaton devces. It s recognzed that ASCII-based data are platform ndependent, so they can be analyzed n dfferent operatng systems and they can be mported to whatever nformaton system or scentfc workflow. However, ths form presents some drawbacks: Low readablty: data can be presented n dfferent unts wthout any context-based explanaton and they become somewhere ambguous. Hard to ntegrate: scentfc data are natvely heterogeneous, unstructured and they are usually stored n dfferent fles and/or n dfferent locatons. Ths makes t dffcult to ntegrate all the data nto one place wthout a common sematc schema. Data searchng: content dscovery s a dffcult task n a large datasets or n thousands of dstrbuted fles. An mportant aspect of envronmental data management s that data, needed for a process, are not always n the adequate format. Scentsts use dfferent tools n dfferent stages of ther research; they develop some tools for ther work by themselves and spend tme to retrofttng data nto acceptable formats for these tools [4]. So, the man problem to address here s how to provde an effcent way to mplement massve data transformaton between texts and databases. Ths s a common problem for both computer scence researchers and envronmental scence researchers, aswe consder envronmental data as a subset of scentfc data. In sem-structured data, the nformaton that s normally assocated wth a schema s contaned wthn the data [3]. The 44 P a g e

2 meanng and logc structure of sem-structured data can be expressed and dentfed by semantc tags. For nstance, XML s a standardzed extended markup sem-structured data. In ths paper, we present our work n progress. We analyze envronmental data structure, and model ths data usng a semantc-based method. Usng ths method, we desgn and mplement a Web servce-based framework for transformaton between massve envronmental text-based data and relatonal databases. As man contrbuton, we present a mappng model for envronmental data transformaton. We apply ths model n a scenaro devoted to the methodology for development of stochastc models for predcton of envronmental parameters. We envson a schema for predcton of envronmental parameters by applcaton of Gaussan processes, e.g. the ozone concentraton n the ar based on data collected on-lne by automatc measurement statons. As well, we can easy apply the developed methodology to predct the concentratons of other ar pollutants e.g. sulfur doxde and ntrogen doxde. The paper s organzed as follows: frst n secton we present the background wth some related work. In secton 3, we present our motvaton and concept for an envronmental Web servces-based workflow. In sectons 4 and 5 we present our scenaro for envronmental data processng based on Gaussan processes. Fnally, n secton 6 we conclude and dscuss some future work. II. BACKGROUND AND RELATED WORK As presented n [3] scentfc utltes can fall nto three categores: (1) scentfc software; () scentfc languages and (3) scentfc workflows. In ths study we present a nonexhaustve lst of mature scentfc utltes.e. scentfc software, scentfc languages, workflows software and systems to justfy the choce that we wll do n our research project. A. Scentfc Software Scentfc software tools n general, load data n memory. Usually scentsts need to perform some extra steps n order to prepare data for processes. To use dfferent tools, scentsts must learn dfferent sets of commands, scrptng or programmng languages for dfferent framework and operatng systems. B. Scentfc Languages The Apache Hadoop [9] s an open-source software lbrary for storage and large-scale processng of data-sets on clusters. It s a framework that allows dstrbuted processng of large data sets across sngle servers or thousands of machnes by usng smple programmng models. As presented n [8], the Java open source lbrary s desgned to detect and handle falures at the applcaton layer, so delverng a hghly-avalable servce on top of a cluster. Google Open Refne (GOR) [6] s a standalone open source desktop applcaton for data cleanup and transformaton to other formats. It s smlar to spreadsheet applcatons; however, t behaves more lke a database. GOR opens a Web nterface powered by aweb server. It operates on rows of data whch have cells under columns, whch s smlar to relatonal database tables. Transformaton expressons are