The Hybrid Probabilistic Query Algorithms Based on Inconsistent Database

Transcription

1 TELKOMNIKA Indonesan Journal of Electrcal Engneerng Vol. 2, No. 9, September 204, pp. 692 ~ 693 DOI: 0.59/telomna.v The Hybrd Probablstc Algorthms Based on Inconsstent Database Gao Hongyu Shandong Wan Je medcal college, Shandong Zbo, Chna E-mal: @qq.com Abstract Integrty constrant s mportant to mae data certan n relaton database. Though there s plenty of uncertan nformaton that s valuable and need to be searched and to be used. Combned wth probablstc database theory and on the bass of summarzng former results, ths paper gves a new query plan amng at nconsstent database. It uses the constrant methods ncludng unon, product, subtracton, selecton, proecton and ln to repar nconsstent data effectvely. Its probablstc calculaton wth four elements and probablstc query rewrtng can overcome shortcomngs of nconsstent databases. The experments show these methods can decrease conflct of data. Keywords: nconsstent databases, probablstc data model, data clean, query rewrtng, query of constrants Copyrght 204 Insttute of Advanced Engneerng and Scence. All rghts reserved.. Introducton As s nown to all, the magc weapon that a relatonal database rules the database feld endurng s descrptve query language SQL and ts basc relatonal algebra operaton. Relatonal algebra s one of the characterstcs of clear query semantcs, another s used to support query optmzaton, and ts advantage comes from the smple and clear data model - the relatonshp, whch has a sound mathematcal bass and system transformaton rules, and wth uncertanty nformaton as the research obect, wth ntegrty constrants, such as the entty ntegrty, doman ntegrty, referental ntegrty, and user-defned ntegrty, these constrants effectvely to ensure the ntegrty and effectveness of the data n a relatonal database. The data conform to the real world entty rules, n favor of the formaton and query of data. Non-unform database, n a nutshell s the transgresson of the database data ntegrty constrant, whch contan volate ntegrty constrants n data and uncertan data. The real world there s many don't comply wth the requrements for data ntegrty, the content of uncertan nformaton, such as maret predcton, fuzzy analyss and so on. It wll need to establsh a can handle data ntegrty, the content database model uncertanty, n terms of dealng wth uncertan nformaton, have put forward a varety of probablstc relaton database model to deal wth uncertanty nformaton. Usng probablty theory [] proposed relatonal schema extensons, usng a probablty of NF relatonal vew. Usng probablty theory, semantc redefnes the proecton operaton selecton and connecton, s one of the earlest put forward extenson probablty relatons lterature, but ncomplete scalng problem; Lterature [2] gves a lmted to the sum of all the probablty of tuples probablty relatonshp model whch equal to, but case s not complete consderaton of the sum of probablty s less than. Lteratures [3, 4] proposes a probablty data for relatonal database model, to further mprove the probablty of the sum s less than, whch put forward four tuples (that s, the probablty of obect propertes, stat c propertes, dynamc propertes, attrbutes) to solve the ey attrbutes of the statc and dynamc propertes of probablty problems, whch greatly expand the relatonal database's capabltes to deal wth uncertan nformaton, but lttle consderaton to the nconsstency data; Lteratures [5, 6] manly defnes the basc concept of database consstency and non-unform data query some methods, such as data cleanng, thnng the consstency of database management s a strategy data cleanng, dentfy and correct errors n the data and restore the database to a consstent state, and puts forward the man methods to deal wth nconsstent data such as consstent query, modfy, query rewrtng and so on, but these nformaton s the man research Receved September 8, 203; Revsed Apr 24, 204; Accepted May 7, 204

