An Intellgent Context Interpreter based on XML Schema Mappng Been-Chan Chen Dept. of Computer Scence and Informaton Engneerng Natonal Unversty of Tanan, Tanan, Tawan, R. O. C. e-mal: bcchen@mal.nutn.edu.tw Shang-Y He Dept. of Computer Scence and Informaton Engneerng Natonal Unversty of Tanan, Tanan, Tawan, R. O. C. e-mal: tkuchrs@hotmal.com Abstract Context-aware computng s one of the attractve research topcs n pervasve computng. Context-aware systems can react to users preferences accordng to context ncludng locaton, tme and other envronment condtons. Context s generated by context nterpreters or aggregated by context aggregators from the sgnals of sensors. A tradtonal context nterpreter s usually bult as an executable hard code called wdget. It s dffcult for the system manager to construct and mantan the large collectons of context. In ths paper, we propose a ntellgent generc context nterpreter usng context scrpts to overcome the hard code dependency between context and hardware devces. The generc context nterpreter mports sensor data from sensor devces as an XML schema. Then, the schema matchng approach s used to help system manager generatng context scrpts nstead of wdgets easly. The system was bult and evaluated by dfferent sensor schemas. The results show that the schema matchng algorthm can match correct sensor types effectvely and provde effcent context generaton and mantenance. Keywords-context-aware system; context nterpreter; schema matchng; ntellgent system I. INTRODUCTION A context-aware system s a moble envronment n whch applcatons can dscover and make use of context nformaton ncludng user locaton, tme, date, nearby devces and other envronmental actvtes to adapt ther operatons and behavor [5]. A number of context-aware archtectures were proposed and employed for a wde spectrum of systems and applcatons [3]. However, snce each ndvdual system focuses on ts specfc applcaton doman, current context-aware systems are heterogeneous n all aspects, such as hardware, moble resources, operatng systems, applcaton software, and platforms [15]. The serous heterogeneous characterstcs of context-aware computng are especally mportant and become sgnfcant drawbacks whle developng context nterpreters for buldng context-aware servces [4][14]. In general, context-aware systems use context to archve the obectvty of controllng servces. Context s generated by context nterpreters or aggregated by context aggregators from the sgnals of sensor devces n the moble envronment. A tradtonal context nterpreter s usually bult as a type of executon codes called wdget [8]. Snce a wdget s usually desgned by system programmers as a hard code to translate sensors data nto a semantc representaton called context, t s dependent upon the sensor devces and the applcaton domans of context-aware systems. It s hard to mantan f some sensors were upgraded or renewed, and t s dffcult for the system manager to construct new servces for new devces. The extendblty of the systems thus wll be restrcted and lack of flexblty. In ths paper, we propose a ntellgent generc context nterpreter based on XML context scrpts generator and XML schema matchng schemes. The proposed generc context nterpreter archtecture conssts of two modules: the context scrpt generator and the generc scrpt nterpreter. We use context scrpts to replace hard code wdgets for solvng the dependency problem between context and hardware devces. The context scrpt generator mports sensor data from sensor devces as an XML schema. Then, the schema matchng scheme s appled to help the system manager generatng context scrpts nstead of wdgets easly. The generc scrpt nterpreter can translate varous context scrpts nto the correspondng contexts used n the applcaton. The generc nterpreter was mplemented and evaluated by varous sensors schemas. The results show that the ntellgent context scrpt generator can effectvely recognze correct sensor types by the support of schema matchng scheme and the context nterpreter provdes effcent context generaton and mantenance. Ths paper s organzed as follows. In Secton 2, we ntroduce the framework of context-aware systems.. The generc context nterpreter s descrbed n Secton 3. Secton 4 presents the schema matchng method for a ntellgent context nterpreter. The results of experments and evaluaton are shown n Secton 5. Fnally, concluson s made n Secton 6. II. FRAMEWORK OF CONTEXT-AWARE SYSTEMS The conceptual framework of context-aware systems conssts of fve layers as shown n Fg. 1 [4]. The contents of each layer are descrbed as follows brefly. Applcaton layer Storage layer Context layer Interpretaton layer Devce layer Fgure 1 The fve-layer conceptual framework.