wrtten n the GOR Expresson Language. It s able to work wth CSV, TSV, XML, JSON, Excel and RDF formats. Matlab [7] s a numercal computng envronment and allows matrx manpulatons, plottng of functons and data, mplementaton of algorthms, creaton of user nterfaces. It mports data from CSV fles, excel spreadsheets and databases. Import functons read the data n memory and reorganze them n vectors or matrces, then all functons work on these data structures and possble nterfacng wth programs wrtten n other languages, ncludng C, C++, Java, and FORTRAN. C. Scentfc Workflows Workflow composton represents the conceptual model of a scentfc analyss whch mples the flow of data wthn a system. Every step of workflows acts on the data. The requred data are obtaned from prevous steps, from local fles, from relatonal databases, from remote servces or another source. Kepler [10] s a free scentfc workflow management system. It s able to acqure data from dfferent sources, process them by prepared or user defned components. Optonally, an external data processng faclty can be appled. Ths software provdes process and data montorng, provenance nformaton, and data movement solutons. Its archtecture s drected graphs where the nodes represent dscrete computatonal components and the edges represent paths along whch data and results can flow between components. In Kepler obtanng data from external sources lke CSV fles, spreadsheets, relatonal DBMSs and remote data sources are done by specfc actors as metaphors to model the steps of workflows. The system ncludes a graphcal user nterface for composng workflows. VsTrals [11] s an open-source scentfc workflow and provenance management system that provdes support for smulatons, data exploraton and vsualzaton [3]. The provenance nformaton s presented as XML fles or as tables n a relatonal database. It allows users to navgate workflow versons, to undo changes, to vsually compare workflows and ther results, and to examne the actons that led to a result. It allows the combnaton of loosely-coupled resources, specalzed lbrares, grd and Web servces. Taverna [1] s an open source scentfc Workflow management tool sute to desgn and execute workflows. It s able to fetch data from CSV and spreadsheet fles, local and remote resources through provded or custom servces. It provdes provenance functonaltes and a common model for workflows and means for sharng and reusng them across the borders of ndvdual workng groups. To leverage the exstng nfrastructure, the computatonal model strongly focuses on Web-servces. It provdes an API and a Web nterface to access data about varous Web servces. III. MOTIVATION AND CONCEPT FOR AN ENVIRONMENTAL WORKFLOW Scentfc Workflows present a managed combnaton of actvtes and computatons n order to resolve scentfc problems. In contrast to busness Workflows that mplement busness processes nvolvng dfferent actors and systems, scentfc workflows are used to realze computatonal experments, possbly confrmng or nvaldatng scentfc 45 P a g e

3 hypotheses. Scentfc Workflow systems mantan the executon of repettve tasks such as data access, transformaton and analyss [1, 4] data from heterogeneous sources, e.g. sensor systems, measurng nstruments, text fles, spreadsheets, databases, smulaton devces, etc. The creaton and exchange of scentfc and envronmental nformaton ncrease the amount of data that should be processed, from one hand, as well as the possbltes for ther nterpretaton, on the other hand. Ths motvates many researchers and specalsts to reconsder the exstng engneerng and network archtectures, the database schemas, the algorthms and rules for data nterpretaton. Besde the hug sze, the data are represented n a way, whch does not allow processng by the tradtonal DBMS, because of ther heterogenety and specfc characterstcs. Sensor systems are usually used to montor the state of the envronment n the urban areas. The obtaned measurements need to be stored n a database, whch s very mportant for the development of schema-based data models. So, the data collected by the sensors are used n real tme by dfferent applcatons through procedures for control of large amount of data n spatotemporal databases. The problem whch arses s related to the nformaton control, because of the specfc characterstcs of the collected data. The