Ontology based data warehouses federation management system

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1 Ontolog based data warehouses federaton management sstem Naoual MOUHNI 1, Abderrafaa EL KALAY 2 1 Deartment of mathematcs and comuter scences, Unverst Cad Aad, Facult of scences and technologes Marrakesh, 40000, Morocco nmouhn@gmal.com 2 Deartment of mathematcs and comuter scences, Unverst Cad Aad, Facult of scences and technologes Marrakesh, 40000, Morocco elkala@hotmal.fr Abstract Data warehouses are nowadas an mortant comonent n ever comettve sstem, t's one of the man comonents on whch busness ntellgence s based. We can even sa that man comanes are clmbng to the net level and use a set of Data warehouses to rovde the comlete nformaton or t's generall due to fuson of two or man comanes. these Data warehouses can be heterogeneous and geograhcall searated, ths structure s what we call federaton, and even f the comonents are hscall searated, the are logcall seen as a sngle comonent. generall, these tems are heterogeneous whch make t dffcult to create the logcal federaton schema,and the eecuton of user queres a comlcated msson. In ths aer, we wll fll ths ga b roosng an etenson of an estent algorthm n order to treat dfferent schema tes (star, snow flack) ncludng the treatment of herarches dmenson usng ontolog Kewords: Data warehouse Federaton, Ontolog, Herarchcal dmenson, Schema Integraton.. 1. Introducton A Data Warehouse reresents the enterrse-wde "sngle source of truth" and cororate memor of all busness rocess data [3], t s "a subject orented, nonvolatle, ntegrated, tme varant collecton of data n suort of management's decsons." as defned b Bll Inmon n 1990, the father of data warehouses. In some cases, one data warehouse s not suffcent to rovde a comlete nformaton about a fact, whch makes groung multle data warehouses the onl soluton. e.g. n the contet of a hotel chan that s geograhcall dstrbuted n man countres, t ma have several heterogeneous warehouses to store and analse data about customers reservatons. ths set of warehouses s what we call " a data warehouse federaton". Federated data warehouses are dfferent than dstrbuted Data warehouses, n order that dstrbuted data warehouses can refer dfferent subjects and there s a strct rule n data dstrbuton (horzontal, vertcal...) whch make t eas to ntegrate the quer results b usng jon or sum oeratons [7]. In federated sstem, the user send hs quer wthout havng an dea about the locaton of data or ts structure, the set of data warehouses s seen as a whole and the result s the combnaton of data warehouses comonents results. The comonents n FDWS (Federated data warehouse sstem) can dffer n asects such as : data model, quer language and data semantc [9]. So, a FDWS must contans the followng elements [8] : An ntegraton rocedure of the schemas of the comonent warehouses gvng the logcal schema of the federaton. A quer language for user who does not need to know the schemas of the comonent warehouses. A rocedure whch enables decomoston of user queres to the federaton nto sub-queres whch are sent to the comonent warehouses the warehouse federaton sstem management s frst based on a logcal schema called the federaton schema, whch ntegrates all the comonents schemas. to create ths schema, we must ntegrate all the other local schemas, wthout loosng nformaton. Durng ths ntegraton, t ma be dffcult to decde keeng or not an nformaton b usng the rocedure shows n [8], whch comare ever measure to the one n the estng federaton schema, f t ests onl the locaton of ths measure whch s

