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Tree Inclusion, Signatures, and Evaluation of Path-Oriented Queries

Tree Inclusion, Signatures, and Evaluation of Path-Oriented Queries Dr. Yangjun Chen Dept. Applied Computer Science, University of Winnipeg, Canada. Motivation Path-Oriented Queries and Tree Inclusion Problem Evaluation of Path-Oriented Queries - Top-down Algorithm for Tree Inclusion

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Tree Inclusion, Signatures, and Evaluation of Path-Oriented Queries

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  1. Tree Inclusion, Signatures, and Evaluation of Path-Oriented Queries Dr. Yangjun ChenDept. Applied Computer Science, University of Winnipeg, Canada • Motivation • Path-Oriented Queries and Tree Inclusion Problem • Evaluation of Path-Oriented Queries • - Top-down Algorithm for Tree Inclusion • - Integration of Signatures into Top-down Tree Inclusion • Experiment Results • Summary and Future Work

  2. Motivation • Local Information Resource Management – document databases • Internet – Distributed Document Databases • Document Databases • - Storage of documents in relational databases • non-structured data, semi-structured data • - Evaluation of path-oriented queries in document databases • path-oriented languages: XQL, XPath, and XML-QL • Query evaluation methods: • inverse-file based • signature based • string-matching based: suffix trees, Pat-trees • tree-inclusion based • Integrating signatures into top-down tree inclusion algorithm

  3. Path-Oriented Queries and Tree Inclusion Problem • XML Documents and Path-Oriented Queries

  4. Path-Oriented Queries and Tree Inclusion Problem • Tree Inclusion Problem • Definition (tree embedding) Let T and P be two labeled trees. A mapping M from the nodes of P to the nodes of Tis an embedding of Pinto Tif it preserves labels and ancestorship. That is, for all nodes u and v of P, we require that • a) M(u) = M(v) if and only if u = v, • b) label(u) = label(M(u)), • c) u is an ancestor of v in P if and only if M(u) is an ancestor of M(v) in T, and • d) v is to the left of u iff M(v) is to the left of M(u). • An embedding is root preserving if M(root(P)) = root(T). It can be shown that restricting to root-preserving embedding does not lose generality.

  5. Path-Oriented Queries and Tree Inclusion Problem • Example: T: P: Hotel-room-reservation Hotel-room-reservation name location reservation name location type address ?x City-or-district price from to rooms country address state City-or- district Travel-lodge Post- code number number street street one-bed- room April 20, 2005 April 28, 2005 Winnipeg Winnipeg $119.00 Manitoba Canada R3B 2E9 515 Portage Ave. 515 Portage Ave.

  6. Path-Oriented Queries and Tree Inclusion Problem • - Algorithms for Tree Inclusion Problem • Bottom-up algorithm: • Kilpelainen-Mannila’s Algorithm (Pekka Kilpelainen and Heikki • Mannila, Ordered and unordered tree inclusion, SIAM Journal of • Computing, 24:340-356, 1995.) • O(|T| |P|) time • O(|T| |P|) space • Chen’s Algorithm (W. Chen, More efficient algorithm for ordered • tree inclusion, Journal of Algorithms, 26:370-385, 1998.) • O(|T||leaves(P)|) time • O(|leaves(P)|min{height(P), |leaves(T)|}) space

  7. Path-Oriented Queries and Tree Inclusion Problem • - Algorithms for Tree Inclusion Problem • Top-down algorithms: • Y. Chen and Y.B. Chen, An Efficient Top-down Algorithm for Tree • Inclusion, in Proc. of 18th Intl. Conf. Symposium on High Performance • Computing System and Application, Winnipeg, Canada: IEEE, • May 2004, pp. 183-187.) • O(|T| |leaves(P)|) time, need no extra space • Y. Chen and Y.B. Chen, On the Top-down Tree Inclusion Algorithm, • submitted to Information Processing Letters.) • O(|T||height(P)|) time, need no extra space • Advantages of top-down over bottom-up: • - better computational complexities • - checking trees page-wise (suitable for the cases of large data volume) • - integrating signatures into tree inclusion to cut useless subtree checkings • as early as possible