space-tme character of data requres the development of new approaches for structurng, explotaton and vsualzaton of these data. Sensor networks and assocated databases are used for montorng and regstraton of varous envronmental phenomena, e.g. for the accurate predcton of the future values of these phenomena and for all stochastc-based data processng for envronmental norm evaluaton. Specfc languages for scentfc data descrpton already exst. CDF and HDF are languages, whch are used n the physcs of thermonuclear synthess, the geology and the astronomy. They represent data models, API, and fle formats for storage and control of scentfc data. These formats allow storng data as a smple table that s dffcult to apply wth a large amount of data that have a complex structure. In our work we process envronmental data as a subset of scentfc data. However, a specfc language for descrpton of envronmental data doesn't really exst. Moreover, there s a large dversty of characterstcs propretary of envronmental data,.e. dfferent scales of measurement expressed n dfferent unts. We suggest the use or the extenson of a scentfc Workflow wth adapted semantcs for presentaton and storage of large amount of data, related to the montorng system that analyzes envronmental parameters. We argue for a semantc and Web servce-based approach for processng envronmental data from multple and heterogeneous sources. The study of envronmental data requres the use of protocols, mathematcal models and procedures, whch need to be valdated. In order to accomplsh ths, we rely on a Workflow scentfc process through ntegraton and control of the components, defnng the ar qualty n the envronment. The scentfc goal of our research work n progress s to study the complexty of the systems for envronmental montorng, whch use large amount of data. Fg. 1. General structure dagram of a scentfc workflow for envronmental data processng. We develop solutons n terms of semantc languages, models and methods for access, storage and use of scentfc and envronmental data, mplemented manly as Web nterfaces and servces. Our focus n ths area s geared towards the desgn and mplementaton of servce orented systems that allow a pay-as-to-go generaton of composte cloud-based servces accordng to the users requrements. In ths paper, we am at the development and ntegraton of technologes and expertse, necessary to resolve the problem wth the huge amount of envronmental data appled to stochastc models for envronmental parameter predcton by applcaton of Gaussan processes. In order to acheve ths goal, we rely on a Workflow-based scentfc process, drected towards the control of data flow (Fg. 1). The man goal ncludes the followng sub-goals: 1) Development of a data control strategy. We study the algorthms and the effcency of the Cloud-XaaS platform (Anythng-as-a-Servce) wth an emphass on the semantc structurng of acqured data from the nstruments n order to facltate the data ntegraton when heterogeneous sources are used.we develop servces for remote data control, assocated wth the data processng,.e.acquston, analyss, requests, actualzaton,computatons and vsualzatonas shown nfg. 1. ) Data storage. We develop a mult-layer model wth an automatc ndexng of data by usng the exstng servces wthn the Cloud-based platforms. We propose a natve data storage archtecture (NXD), whch s adapted to varous functons allowng the connecton wth other platforms. 3) Dstrbuton of the envronmental data. We develop a model for dgtal vsualzaton of envronmental data through a transformaton process for Web-based presentaton n terms of tables and/or vector graphcs. The envronmental data are transformed nto SVG, as an XML document, whch allows buldng applcatons for mmedate graphcal representaton of the prognoss on the user sde. The dgtal vsualzaton s assocated wth the latest advances n responsve desgn that takes nto account all partculartes of desktop and moble devces based on meda queres. 4) Development of mathematcal models for predcton of envronmental parameters. Ths ncludes the system 46 P a g e