1) The devce layer: Ths layer contans the operatng physcal devces used n the context-aware systems ncludng sensors, dentfers, moble devces, and actuators, etc. 2) The nterpretaton layer: Ths layer descrbes the semantc mappng between the devce layer and the context layer contanng context nterpreter and context aggregator. Context nterpreter: The raw sgnals from sensors or moble devces cannot work as ther orgnal format. They have to be transformed to context n context-aware system. The context nterpreter s used to nterpret the structures of raw data and represent the nformaton as low-level context called sensor context. Context aggregator: The context aggregator then gathers the related low-level sensor context data to form a hgher-level context. 3) The context layer: Context processng s the core of a context-aware system. Context nformaton s generated and managed n ths layer. Context model s used to descrbe the nteractve actvtes of the resource layer. Effectve context extracton and effcent context management are the two man functons of managng context. 4) The storage layer: The storage layer stores not only the context data of the current status but also the hstorcal context data n the context-aware system. The context data produced n the context layer are used to provde the servces of applcatons n the applcaton layer. To easly access context data, an effectve context database s requred. The context data access mechansm generally ncludes the storage of context schema and context query. 5) The applcaton layer: In ths layer, applcaton can be defned and executed by queryng the current status of context and the related hstorcal context data from the context database n the storage layer. Snce the contents of context-awareness are accessed by context queres from context databases, the varous applcatons can be constructed under dverse applcatons. III. THE GENERIC CONTEXT INTERPRETER The archtecture of the proposed generc context nterpreter [4] s shown n Fg. 2. The man components consst of the context scrpt generator and the generc scrpt nterpreter. The context scrpt generator further contans three functons modules: the context mappng operators, the schema matchng algorthm, and the schema mappng hstory. The components are explaned as follows. Sensor data schema: Sensor schemas are provded by some technques of connectvty standards, for example, UPnP and SOAP, whch enable data transfer n XMLbased procedure call. Each type of sensor delvers ts sensor data by the predefned XML schema accordng to the hardware specfcaton. Context model: The context model s bult for dfferent applcaton envronments. For mappng context schemas nto sensor data schemas, ontology wth XML-based representaton s used for constructng the context model of the system. Sensor data schema Context Mappng Edtor Schema Matchng Context model Context scrpt generator: Interpreter generator produces nterpretaton scrpts whch contan the mappng relatonshps between context schema and sensor data schema. The mappng tool s the context mappng edtor whch supports a graphcal user nterface to assst users to buld the relatonshp between context schemas and sensor data schemas. The mappng operaton can be fnshed automatcally by applyng the schema matchng module. Interpretaton scrpts: The nterpretaton scrpt uses XSL (extensble Stylesheet Language) to descrbe the schema mappng. The content of the scrpt s the rules of transformng source data schemas (sensor data) nto target data schemas (contex. Generc scrpt nterpreter: Snce nterpretaton scrpts are represented n XSL, the XSLT (XSL Transformaton) Processor can be used to be the generc nterpreter drectly. The XSLT-Processor wll read the sensor raw data wth XML tag and nterpret the context accordng to the correspondng nterpretaton scrpt. In ths archtecture, the context nterpreters generated by the context nterpreter generator are represented as context scrpts nstead of hard codes as wdgets. The nterpretaton scrpt draws lnkng relatonshps and transformng methods between sensor data and the semantcs n the context model. The generc scrpt nterpreter then translates sensor raw data nto context by the correspondng context scrpts whle the context nterpretaton process beng proceeded. IV. Mappng Hstory Context Scrpt Generator Interpretaton Scrpts Fgure 2. The archtecture of generc context nterpreter. THE SCHEMA MATCHING Sensor XML data Generc Scrpt Interpreter Context A. Notaton of Symbols In ths paper, the schema matchng scheme s mportant for fndng a correct sensor type for context mappng. Wthout the automatc schema matchng, users wll mss the prevous context nterpretaton of sensors and t further causes the problem of nconsstency on context nterpretaton. We apply several schema matchng approaches to the system. The Smlarty Yeld Matcher (SYM) [7] s ntroduced here..