  8. Evaluation of Path-Oriented Queries • - Top-down Algorithm • Target tree: T = <t; T1, ..., Tk>, where t = root(T) and each Ti (i = 1, …, k) • is the subtrees of t; • Pattern forest: G = <P1, ..., Pq>, where each Pj(j = 1, …, q) is a subtree. • Main idea: • The algorithm attempts to find the number of subtrees j ( 0) within an • ordered forest G = <P1, ..., Pq> (q  1), which are embedded in a target • tree T. If j = q, we say that G is embedded in T. If j < q, then only the trees • P1, ..., and Pj are embedded in T. Let p1, ..., pq and t be the roots of P1, ..., Pq • and T, respectively. Since a forest does not have a root, we use a virtual • node pv to serve as a substitute for root(G). Thus, root(G) will return pv if • G = <P1, ..., Pq> with q > 1, and will return p1 if q = 1.

  9. Evaluation of Path-Oriented Queries - Top-down Algorithm Case 1: root(G) pv (i.e., G = <P> is a tree and root(G) = p), and label(p) label(t). If G is embedded in T, then there must exist a subtree Ti of t such that it contains the whole G. The algorithm should return 1 if an embedding can be found and 0 if it cannot. label(root(T))  label(root(G)) G: T: Ti Tree G is included in Ti.

  10. Evaluation of Path-Oriented Queries - Top-down Algorithm Case 2: root(G) pv (i.e., G = <P> and root(G) = p), and label(p) = label(t). Let <P1, ..., Pl> (l  0) be the forest of subtrees of pand <T1, ..., Tk> the forest of subtrees of t. If G is embedded in T, there must exist two sequences of integers: k1, ..., kg and l1, ..., lg (g l) such that includes < , ..., > (i = 1, ..., g, l0 = 0, lg = l), where < , ..., > represents a forest containing subtrees , ..., and . Thus, if lg = l, the algorithm should return 1 since we have a root preserving inclusion of G in T. Otherwise, it should return 0. label(root(T)) = label(root(G)) G: T: p t = Pl Tk P1 T1 … … … … … … include include

  11. Evaluation of Path-Oriented Queries - Top-down Algorithm Case 2: root(G) = pv and there exists an integer j (0 j q) such that <P1, ..., Pj> is included in T. If j = q, then the whole G is embedded in T. There are two possibilities to be considered when looking for j. The first possibility is similar to Case 2, where there are two sequences of integers: k1, ..., kg and l1, ..., lg (g q) that represent the order, in which the subtrees of root(G) are embedded in the subtrees of root(T). In thiscase, j = lg. If j = 0, we will check the second possibility to see whether there exists a root preserving inclusion of P1 in T, i.e., label(p1) = label(t) and the subtrees of p1 are included in the subtrees of t. In this case, j = 1.

  12. possibility 1: qv(virtual node) T: G: t = Pl Tk P1 T1 … … … … … … include include Evaluation of Path-Oriented Queries - Top-down Algorithm possibility 2: label(root(T)) = label(root(P1)) qv(virtual node) G: T: t = Pl Tk P1 T1 … … … … … … include