4 ntegraton va Web servces of the modelng approach based on Gaussan processes wth data about the concentratons of ozone, sulfur doxde and ntrogen doxde n the ar, collected at the automatc measurement statons. 5) Metadata descrptons. Scentfc Workflow systems typcally descrbe data processng va a Workflow defnton language. However, current specfc Workflow defnton languages, even adopted by current mature scentfc Workflow systems, are too complex and excessve for non-professonals. We desgn an XML-based envronmental data defnton language usng schema descrptons to sut a lghtweght workflow system n a specfc doman such as ar qualty. 6) Data ntegraton. Notable characterstcs of scentfc computng are data ntegraton, data manpulatons durng calculaton, scentfc analyss, data mgraton and the data store on dstrbuted machnes accordng to gudelnes and logcal relatons [8]. We assert that Web Servces can be used to unlock heterogeneous scentfc systems to extract and ntegrate envronmental data. There are two ssues n usng a scentfc Workflow approach to predcton modelng: The frst one s the choce of a Workflow composton and executon envronment. The second ssue adapts the process steps n an envronmental data management sute. We recommend the second ssue because t can be assocated to Web Servce technology. It s necessary to recall that the Web Servce paradgm enables the aggregaton of multple data sources. In ths approach, each process step s mplemented as a Web Servce and Web Servces are chaned together to form a modelng task as shown n Fg.. In the core of Web Servce technology s the Web Servces Descrpton Language (WSDL) [13]. WSDL provdes a XML-based framework and language for defnng nterfaces e.g. nput and output, SOAP access specfcaton (Smple Object Access Protocol) [14] and the locaton of the servce. Ths approach can acheve greater system nteroperablty wth exstng scentfc Workflows. Fg.. Integrated platform for envronmental data processng wth envronmental metadata descrpton, Web Servces Management for data and applcaton ntegraton wth an envronmental Workflow. IV. ENVIRONMENTAL DATA METADATA DESCRIPTION AND PROCESSING A. Envronmental Data Need a MetadataDescrpton In general, to be able to process scentfc and envronmental data t s mportant to know ther meanng, e.g. what t s about, how they was obtaned, how they are formatted and so on. Ths nformaton s coded and stored as data about the real data,.e. an underlyng defnton or descrpton. The formal descrptons are useful to record meanngful nformaton about the data, ther provenance and ther codng n order to be understood by other users. So, we generate metadata as data that descrbe other data wth some common characterstcs: The metadata summarze basc nformaton about data, whch can make fndng and workng wth partcular nstances of data easer, or to locate a specfc set of data by flterng through metadata. Metadata for scentfc and envronmental data contan descrptons of the content, as well as keywords lnked to the content. These are usually expressed n the form of meta-tags. The meta-tags are the vocabulary of metadata and they are often evaluated by search engnes to help decde of data relevance. The metadata nformaton s to be used n automated data processng by standard procedures,.e. the procedures have to understand metadata and to process data accordng to metadata descrpton. Metadata can be created manually, or by automated nformaton processng. There are a lot of research works n the metadata doman as descrbed n [4].Some of them try to defne a formal language able to descrbe a wdest set of data. Organzaton such as OMG[3] developed standard models and languages such as CWM and UML. On the bass of CWM several metadata models for busness applcaton were developed n []. The man dffculty to address here s the data heterogenety, the varety of ther applcatons and the wde range of specalzed languages used for ther descrpton. The natve heterogenety specfc to envronmental data requres a meta-descrpton that takes nto account the dfference n sze, the dfference n measurement scale, the dfference n context or provenance. In ths study, we fnd that languages mentoned so far do not appear to be entrely satsfactory. Therefore we recommend more approprate envronmental data semantcs to be defned. B. Metadata Types and Models In our research work for the descrpton of envronmental data we defne dfferent types (levels) of metadata: 1) Orgn:ths data descrbe the ownershp of each pece of data, the place where t s stored, the organzaton and/or the person responsble for ts mantenance. ) Access rght:ths data descrbe rghts to read, wrte or process data by someone. 47 P a g e