2 6922 ISSN: obect or determnstc data, do not apply to the uncertan data. Lteratures [7-9] from the angle of probablty and statstcs to deal wth uncertan nformaton, and puts forward the concept of complete defnton and the correspondng query rewrte the relatonshp. Consderng the operaton of the obect manly s the statc property, the operatng obect of another attrbute s only made a smple prompt dynamc property. Lteratures [0, ] from XML sem-structured angle dscuss the probablty of the query method of non-unformty of database operatons whch have mportant reference value. But wth the rapd development of Internet, the networ has qucly become an mportant means of nformaton dssemnaton and exchange. Especally on the Web, has a very rch source of data, most of these data s change, some of them are regular changes, some of them are rregular changes, or changes n the law s uncertan. Lteratures [2, 3] For regular change can tae advantage of the relatonal database, and for a large number of rregular data s unable to use SQL queres The accurate data for the producton of large numbers of these rregular does not always mantan the consstency of a relatonal database, data conflcts tend to, but they stll have use value of nformaton, not smply dscarded. But some nformaton s not accurate, t volates the prncple of relatonshp database consstency, therefore, to fnd out an effectve method to the consstency of nconsstent nformaton nto nformaton, and can perform the correspondng accurate query becomes the ey to solve the problem. Partcle swarm optmzaton and ant colony algorthm as the current group of ntellgence research felds n the two man algorthms, rely on s a probablty search algorthm/ As a nd of evolutonary computaton method, partcle swarm optmzaton (PSO) algorthm s to smulate the behavor of the brds feed on, based on a seres of teratve optmzaton algorthm, the frst ntalzed to a group of random solutons, through the teratve search for the optmal soluton, has the followng characterstcs: wth a mult-pont search capabltes, to establsh balance between dversty and centralzed search. And ant colony algorthm s also n recent years, the brth of the stochastc optmzaton method, manly through the nformaton transmsson between ant colony and acheve the goal of optmzaton and ts advantages: one s to have postve feedbac mechansm, through the pheromone update the query optmal path; 2 t s general-purpose stochastc optmzaton method, t s not a smple smulaton of real ants, but s more ntegrated nto people's ntellgence; Three s a global optmzaton method, not only solve the sngle obectve optmzaton problem, and can solve the mult-obectve optmzaton problem. So combnng wth the probablty problem, we conduct exact solutons usng PSO and ant colony algorthm. Loong forward to get wn-wn n terms of optmzaton performance s to do complementary advantages n tme performance. Compared wth other query optmzaton algorthm, whch has the followng advantages: ) Because ndvdual fault affects to the whole problem solvng, wthout centralzed control constrants do not ensure that the system has stronger robustness; 2) In a drect way of communcaton to ensure the scalablty of the system, due to the ncrease of the ndvduals n the system and ncrease the communcaton overhead s less; 3) Three parallel dstrbuted arthmetc model, can fully use multple processors, such dstrbuted more sutable for the worng state of the networ envronment; 4) The contnuty of 4 for problem defnton wthout specal requrements; 5) ablty of each ndvdual n the system s very smple, every ndvdual executon tme s short, and the algorthm mplementaton s easy. In ths paper, the uncertanty of nconsstent database nformaton s proposed to the determnstc algorthm. n ths artcle about the probablty and the consstency of database operatons research as the foundaton, t s by ncreasng the probablty constrant condtons. The accurate extracton of data element characterstcs, expand the T-SQL relatonal expresson's ablty to process data, not only can mae the uncertan nformaton nto certan nformaton, and adopt the method of probablstc query to query rewrte of nconsstency data, helpng the promptness and accuracy of the query. 2. Problem Descrptons 2.. Problem Defnton Defnton (Consstency): A pattern å R has a set of database ntegrty constrants on the set n the lterature [5]. If the pattern of a database nstance on the R and R satsfy å, TELKOMNIKA Vol. 2, No. 9, September 204:

3 TELKOMNIKA ISSN: whch called nstance r s consstency, remember to r = å ; Otherwse s consstent, remember to r ¹ å. Defnton 2 (the nconsstent database) : r s an example of the database D, for any ntegrty constrants on the database D (IC), f $ r = IC, D s consstency database, otherwse s consstent. Defnton 3 (short, the dfference between): There are two database nstance r and r ' on a model of R. Gven a set of database query constrant set å, database on the å s consstent. The dfference between R and R ' s: D ( r, r ')( = r Ä ')( r = ')( r - ') r È r - r ¹ null Defnton 4 (query ntegrty constrant): Entty rules correspondng to the pattern cannot be created on the R ntegrty constrants å. A gven query Q must satsfy the å on R, says R = QIC, otherwse R ¹ QIC. Real world enttes correspondng to the model, as may be nconsstent data ntegrty constrants n volaton of the patterns, so cannot be created, but n the query results must satsfy these constrants that gven, wth all the tuples satsfy the query selecton condtons, QIC to get consstent results agan set to flter the results [4]. Defned 5 (repar) There s a set of ntegrty constrants on a pattern R collecton å. r s an nstance on the R. R ' s R a subset meet of å n R. If R ' satsfy the followng condtons: ) R ' = å 2) There s no another nstance r ' = å, and meet the D( r, r ')( Ì ',) D r r. Suggests that R ' s a meet the largest subset of å on the model R, called R ' s a repar of R. Defnton 6 (query rewrte) : R s a relaton. å s a set of constrants that I s an nstance on the R. q s a query Boolean value on the R. For å n every nstance I have å ( q,) I = true, says I satsfy the consstency on R. The query value q satsfy the query condtons, otherwse the å ( q,) I = false. I does not meet the consstency on the R. q query faled n values, namely, gven a SQL query q and a set of ey constrants å, wll rewrte the SQL query q nto another Q. ndependent of the data and wor for every nconsstent database. Such as, Y ( x, x,...,) x = select * from T 2 where f ( x, x,...,) x 2 Among, 2 f ( x, x,...,)() x =...() R Ù A = x Ù A = x. c. Q c can return consstent results. The rewrte s Defnton 7 (consstent query): R s a gven model, a set of IC set å and a database nstance on the r. r can be consstent, n volaton of å some ntegrty constrants n the IC, a repar R ' s a subset of the R. At R, R ' meet å n IC, the dstance R ' and I must be mnmzed. Repar s not the only commonly, each repar for the consstency of database (IDB) may be meet å. An example of a gven r, for every repar on the on r = Q[ t], the tuple t s consstent. R '. A query Q meet on r So a tuple on the probablstc relatonal schema s nto the ey propertes, statc propertes, dynamc propertes, probablty. Its descrpton s no longer a smple obect n tradtonal relatonal data model, but a dynamc obect (ncludng dynamc obect tself and the dynamc propertes) of an event wth the probablty of the event to occur. Formally stll expressed wth two-dmensonal table, t essentally descrbes probablty of a certan event occurs, the dfferent lne descrbes a dfferent event and probablty, are not allowed to appear on the same events n the same relatonshp [5-6]. The Hybrd Probablstc Algorthms Based on Inconsstent Database (Gao Hongyu)

4 6924 ISSN: Formal Presentaton As shown n Table and Table 2, f you want to fnd the networ course grade of name Bo Lu, or fnd onlne courses whose score s 97 ponts, s unable to provde accurate nformaton, because Bo Lu networ currculum may s 97, t s also possble s 62, or the networ course grade may be Bo Lu 97, whch s also may be WenMu. Table. Student Selecton Course Lst Student d Name Courses Probablty s Bolu Networ 0.8 s Yangmu Englsh 0.6 s Bolu Operatng system 0.2 s Wenmu Networ 0.4 Table 2. Cause Result Table Course code Course Grade Probablty t 0028 Networ t2 006 Englsh t Networ t4 004 Englsh Table Student d s as the ey attrbutes, name s a statc property, course s dynamc propertes. The probablty of 0.8 sad the probablty of student d selecton networ course s 0.8, Fgure s the use of concse tree to show ths relatonshp accordng to Table and 2. Fgure. Course Selecton and Score Probablty Tree Table 3. Probablty Table of Englsh Grade Course code Course Grade Probablty 006 Englsh Englsh Englsh null 0. TELKOMNIKA Vol. 2, No. 9, September 204:

5 TELKOMNIKA ISSN: Methods of Probablstc and Algorthms 3.. Partcle Swarm Optmzaton (PSO) Algorthm () Partcle Swarm Optmzaton Partcle swarm optmzaton algorthm (PSO) s a nd of evolutonary computaton technology comes from the study of brds feed on behavor and nventon. Frst put forward by Dr Barnhart and Dr Kennedy n 995 [7]. In the process of brds feed on, every brd can be regard as a partcle, every brd, accordng to two factors determne the feedng path tself before a brd s taen by the optmal path and the other one s the optmal path through the other companon [8]. Mathematcally, assume that the search space s d dmenson, whch X = ( x, x,...,) x s the frst partcle n the dmenson of D vector. t t t t 2 d X t can be seen as a potentally vable solutons. By the changes of the partcle velocty, the optmal soluton s realzed by the dmenson D bad to search. The frst of the partcle's velocty s defned as: t t t t t t t t V = ( v, v 2,...,) vd. P = ( p, p 2,...,) pd represented partcles wthn the number of teratons to obtan the optmal value n teratons, sad all the partcles wthn the number of teratons to obtan the optmal value. Practce through the optmzaton value determned by the problem of ftness value to evaluate the partcle from "bad" to good. Each partcle by the above two extreme value constantly update themselves, so as to produce a new generaton of groups [9]. Partcles wll be subect to the followng formula to update ther speed and poston: v = wv + c rand()()()() p - x + c Rand p - x () t t- t t t t d d d d 2 gd gd x + = x + v t t t d d d (2) Whch c, c2 are constant and are called learnng factor; rand() and Rand() are the random Numbers on [0, ], w s for the nerta weght (nerta weght). Formula s composed of three parts, part that partcle velocty, llustrates the partcle current status; Part 2 s the cogntve part (whch the modal), mean the partcles themselves to thn; Part 3 s the socety part (socal modal). Three parts together determnes the space search ablty of partcle. Part has the ablty to balance the global and local search. Part 2 mae the partcles have enough strong global search ablty, avod the local mnmum. Part 3 shows the nformaton sharng between partcles. In the three parts of the partcles together determne the effectvely reach the best poston under the acton [20]. In addton, the partcles constantly adust ther poston accordng to speed, but also s lmted by a maxmum speed V max. When V more than V max, t wll be lmted to V max. 3.. Ant Colony Algorthm T alternatve set, T2 alternatve set, T3 alternatve set, T4 alternatve set Fgure 2. The Optmzaton Selecton Probablty of the XML Node Resources Based on Ant Colony Algorthm Usng ant colony algorthm to solve the XML query optmzaton selecton problem, whch s shown n Fgure 2. In the fgure, ovals represent tas T correspondng alternatve The Hybrd Probablstc Algorthms Based on Inconsstent Database (Gao Hongyu)

6 6926 ISSN: resource set, sold round represent alternatve path correspondng to the node. To solve problems from each alternatve resources concentraton optons out of a resource node, mae all choose the path of mnmum cost, get the results of the optmzaton. If each node resources as ants clmb over a node, the problem solvng process can be transformed to how to accordng to the T, T 2,..., Tn order to select a path, whch maes the ants clmb over the path through the largest total probablty, and compared the number of mnmum. In Fgure 2, n order to facltate the ant search resource node, unform number has carred on the all the resource node. 0 s for the vrtual resource node, as the startng pont of the ants crawlng [2-22]. In order to mae the problem sutable to be solved by the ant colony algorthm, stll need through approprate transformaton, due to the applcaton of ant colony algorthm to solve the problem s the ey to determne the change of the pheromone, therefore the followng two type of pheromone updates are used: D t = p Q / L p (3) D t = g Q 2 / L g (4) In Equaton (3), Dt p for local pheromone updatng, whch represents the ant every clmb over a dstance, each choosng a resource node, local pheromone s left n ths way. Q s constant, represents the strength of the pheromone. respectvely for the startng pont and end pont of the ourney, (5). Lp as the ourney length,,, Lp confrmed by wth Equaton L (,) p ì C + trc (,) Î A = ï í ïï î C 0. w (5) Wth C for the cost of resource node, trc are order constrants for the operaton. A s the set of drected edges n the graph constrant for tas cost of resource nodes, order. In Equaton (4), pheromone, g Dt g as a global pheromone updatng, represent the strength of the L for (compare number) s the total length of the path wth allowed () t, whch represent the frst t ant allowed to crawl resource nodes. Wth an array of tabu ant has clmbed resource nodes, such as allowed (0) = {, 2,3}, allowed () = {4, 7}, tabu (0) = 0, Lg can be calculated by the followng formula: n- g å ((),( )) t= 0 L = Lp tabu t tabu t Probablstc and Algorthm Optmzaton Accordng to the characterstcs of the XML tree, must carry on the probablty nqury. Frst to determne the probablty of each node and the determnaton of probablty need combned wth the specfc operaton. If adopts the model of tree search, the process s not smple, need to be pont by pont comparson. Usng ant colony algorthm, the prophase of probablty processng speed s slow, and the partcle swarm algorthm because do not need too many constrants, so t's sutable for a random query n the earler tme [23]. ) Frst conduct formalzed descrpton to the whole operaton. Suppose, there has n data node set N = (,2,...) n. The nodes have order relaton before and after the node. P s (6) TELKOMNIKA Vol. 2, No. 9, September 204:

7 TELKOMNIKA ISSN: the drect antecedent nodes for actvty set n the node before t, any sequence actvty P Î, cannot conduct before ddn't fnsh the former; Each node can be executed multple tmes, the tme needed for executon to the correspondng node for d a tme unt. A set of query data for a gven K, how to ensure that the query wthn gven tme T, and the tme spent the shortest, otherwse fal. Frst ntroduced two decson varables: x t = ìï í ïî 0 Node n tme t s proceedng Others The mplementaton tme of the node : { } ET = mn tx x = t T t The end executon tme node : FT = ET + DJ Msson follows: The obectve functon: K based on query tme T lmted mathematcal model descrpton s as mn max() tx t n, t T (7) 2 å å å T (8) = t= = K T n 2 mn(()) s = tx t - P T Among them: T T n T t = = = å å tx t (9) T = mn FTK + dk, Constrants: P For selectng nodes probablty. T å t= x = d, =, 2,... n t (0) FT - FT ³ d " Î P, =, 2,... n () å n = t tr T =, 2,..., K ; t =, 2,... T (2) x = 0 or, =, 2,..., n; t =, 2,..., T (3) Obectve functon (7) represents the query actvty tme as short as possble and feasble solutons; Target (8) represents the mnmum varance accordng to the actual probablty of tme; Obectve functon (9) represents the average requrements value under the condton of query actvty tme s the shortest and each node wthn the scope of the query tme; The Hybrd Probablstc Algorthms Based on Inconsstent Database (Gao Hongyu)

8 6928 ISSN: Constrant (0) represents no matter how to arrange each nodes, ts executon tme s fxed; Constrants () means before each query process must meet tght relatonshp; Constrant (2) means at the end of each sesson usng varous tme amount cannot be greater than the amount of tme spent. Constrant (3) s the varable constrants. 2) Algorthm solve () Frst, a seres of random numbers generated by random functon sequence accordng to the not ponts, and then correspondng sequence accordng to the sze of scheduled agan. In the seres of sequences, the sequence selected to meet the requrements set for the correspondng solvng partcles, whch method s lsted the ntal group of X and the drecton of the ntal group of V then use partcle swarm optmzaton (PSO) algorthm, startng from the ntal group. Partcles accordng to ther own and partner's flght experence constantly adust the poston and speed, so as to produce a new generaton of groups. V = wv + c Rand ()() PB - X + + c Rand ()() GB - X 2 (4) X = X + V + (5) In the equaton, the frst teraton; V s partcle speed n the teraton; PB For partcle ndvdual extreme value; Rand() for random Numbers on the nterval [0, ]; w s nerta weght; c X GB s for partcle place n as global extreme value; c s the cogntve factor, adust to PB flght step length; 2 s socal factor, adust to GB step length of the flght The teraton process, the partcle velocty and poston are restrcted to a partcular range, at the same tme, the PB and GB constantly updated, the fnal output of the GB s global optmal soluton. (2) to fnd success n the node, usng the ant colony algorthm rules correspondng to the probablty of nodes can be calculated usng the followng formula pheromone on the path to the next should choose path probablty. t ()()()() t + n = - r t t + D t t (6) m D t ()() t = D t å = t (7) Dt moment, t 0 represented the ncrement of the pheromone on the path n ths crcle. In the ntal D =. Dt () t. pheromone on the path the, represented the frst ant release n the crculaton process of D t () t ìï Q D = ï í ïïïï IF ant pass the path, n ths cycle (8) ï ïî 0 o. w Q s a constant, represent the pheromone strength. D represents the crcle formed by the frst ant tmes and tme unts. The ant loop, transfer s determned by the transton probablty p to that node. TELKOMNIKA Vol. 2, No. 9, September 204:

9 TELKOMNIKA ISSN: p ìï = ï í ïïïï å t a h sî allowed b t a s h b s ïî 0 o. w Î allowed (9) tabu Among, allowed = { c- tabu } represent the select nodes that ant set current. s taboo table, whch records the ant has been passng nodes and can't wal, used to ndcate the memory of the artfcal ants. optmzaton problem h - h means some heurstc nformaton, n the = d, a, b embodes the pheromone and the heurstc nformaton for decson-mang the mpact of ants. So the probablty of XML query nto the partcle swarm optmzaton (PSO) and ant colony algorthm, the man process s as follows: frst, usng partcle swarm method, produce a seres of random partcles, and under the constrant condton, fnd out the query results of the optmal soluton, and records the correspondng path, followed by usng ant colony algorthm, solvng the pheromone on the path, when carres on the data query agan, ust the path of the frst query has successfully, f not success, agan usng partcle swarm algorthm, generate new partcles, and solvng the correspondng values, as shown n Fgure 2. Fgure 2. XML whole Framewor Based on Ant Colony and Partcle Swarm Algorthm 4. Smulaton and Analyss 4.. Data Preparaton In order to verfy the correctness of the probablty query algorthm, and whether can satsfy the condton. The correspondng experment was carred out, expermental envronment under the followng condtons: the operatng system: professonal sp4 verson wn2000, database: mssql2005, CPU: C - M.3 GB and 768 MB of memory, hard ds: 80 GB, 5400 RPM, huge amounts of data to survve DataFactory5.2 verson. Database table used n ths study are for the structure of Table, and Table 2. Its record sze set s 00, 500, 000 and Respectvely, four characters of the feld length s 2, respectvely, to a unform database for cleanng, query rewrtng and probablstc query rewrte, wth P =, 2, 3 respectvely, ey attrbutes and dynamc propertes each of. The Hybrd Probablstc Algorthms Based on Inconsstent Database (Gao Hongyu)

10 6930 ISSN: Smulaton and Analyss Dfferent combnatons of parameters can get dfferent database. Test on a dfferent database wll get dfferent results. Here manly lsted several typcal schemes of these test results, accordng to the several groups of test data can be analyzed from a varety of methods for the consstency of database query optmzaton effcency. Record number Table 4. Three Relatonshps Number under the Same Condton probablty Cleanng rewrte record number for each group form The repeat tmes of the later two tems Condton Condton Condton 00 T=94,T2= < T=34,T2= <60* T=509,T2= <492* T=623,T2= <378*369 Each group form s record the number. By Table 4, the proporton of the query frequency of data cleanng wrte growed much faster than the query rewrte, and the probablty of the query accuracy than wrte faster and more accurate, especally when the precson of the probablty, the greater the search range s smaller. The potental for conflct data s less, but because of rewrtng query need to duplcate records wth probablty groupng, so although the accuracy s better than cleanng, but need to query multple tables of consstng. The prce s relatvely hgher than clean, but more relablty than cleanng, because cleanng wll mae some useful data loss does not meet the condtons, so the probablty of query method can be wdely used n ntellgent decson mang and data mnng. To compare wth Reference [4] dynamc rules wth the prunng n the data durng the condton of smulaton calculate., respectvely, three of the same node, accordng to the proposed ant colony algorthm wth hybrd operator s calculated, results as follows: Table 5. Smulaton Results Compared Tmes m=5 m=0 m=50 m=00 Prunng Ant colony Concluson Ths paper puts forward an mproved algorthm based on probablty of XML query, combned wth partcle swarm optmzaton and ant colony algorthm. Frst of all, usng partcle swarm algorthm and the hybrd method s rapd probablty of a set of nodes, and then through the orthogonal operator sees the optmal soluton of ant colony algorthm and global (or local) suboptmal soluton path pheromone strength. Wth the strength of the pheromone sze mprovement XPath query method, whch effectvely overcomes the defect of tree query pattern repeated comparson, maes an XPath query s more ntellgent and adaptve, whch s helpful for the applcaton of XML query and promoton. Acnowledgment The authors acnowledge fnancal support from the Natural Scence Foundaton of ** Cty, Chna (200A6035), the Innovaton and Entrepreneurshp Fund of Nngbo Cty, Chna (***), the Funds for Creatve Research Groups of Chna (No ) and Proect, Chna (No. B0805). References [] Barbara D, Garcla-Molna H, Porter D. The Management of Probablstc Data. IEEE Trans.Knowl.Data Eng. 992; 0: TELKOMNIKA Vol. 2, No. 9, September 204:

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