We frst ntroduce and defne the symbols used n the SYM approach. S : Source schema. s : an element node n source schema S. T : Target schema. t : an element node n target schema T. Lsm(s, : the lngustc smlarty between nodes s and t. DTcom(s, : the data type compatblty between s and t. Ssm(s, : the structure smlarty between rooted at s and t. Wsm(s, : the weghted smlarty between rooted at s and t. th hgh : the threshold for ncreasng Wsm(s,. th low : the threshold for decreasng Wsm(s,. th accept : the threshold for acceptance of vald mappng. C nc : the multplcatve factor for ncreasng Wsm(s,. C dec : the multplcatve factor for decreasng Wsm(s,. W struct : the weght of structure smlarty for Wsm(s, t ). leaves( : the set of leaves n the subtree rooted at s. level( : the depth of node s, the depth of root s 1. StrongLnk(s, : the weght smlarty Wsm(s, th accept. The man smlarty measure of matchng schemas n SYM s the weghted smlarty Wsm. For any two element nodes s and t belongng two schemas S and T, respectvely, the computaton of Wsm(s, contans two phases: the lngustc matchng and the schema structure matchng. The goal of the lngustc matchng phase s to fnd the lngustc smlarty Lsm(s, between two element nodes s and t. In the schema structure matchng phase, we frst compute the compatblty of data type DTcom(s, and the structure smlarty Ssm(s,. Then, the weghted smlarty Wsm(s, s measured by combnng Lsm(s, wth DTcom(s, or Ssm(s,. The detaled matchng algorthm s descrbed n the followng subsectons. B. Lngustc Matchng The matchng of element names n schemas s frst step for most of the schema matchng methods. A good name matcher can dentfy correct lngustc matchng of element names effectvely. An accurate name matchng also helps to accomplsh the element-level matchng problem. However, a sngle name matcher wth smple smlarty measure cannot perform effectve matchng results n general. Here, we proposed a lngustc matchng method based on four name matchers: Levenshten, 3-grams, Jaro-dstance and WordNet. The computaton of lngustc smlarty for Levenshten, 3- grams, Jaro-dstance are lsted n [1]. For two strngs s and t, the smlarty values between s and t are denoted as sm lev,(s,, sm tr (s,, and sm Jaro (s,, respectvely. WordNet was developed by Mller et al.[18]. The relatons of words lke, hyponyms, synonym and antonym are also computed by the levels of semantc herarchy n the groups of words called synsets. Two words s and t are frst stemmed. Then, the smlarty of two words s computed by the depth of the dfferent Lexcon s herarches [13], as follows: depth1 depth depth2 depth, (1) depth1 depth2 Dstance 2 2, (2) sm Wordnet ( s, 1- Dstance where depth s the common parental depth, depth 1 s the depth of s and depth 2 s the depth of t from the root of the herarchy. Although we ust ntroduce the four dfferent name matchers, they are usually not used at the same tme whle computng the lngustc smlarty Lsm(s, of element names s and t. One of the reasons not usng all name matchers s that these name matchers nclude mplct smlarty herarchy of each other. For nstance, the 3-grams matcher has a hgh smlarty when the Levenshten matcher gets hgh smlarty. Another reason s to reduce the computaton tme. Snce the more matchers are operated, more computaton cost s needed. Especally, WordNet spends a lot of tme on searchng dctonary for computng the smlarty n ther synsets. It s not a great dea to often execute WordNet. Hence, a decson tree of combnng name matchers [10] s desgned. The decson steps are lsted as follows: Step 1. For two nput element names s and t, we check whether the two strngs are the same or not. If they are dentcal, the lngustc smlarty s set to be one and the processng s halt; otherwse, the above name matchers are used to compute the lngustc smlarty as the next step. Step 2. The sm edt (s, are frst computed and tested. Then, one of the followng three cases wll happen: 1) If the value sm edt (s, s larger than 0.55, the lngustc smlarty s set to the value sm edt (s,. 2) If the value sm edt (s, s less than 0.25, WordNet s used and the lngustc smlarty s set to the value sm wordnet (s,. 3) If the value sm edt (s, s between 0.25 and 0.5, the 3-gram and Jaro-dstance matchers are used as Step 3. Step 3. The average value of sm tr (s, and sm aro (s, are computed. If the average of the two matchers s larger than 0.15, the lngustc smlarty s set to the average; otherwse, WordNet s fnally used and the lngustc smlarty s set to the value sm wordnet (s,. Snce WordNet matcher s tme-consumng, t wll not be started untl no proper lngustc smlarty s produced by the other matchers. C. Schema Structure Matchng The goal of the schema structure matchng phase s to fnd the weghted smlarty Wsm(s, between two nodes s and t belongng to schema S and T, respectvely. The weghted smlarty s calculated by a combnaton of the lngustc smlarty (Lsm) and the structure smlarty (Ssm). Wthout loss generalty, we suppose that a node n a schema would be an nternal node or a leaf node. Hence, the structure matchng n schemas may perform on two leaf nodes, two nternal nodes and one leaf node vs. one nternal node. We regard matchng two leaf nodes as the leafstructure smlarty matchng. By contrast, the nonleafstructure matchng ncludes the cases of matchng two nternal nodes and matchng one leaf wth one nternal node.