5 3) Processng:the data about specal routnes or/and algorthms for processng a pece of data. Fg. 4. Canoncal form of our envronmental data processng model Fg. 3. Envronmental data model presented n three sectons wth two reference classes that tell the mappng between both models presented as XML schema,.e. (1) General; () Semantcs; (3) Layout. 4) Formattng:ths data descrbe how data are recorded and stored; are they numercal and n what unt of measurement are they wrtten. 5) Namng and Meanng: ths data descrbe data about the namespace of every pece of data, ther meanng descrbed by the language of the knowledge doman and the data provenance. Fg. 3 shows our concept of XML schema for metadata descrpton. Ths choce s argued by the dfferences between busness data and scentfc data as descrbed below: Most scentfc data s numercal and float especally n domans wth strong mathematcal background as physcs, chemstry and engneerng. The datasets concernng one source are huge. The orgn and access metadata values are dentcal for whole datasets. They do not dffer from value to value as n the case of busness data. Most of scentfc data are multdmensonal tables. C. Relatonal Model of Scentfc Data In our work we propose a scentfc-envronmental dataset as a smple relatonal database. So, the metadata was devsed n three parts (sectons). 1) General: metadata about the orgn and access rghts. Ths part contans also a general descrpton of the data and references to specfc procedures used to process them. ) Semantcs: contans elements descrbng the meanng of the data fle. The man hypothess was that most envronmental data can be presented as one or as few tables contanng two types of quanttes: (1) ndependent and () dependent quanttes. Ths way they can be examned as a relatonal table wth a prmary key consstng of ndependent quanttes and the dependent quanttes as non-key attrbutes. There are other data named parameters that are common for the whole dataset and characterze the envronment of the experment or the assumptons of the smulaton as shown n Fg. 3. 3) Layout:descrbes the formattng and the structure of the raw data. In Fg. 4 our envronmental data processng model s presented. The dea behnd s to convert envronmental data to the structure of the developed semantcs model named canoncal or standard form. By ths approach t becomes easer to develop assocated Web servces for envronmental data processng. Instead developng M N dfferent Web servces processng M dfferent data structures to N results we can produce M transformatons (automatc) to standard form and N Web servces. The converson s done accordng the metadescrpton of data and Web servces defned n the canoncal descrpton shown n Fg. 3. The proposed soluton serves as a modelng language for expermental and measured data from dfferent envronmental sources and captures, especally appled to predct the concentratons of ar pollutants n an nspected regon. 48 P a g e

6 V. ENVIRONMENTAL DATA PROCESSING BASED ON GAUSSIAN PROCESSES Ths secton s devoted to the methodology for development of stochastc models for predcton of envronmental parameters by applcaton of Gaussan processes. It represents the core of the data processng block n the structural dagram, shown n Fg. 1. The Gaussan process (GP) model s a probablstc, non-parametrc black-box model based on the prncples of Bayesan probablty. The output of the GP model s a random varable wthnormal dstrbuton, expressed n terms of the mean and the varance. The mean value represents the most lkely output and the varance can be nterpreted as a measure of ts confdence. The obtaned varance, whch depends on the amount and the qualty of the avalable dentfcaton data, s mportant nformaton when t comes to dstngushng the GP models from other computatonal ntellgence methods. Because of ther propertes GP models are especally sutable for uncertan processes modellng or when modellng data are unrelable, nosy or mssng. In ths respect, GP models ft well for envronmental systemmodellng. Its use and propertes for modellng are revewed n [15]. The use of Gaussan processes for dynamc system modellng s a relatvely recent development [16, 17, 18]. A retrospectve revew of dynamc system modelng wth Gaussan process models can be