2 characterzed b the coule D, '_ 0 b name s added, where : D 0 : reresent the fact table n the data warehouse. b'_ name : reresent the name of the measure, a new measure s added to the schema. An algorthm s mlemented to ntegrate dmensons attrbutes, resectng the same logc. In fact, ths algorthm resent ts lmts n case we have a measures or a dmenson attrbutes that refer to the same subject, and reresented b two dfferent terms n data warehouses local schemas and t doesn't treat the relatonsh that could been between attrbutes and the case of herarchcal dmensons. Our aroach consst of usng an alcaton ontolog defned n [10] as "a descrton of knowledge necessar to acheve a artcular task and that allows to use the same rogrammng language as the alcaton rogrammng language ", to fll ths ga nstead of usng onl Meta data that does not full reresent the semantc relatonsh between local schema measures and dmenson attrbutes, and those of the federaton schema. In ths artcle, we roose an ontolog based data warehouses federaton management sstem to solve the roblem of semantc heterogenet durng federaton schema creaton, based on hotel chan data warehouse sources. Moreover, n our knowledge, there s no studes that used ontolog n a federaton contet to solve ths roblem, whch justf our choce. Then n secton 2; we resent and analss n summar a set of related works. 2. Related works In all doman research, It s alwas worth consderng the others work, dscuss t and check f we can refne and etend t for our artcular urose. In comuter scences, reusng estng sources s one of the reasons that made the develoment of ths doman ossble. Warehouses federaton accordng to Sheth and Larson [9], and that aears n [7] and [4], s a set of data warehouses that are heterogeneous, autonomous and dsersed. Ever comonent can contnue ts local oeratons and at the same tme artcate n federaton. It's for the better that all the ntegraton oeratons be done wthout nterrutng the rocess of comonent data warehouses. There are no man studes on the data warehouse federaton, however, R. Kern, K. Rk, and Ngoc Thanh Nguen, roosed a framework for buldng logcal schema and quer decomoston n data warehouse federatons [7], the develoed an algorthm to ntegrate comonent schemas nto one global logcal federaton schema. But ths algorthm resents some lmts n order to treat the case of warehouses wth star schema onl, and t doesn't consder the herarchcal dmensons and all the heterogenet tes, whch are descrbed n [9] as the dfference n structure, where dfferent data models rovdes two dfferent structural rmtves. then, dfferences n constrants,dfferences n quer languages and semantc heterogenet. Semantc heterogenet, s one of the bggest roblem that faces nformaton ntegraton nowadas, t occurs when two snonm terms from two dfferent sources descrbe the same subject [1] ( e.g: schedule and tmetable are snonms but we have to show t to the sstem). one of the solutons to fll ths ga s usng ontolog, whch s accordng to [6] " ontolog s a formal elct descrton of concets n a doman of dscourse (classes (sometmes called concets)), roertes of each concet descrbng varous features and attrbutes of the concet (slots (sometmes called roles or roertes)), and restrctons on slots (facets (sometmes called role restrctons)). An ontolog together wth a set of ndvdual nstances of classes consttutes a knowledge base. In realt, there s a fne lne where the ontolog ends and the knowledge base begns." Accordng to ther use, we dstngush man tes of ontologes, Generc Ontolog, Doman ontolog, Alcaton ontolog, Reresentaton ontolog, The ontolog of methods, tasks and resoluton of roblems, Lght ontolog and rch ontolog[2]. Even f usng ontolog ma resolve the heterogenet roblem n federated data warehouses, t s not et used n ths contet, and all the solutons roosed are based on Meta data reostores, whch solve the roblem of structure defnton but not the semantc ssues. 3. Our contrbuton 3.1 Presentaton of the soluton Our work s an etenson to[7] algorthm to create the global logcal federaton schema. R. Kern, K. Rk, and N. Nguen, roosed an algorthm of ntegraton of comonent schemas nto a federated logcal schema. The assume that all warehouses are wth

3 star schema, so the do not deal wth herarches n dmensons. In fact, even wth a star schema the herarch for dmenson are stored are stored n the dmensonal table tself. Whereas, n a snow flack schema, a dmenson table have more or more arent tables, and herarches are broken nto searate tables n snow flake schema. These herarches hels to drll down the data from tomost herarches to the lowermost herarches[5]. Our objectve s an mrovement of ths ntegraton algorthm to cover heterogeneous schemas (snow flack or star schema). And use ontolog as a tool to solve the semantc heterogenet roblem nstead of usng meta data onl. We roose a federaton data warehouse management sstem (FDWS), whch cover : Imroved algorthm for schemas ntegraton usng alcaton ontolog A quer analss and decomoston tool. An ontolog-based ntegraton Algorthm for quer results. 4. The quer results are ntegrated usng ontolog to solve the heterogenet roblem. 3.2 Integraton schema's algorthm In our case, the nut can be wth dfferent schemas tes ( star, snow flack), so to treat the dmenson herarches we roose the followng algorthm: Annotaton We use the same notaton as [7]. Inut. j P as the set of arents of a dmenson defned b j P,..., D1 D n H a Data warehouse schema defned as H D0, D1,..., D F an estng federaton schema defned b F D0, D1,..., Dm Outut. Fg. 1. Structure of the roosed Data warehouses federaton management sstem 1. Ever federaton comonent ma or not have ts own local alcaton ontolog, whch s wrtten n OWL language descrbng the semantc of ever attrbute and measure, and descrbe the relatonsh between tems and herarches of dmensons b usng s_a and arentof relatons. 2. Ths local ontologes are eorted to the logcal laer ontolog reostor, besdes that a meta data ml fle s loaded nto the federaton sstem to descrbe data structure. 3. The user quer s analzed b the FDWS, decomosed, eecuted on the selected comonents F the federaton after ntegraton wth H. Other notatons are used: a _ name : name of attrbute a b _ name : name of measure b D D : D s smlar to D (based on ontolog and meta data OR eert's decson) a a : a s smlar to a (based on ontolog and meta data OR eert's decson) b b : smlar measures (based on ontolog and meta data OR eert's decson) Recall of the Measure ntegraton algorthm. R. Kern, K. Rk, and N. Nguen n [7], defned a measure ntegraton algorthm as follow: For each measure from nut data warehouse tr to fnd corresondng measure n federaton schema. If such a measure ests n federaton schema add a mang between them. If none of the federaton measures corresonds to the current one add t to the federaton and make a mang between new measure and the current