The leaf-structure smlarty matchng Snce the nodes s and t are leaves (element name n ths case, there s no tree structure on s and t. The structure smlarty, Ssm(s,, consders the data types of the element names as the measure of ther structures. We make use of the type defntons on W3C Schema [15] and the data type converson of W3C XQuery [16] to construct a data type compatblty table for every data type. In the compatblty table, f the data type of the node s cannot be converted nto the data type of the node t, the data type compatblty between s and t, DTCom(s,, s set to be 0.1. If the data types of the nodes s and t can be converted each other, the value of DTCom(s, s 0.5. If the data type converson of the nodes s and t depends on the source values, DTCom(s, s set to be 0.3. In ths case, snce the structure smlarty Ssm(s, s measured by the data type compatblty DTCom(s,, the weghted smlarty of the leaf-structure smlarty matchng s defned as Wsm(s, = 0.5 DTcom(s, + 0.5 Lsm(s,. (3) The nonleaf-structure smlarty matchng If one of the matchng nodes s and t s an nternal node or both of them are nternal nodes, the tree structures rooted at s and t need to be further consdered. The structure smlarty of the two nodes s and t s measured by the weghted smlarty of nodes n the sets of leaves( and leaves( rooted at s and t, respectvely. The structure smlarty, Ssm(s,, n the nonleaf-structure smlarty matchng s defned as Ssm( s, where ( x, y ) {( x, y leaves( 1 ) Wsm( x, y for ( y' ) {( y' ) for ) leaves( 1 leaves( leaves( x leaves (, y satsfyng max( StrongLnk Wsm( y' ), (4) leaves ( ( x, y y' leaves(, x' leaves( ))}, satsfyng max(stronglnk( y' ))}; and StrongLnk(s, s the matchng par that the weghted smlarty Wsm(s, s larger than the threshold th accept. The weghted smlarty of nonleaf-structure smlarty matchng s defned to be the combnaton of the structure smlarty Ssm(s, and the lngustc smlarty Lsm(s,, as follows: Wsm(s, = W struct Ssm(s, + (1-W struct ) Lsm(s,, (5) where 0 W struct 1. The algorthm of matchng two schema structures starts tree matchng process from the element names (leaf node of the schemas. The value of Wsm(s, n the leaf-structure smlarty matchng s frst computed by equaton (3) for all leaf nodes of the source schema and the target schema. Then, the values of Wsm(s, for the nonleaf nodes at the upper level are computed by StrongLnk(leaves(, leaves() of the leaves rooted at s and t. The larger value of Wsm(s, s performed, the stronger structure smlarty of the nodes s and t are resulted. On the contrary, f the value of Wsm(s, s small, the structure smlarty of the nodes s and t s weak. Snce the values of Wsm(s, for the nodes at upper levels of schemas are stll computed by the leaves of subtrees rooted at s and t, we have to make use of adustng the values of Wsm on the leaf nodes to reflect the current structure smlarty of nonleaf nodes. Two crtera, th hgh and th low, are used to strengthen and weaken, respectvely, the values of weghted smlarty on leaf nodes. If the value of Wsm(s, for two nonleaf nodes s larger than th hgh, all of the values of weghted smlarty Wsm(leaves(, leaves() are ncreased by multplyng the multplcatve factor C nc. On the other hand, when the value of Wsm(s, for two nonleaf nodes s less than th low, all of the values of weghted smlarty Wsm(leaves(, leaves() are decreased by multplyng the multplcatve factor C dec. The multplcatve factors C nc and C dec are set as follows: 1 C nc = 1 3[ level( level( ], s S, t T; (6) 1 C dec = 1 2 [ level( level( ], s S, t T. (7) The detaled schema tree matchng algorthm s lsted as n Fg. 3. The postorder sequence s used for matchng nodes from the leaves to the upper levels. 1. Sub Schema_Tree_Match(SourceTree S, TargetTree T) 2. S = post-order(s), T = post-order(t) 3. for each s n S 4. for each t n T 5. f (s, t are leave then 6. set DTcom(s, = Datatype-Compatblty(s, 7. Wsm(s, = 0.5 DTcom(s, + 0.5 Lsm(s, 8. end f 9. f (s, t are non-leaf node then 10. compute Ssm(s, = structural-smlarty(s, 11. Wsm(s,=W struct Ssm(s, + (1-W struct ) Lsm(s, 12. end f 13. f (Wsm(s, th hgh ) then 14. ncrease-weghtedsmlarty(leaves(,leaves(,c nc ) 15. end f 16. f (Wsm(s, th low ) then 17. decrease-weghted-smlarty(leaves(,leaves(,c dec ) 18. end f 19. end for 20. end for 21. End Sub Fgure 3. The schema tree matchng algorthm.