found n [19]. A Gaussan process s a collecton of random varables whch have a jont multvarate Gaussan dstrbuton (Fg. 5). Assumng a relatonshp of the form y f( x ) between an D X R and output R Y, we have (1), (),..., ( )~ (0, ) nput y y y M N Σ, where pq Cov( y( p), y( q)) C( x( p), x ( q)) gves the covarance between the output ponts y( p ) and yq ( ) correspondng to the nput ponts x( p) and x ( q). Thus, the mean ( x ) (usually assumed to be zero) and the covarance functon C( x( p), x ( q)) fully specfy the Gaussan process. Note that the covarance functon C( x( p), x ( q)) can be any functon wth the property that t generates a postve defnte covarance matrx. A common choce s: Consder a set of MD-dmensonal nput vectors X [ x1, x,..., x ] T M and a vector of output data y [ y1, y,..., y ] T M. Based on the data ( Xy, ), and gven a new nput vector x, we wsh to estmate the probablty dstrbuton of the correspondng output y. Unlke other models, there s no model parameter determnaton as such, wthn a fxed model structure. Wth ths model, most of the effort conssts n tunng the parameters of the covarance functon. Ths s done by maxmzng the log-lkelhood of the parameters, whch s computatonally relatvely demandng snce the nverse of the data covarance matrx (M M) has to be calculated at every teraton. The descrbed approach can be easly utlzed for regresson calculaton. Based on a tranng set X, a covarance matrx K of sze M M s determned. As already mentoned before, the am s to estmate the probablty dstrbuton of the correspondng output y at some new nput vector x. For a new test nput x, the predctve dstrbuton of the correspondng output s y x,( X, y ) and s Gaussan, wth mean and varance: T 1 ( x ) k( x ) K y ( x ) k ( x ) k( x ) K k( x ) T 1 0 where k( x ) [ C( x1, x ),..., C( x, )] T M x s the M 1 vector of covarance between the test and tranng cases and k ( ) C(, ) 0 x x x s the covarance between the test nput and tself. The dentfed model, n addton to mean value, also provdes nformaton about the confdence n predcton by the varance. Usually, the predcton confdence s depcted wth nterval whch s about 95% confdence nterval. D 1 C( x( p), x( q)) v1exp w ( x ( p) x ( q)) v0 pq 1 where Θ [ w1,..., wd, v0, v1 ] are the "hyper-parameters" of the covarance functon, x denotes the -th component ofthe D-dmensonal nput vectorx, and s the Kronecker pq operator. The covarance functon (1) s composed of two parts: the Gaussan covarance functon for the modelng of system functon and the covarance functon for the modellng of nose. The nose s usually presumed to be whte. Other forms of covarance functons sutable for dfferent applcatons can be found n [15]. For a gven problem, the hyper-parameters are learned (dentfed) usng the data at hand. After the learnng, one can use the w parameters as ndcators of how mportant the correspondng nput components (dmensons) are: f w s zero or near zero t means that the nputs n dmenson contan lttle nformaton and could possbly be removed. Fg. 5. Modellng wth GP - Gaussan dstrbuton of predctons at new ponts x, x and x, condtoned on the tranng ponts (.) P a g e

7 M 1 1 [ yˆ y ] LD log( ) log( ) M 1 Fg. 6. Usng GP models - n addton to the predcton mean value (full lne), we obtan a 95% confdence regon (dotted lnes) for the underlyng functon f. Ths confdence regon can be seen n the example n Fg. 6as band bounded by dotted lnes. It hghlghts areas of the nput space where the predcton qualty s poor, due to the lack of data or nosy data, by ndcatng a wder confdence band around the predcted mean. Gaussan processes can, lke neural networks, be used to model statc non-lneartes and can therefore be used for modelng dynamc systems [16, 18] as well as tme seres f lagged samples of output sgnals are fed back and used as regressors. For the envronmental parameter dynamcs modellng we consder representaton where the output at the step k depends on the delayed outputs y: y( k) f ( y( k 1), y( k ),..., y( k n)) ( k) where ( k) s whte nose and the output yk ( ) depends on the vector [ y( k 1), y( k ),..., y( k n)] T. Assumng the sgnal s known up to the step k, we wsh to predct the system output h steps ahead,.e., we need to fnd the predctve dstrbuton of y( k h) correspondng to x ( k h). Multplestep-ahead predctons of a system modeled by eq. (3) can be acheved