4 one. Dmenson Integraton In ever teraton of the algorthm, the global schema s beng udated b ntegratng arents of dmensons, then ntegratng the dmenson t self. 1. For each dmenson from a comonent schema, usng ontolog, we etract the set of ths dmenson arents, ths set can be equal to or contans one or man tems. a. For each arents tem, we look for smlart n F, f t contans a smlar structure, we comare ts attrbutes wth the estng one, n case two attrbutes are smlar, we add a new locaton to the attrbutes nventor reresented b the foreach f coule D, a _ name,, we add the attrbute as a new one to the dmenson. In case that the attrbute doesn't est n the target dmenson, we add a new attrbute. b. After ntegratng all the dmenson arents, we ntegrate usng the same oeratons the dmenson t self. D n H, 1,2,..., P foreach dmenson D n f D F : D D t t P foreach attrbute a' nd f a Dt : a a' a s characterzed b a _ name, lst lst lst D, a'_ name Dt Dt a'_ name, D, a '_ name D t foreach a'' n D Dt Dt a''_ name, D, a''_ name F F D t f D F : D D t t foreach attrbute a ' nd f a Dt : a a' a s characterzed b a _ name, lst lst lst D, '_ a name D '_, t Dt a name D, a '_ name Dt foreach a''' n D D '''_, t Dt a name D, a '''_ name F F D t 4.Eamle We consder that we have two data warehouses whch reresent the sources of our federaton sstem. The frst comonent s wth start schema, so herarches dmenson are reresented n dmenson tself. e.g. the herarch Countr Re gon Ct.

5 Fg. 2. A star schema of hotel reservatons The second comonent, s a snow flack schema reresentng Hotel reservatons. ths schema contans some herarches of dmensons. flack schema related to a reservaton management n a hotel chan. 1. We frst etract ontologes and metadata fles from dfferent nodes, n the ntegraton laer of the FDWS, then nclude new entres nto the global ontolog reostor. 2. Then we ntegrate fact tables b testng the estence of ths table n the global federaton schema, f t ests, we comare ts measures to the estng ones referrng to the ontolog reostor. 3. net ste s to ntegrate dmensons and herarches dmenson, e.g: we frst ntegrate the clent dmenson from DW1 nto the global schema, then when we tr to nclude Customer dmenson, wtch s a snonm of clent dmenson, so referrng to the ontolog reostor we don't add t as a new dmenson, and we comare ts attrbutes wth clents attrbutes. Based on arrentof relatonsh mentoned n ontolog fles, between Customer/clent and Categor and sub_categor we ntegrate ths herarch. 5. Imlementaton Fg. 3. A snow flack schema for hotel reservatons After alng the roosed ntegraton algorthm we get the global schema as follow: The ntegraton schema algorthm was mlemented usng Java API Jena, to manulate RDF language from java alcaton. We are usng two data warehouses; the frst one wth a star schema and has an ontolog wrtten n OWL/RDF, the second data warehouse wth a snow flack schema and has no local ontolog. Metadata fles and OWL/RDF fles are maed nto ml fle and transferred nto the network to the Federated data warehouses management sstem. 6. Concluson In ths artcle, we have resented a art of our data warehouses federaton management sstem. In artcular the rocess of creatng the federaton schema based on the ntegraton of local schemas usng alcaton ontolog. Whch makes ossble to treat the herarches of dmensons b analzng the arentof relatonshs, and make the t eas to automate the ntegraton rocess n federaton contet. Fg. 4. The result of comonents schemas ntegraton Let consder two data ware houses, the frst one (Fg2) wth a star schema and the second one (Fg3) s a snow References [1] D. AWAD, H. T., V. COURBOULAY, A. REVEL Ontolog-based Soluton for Data

6 Warehousng n Genetc Neurologcal Dsease Proceedng of the world congress on Engneerng [2] GAYO DIALLO, L. D. T. D. L. I., DE LA MODÉLISATION ET DE LA COGNITION Une Archtecture à Base d Ontologes our la Geston Unfée des Données Structurées et non Structurées. TIMC-IMAG UMR, [3] INMON, W. H The Oeratonal Data Store. [4] JINDAL, R., ACHARYA, A Federated Data Warehouse Archtecture. Whte aer, Wro Technologes. [5] LLC, P. B. S Learn data modelng [Onlne]. Avalable: htt:// [6] MCGUINNESS, N. F. N. A. D. L Ontolog Develoment 101: A Gude to Creatng Your Frst Ontolog. [7] R. KERN, K. R., N. NGUYEN A Framework for Buldng Logcal Schema and Quer Decomoston n Data Warehouse Federatons. Comutatonal Collectve Intellgence. Technologes and Alcatons, Lecture Notes n Comuter Scence Volume 6922, [8] R. KERN, T. S., N. NGUYEN A formal framework for quer decomoston and knowledge ntegraton n data warehouse federatons. Eert Sstems wth Alcatons, 40, [9] SHETH, A. P., LARSON, J.A. Setember Federated Database Sstems for Managng Dstrbuted, Heterogeneous, and Autonomous Databases. ACM Comutng Surves Vol. 22, No. 3. [10] VAN HEIJST, G., SCHREIBER, A. ET WIELINGA, B Usng elct ontologes n kbs develoment. Int. J. of Human-Comuter Studes, 46(2/3),