D. Schema Smlarty After the computaton of all values of Wsm(s, for the source schema S and the target schema T, the match pars of the two schemas are generated by the Wsm(s, of the leaf nodes s n S and t n T. The match pars generatng algorthm s descrbed as follows: Step 1. Consder the matchng from S to T. For each leaf node s n S, the matched node t n T must satsfy the condtons of maxmzng Wsm(s, and Wsm(s, th accept. Let the set of selected match pars be S T. Step 2. Consder the matchng from T to S. For each leaf node t n T, the matched node s n S must also satsfy the condtons of maxmzng Wsm(s, and Wsm(s, th accept. Let the set of selected match pars be T S. Step 3. The fnal match results are the match pars n the set S T T S. After generatng the matchng pars, the schema smlarty between two schemas S and T s defned as ss, tt Wsm matched ( s, ScSm( S, T ). (8) leaves( S) V. EXPERIMENTAL RESULTS The generc context nterpreter proposed n ths paper used Mapforce API to develop the context mappng n the context mappng edtor. The ntal blank system needs buld context mappng manually. Once more mappng datasets were accumulated n the mappng hstory, Users wll be able to refer to the exstng schema mappng cases and a smlar sensor schema mappng was selected to modfy as a new context mappng. The test schema sets nclude seven dfferent sensor schemas lsted n Table I. The depths of schema structures are four levels. The number of leaves s between the range four and sx. The number of nodes s n the range of seven to ten. We frst ranked the smlarty degree of each schema by experts as shown n Table II. Then the proposed schema matchng algorthms, Cupd [11] and COMA++ [2][8] are tested on each schema. To evaluate the schema matchng performance of rankng, we refer to R norm [11] values as the crteron of effectveness. The matchng results of smlarty are evaluated and ranked, as shown n Table III. TABLE I. THE STRUCTURE UINFORMATION OF SENSOR SCHEMAS. Schema Leaves Nodes Depth GPSData (GPS) 5 9 4 HumdtyData (Humd) 5 9 4 IRData (IR) 5 9 4 LghtData (Lgh 5 9 4 RFIDData (RFID) 5 8 4 SensorData (Sensor) 6 10 4 Temp2Data (Temp2) 4 7 4 The two methods SYM and SYM-Dct represents the proposed schema matchng algorthms. The dfference between them s that SYM-Dct used the full algorthm of the four lngustc matchers, whereas SYM sets the Lsm(s, to be 0.4 nstead of usng WordNet searchng and smlarty computng. The expermental results show that the SYM leads n four schemas and COMA++ s generally superor to others n three schemas. The R norm values of COMA++ are better than Cupd for 5 schemas except S 5 :RFID and S 6 :Sensor. The reason s that the type of value(xs:decmal) n S 7 :Temp matched the type of value(xs:strng) n S 5 and S 6. Ths mstake causes the hgher rank of S 7 :Temp. It shows that COMA++ s relatvely weak n the matchng of types on leaves. The SYM-Dct s surprsngly the worst n the four methods. The man reason s that the matchng of WordNet dd not perform proper lngustc smlarty matchng results. The WordNet may gve relatve hgh smlarty for two dfferent terms. On the contrary, the SYM drectly sets the values as 0.4 s a good smlarty snce the lngustc smlarty s generally less than 0.5 f the three lngustc matchers are not used. The average R norm values show that the SYM has the best rankng. The COMA++ combnes dfferent matchers and gans the second place. Generally speakng, SYM-Dct and Cupd s not recommended to be used n ths applcaton. VI. CONCLUSION The man contrbuton of ths paper s to propose an ntellgent context nterpreter to get context ndependence. A context nterpretaton scrpt s proposed to replace hard codebased context nterpreter. We desgn a generc context nterpreter consstng of the context scrpt generator and the generc scrpt nterpreter. We also desgn a context edtng tool for support the context mappng operaton and devces mantenance. By employng schema matchng approaches, the generc context nterpreter performs a more ntellgent operatng nterface for users. The heterogenety n pervasve context-aware computng wll gan a graceful soluton. The proposed schema matchng methods also show ther effectveness on the matchng rank. The SYM method s superor to SYM-Dct, Cupd and COMA++. The heterogenety s a seres problem whle developng and extendng context-aware applcatons n an envronment of pervasve computng. Ths work s ntended as a startng pont of further nvestgatng on context-aware computng. The problems of context management for context-aware computng wll be pad more attenton n the future. ACKNOWLEDGMENT Ths research was supported n part by the Natonal Scence Councl of Tawan, R.O.C. under contract NSC 98-2221-E-024-012. REFERENCES [1] A. Algergawy, E. Schallehn, G. Saake, A Sequence-based Ontology Matchng Approach, n Proceedngs of 18th European Conference on Artfcal Intellgence Workshops, pp 26-30, Patras, Greece, July, 2008.
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