by teratvely makng repeated one-step-ahead predctons, up to the desred horzon [16, 18]. The qualty of the mean values predcted by a Gaussan process model can be assessed by computng the average squared error (ASE) [15]: 1 ASE y y M [ ˆ ] M 1 where y and ˆ y are the output predcton and the output measurement at the -th step. Addtonally, the qualty of the predcton varance can be assessed wth the logarthm of the predctve densty error (LD) [15]: where are the predcton at the -th step. The descrbed methodology for development of GP models for envronmental parameter predcton has been already appled to predct the ozone concentraton n the ar of Bourgas cty, based on data collected on-lne by the automatc measurement statons [0, 1]. Ths methodology can be easly appled to predct the concentratons of other ar pollutants lke sulfur doxde and ntrogen doxde n some of the most ar polluted ndustral ctes n Bulgara (Plovdv, Stara Zagora, Varna, Bourgas). VI. CONCLUSION In ths paper we proposed a concept of the framework for envronmental data processng and stochastc models for predcton of envronmental parameters. We analyzed envronmental data structure, and modeled ths data usng a semantc-based method. Usng ths model, we desgned and mplemented a framework based on Web servces for transformaton between massve envronmental text-based data and relatonal databases. We presented a mappng model for envronmental data transformaton to be used by applcaton of Gaussan processes. For future work we emphasze for envronmental rsk management and data provenance lnked to gas emssons and polluton of ar n ndustralzed ctes. REFERENCES [1] Joost N. Kok, Anna-Lena Lamprecht, and Mark D. Wlknson, Tools n Scentfc Workflow Composton, T. Margara and B. Steffen (Eds.): ISoLA 010, Part I, LNCS 6415, pp , Sprnger, 010. [] Sh Feng, Je Song, Xuhu Ba, Dalng Wang, and Ge Yu, A Web-Based Transformaton System for Massve Scentfc Data, L. Feng et al. (Eds.): WISE Workshops, LNCS 456, pp , Sprnger, 006. [3] Javad Chamanara, Brgtta Köng-Res, ScQL: A Query Language for Unfed Scentfc Data Processng and Management, In: PIKM 1, Mau, Hawa, USA, 01. [4] P. Prabhu, T. B. Jabln, A. Raman, Y. Zhang, J. Huang, H. Km, N. P. Johnson, F. Lu, S. Ghosh, S. Beard, T. Oh, M. Zoufaly, D. Walker, D. I. August. A Survey of the Practce of Computatonal Scence. In ACM, edtor, State of the Practce Reports, pp. 19:1 19:1, ACM Press, 011. [5] A. Alamak, V. Kantere, and D. Dash. Managng scentfc data. Commun. ACM, 53(6):68 78, 010. [6] Google Refne. [7] Matlab: The Language of Techncal Computng. matlab/. [8] Gaozhao Chen, Shaochun Wu, Rongrong Gu, Yongquan Xu, Lngyu Xu, Yunwen Ge, Cucu Song, Data Prefetchng for Scentfc Workflow Based on Hadoop, Computer and Informaton Scence 01, Studes n Computatonal Intellgence Volume 49, pp 81-9, Sprnger, 01. [9] Apache Hadoop, [10] The Kepler Project. [11] VsTrals. [1] Taverna Workflow Management System. [13] W3C, WSDL, [14] W3C, SOAP, 50 P a g e

8 [15] C. E. Rasmussen and C. K. I. Wllams. Gaussan processes for machne learnng, MIT Press, Cambrdge, MA, London, 006. [16] K. Ažman, J. Kocjan. Applcaton of Gaussan processes for black-box modellng of bosystems. ISA Transactons, Vol. 46, No 4, pp , 007. [17] A. Grancharova, J. Kocjan, and T. A. Johansen. Explct stochastc predctve control of combuston plants based on Gaussan process models. Automatca, vol. 44, No. 6, pp , 008. [18] J. Kocjan, A. Grard, B. Banko, R. Murray-Smth. Dynamc systems dentfcaton wth Gaussan processes. Mathematcal and Computer Modellng of Dynamc Systems, vol. 11, No. 4, pp , 005. [19] J. Kocjan, Dynamc GP models: an overvew and recent developments, ASM'1 Proceedngs of the 6th nternatonal conference on Appled Mathematcs, Smulaton, Modellng, Pages 38-43, 01. [0] D. Peteln, J. Kocjan, and A. Grancharova. On-lne Gaussan process model for the predcton of the ozone concentraton n the ar. Proceedngs of BAS, vol.64, No.1, pp , 011. [1] D. Peteln, A. Grancharova, and J. Kocjan. Evolvng Gaussan process models for predcton of ozone concentraton n the ar. Smulaton Modellng Practce and Theory, vol.33, pp.68-80, 013. [] Davd Marco, Mchael Jennngs, Unversal MetaData Models, ISBN , Wley 004. [3] OMG Specfcatons, [4] A. Alamak, V. Kantere, D. Dash, Managng Scentfc Data, Communcatons of the ACM, vol. 53, 6, pp.68-78, P a g e