  13. Evaluation of Path-Oriented Queries - Top-down Algorithm • j := bottom-up-process(T, G); • 13. if (j = l) then return 1 else 0;} • else {ift is a leaf then return 0; • 14. (*handling Case 1*) • 15. i := 1; • 16. while (i k) do • 17.{iftop-down-process(Ti, G) > 0 then return 1; • 18. i := i + 1;} • 19. return 0;} } • end functiontop-down-process(T, G) input: T = <t; T1, ..., Tk>, G = <p; P1, ..., Pq> (*p may or may not be a virtual node.*) output: if root(G) is virtual, returns j  0; else returns 1 if T includes G; otherwise returns 0. begin 1. ifroot(G) is virtual then 2. if (|T| < |P1| + |P2| or p has only one child) 3. thenG := P1; 4. else {j := bottom-up-process(T, G); 5. if (j = 0 and label(t) = label(P1’s root)) (*second possibility in Case 3*) 6. then {change P1’s root to a virtual node; x := bottom-up-process(T, P1); 7. if (x = the number of the children of P1’s root) thenj := 1 else j := 0;} 8. return j;}} 9. if |T| < |G| return 0; 10. else {if (label(t) = label(p)) (*handling Case 2*) 11. then {p := virtual node; functionbottom-up-process(T, G) input: T = <t; T1, ..., Tk>, G = <p; P1, ..., Pq> output: j - an integer begin 1. j := 0; i := 1; 2. while (j < q and i k) do 3. { x := top-down-process(Ti, G); 4. j := j + x; G := <p; Pj+1, ..., Pq>; i := i + 1; } end

  14. Integration of Signatures into Top-down Inclusion • Definition A signature for a key word or an attribute value is • hash-coded bit string. • - Example: (constructing a signature for a word with m = 4 and F = 12) • “database” •  • letter triplets: dat, ata, tab, aba, bas, ase •  • H(dat) = 5, H(ata) = 1, H(tab) = 8, H(aba) = 1, H(bas) = 10, • H(ase) = 8. •  • 100 010 010 100 • D. Dervos, Y. Manolopulos and P. Linardis, “Comparison of signature • File models with superimposed coding,” J. of Information Processing • Letters 65 (1998) 101 - 106.

  15. Integration of Signatures into Top-down Inclusion Definition A signature for a key word or an attribute value is hash-coded bit string. - Important parameters: m: number of 1s in bit string F: length of bit string D: size of a block (or average number of the key words of an element) optimal choice of the parameters: Fln2 =mD(1) S. Christodoulakis and C. Faloutsos, “Design consideration for a message file server,” IEEE Trans. Software Engineering, 10(2) (1984) 201-210.

  16. 0101 0011 0001 0010 1010 1100 a: b: c: d: e: f: 0000 1000 0101 1000 1000 0000 T: a e b t0 t1 t2 t1 t2 t0 e c d f t22 t12 t22 t21 t11 t11 t12 t21 Integration of Signatures into Top-down Inclusion - Assigning signatures to tree nodes Let v be a node in a tree T. If v is a leaf node, its signature svis equal to the signature assigned to its label. Otherwise, sv= s v1 ... vn, where s represents the signature for the label associated with v, and s1, ... , and snare the signatures of v’s children: v1, ..., vn, respectively. T: a 1111 1101 e b 1111 1101 1111 1000 f e c d 1100 0000 0001 0101 0010 1000 1010 1000

  17. t0 t1 p0 t2 t21 p1 t22 t11 p2 t12 e 1111 1101 c d 0010 1000 0001 0101 Integration of Signatures into Top-down Inclusion • - Cutting off useless subtree checks by examining signatures • We assign each node v in T a bit string sv (called a signature), and each node • u in P a bit string su in such a way that if su matches sv then the subtree Tv • rooted at v may includes the subtree Pu rooted at u. Otherwise, Tv definitely • does not contain Pu. By “matching”, we mean that for each bit set to 1 in su, • the corresponding bit in sv is also set to 1 while for a bit set to 0 in su, the • corresponding bit in sv can be 0 or 1. In the following, we discuss this • technique in great detail. virtual node T: P: This subtree will not be explored. a 1111 1101 a b 1111 1000 0011 1101 e c d f 0010 1000 1100 0000 1010 1000 0001 0101

  18. Integration of Signatures into Top-down Inclusion • - Determine the length of signatures • Consider s = s1 / s2, where s1 and s2 are of length F and with m1 • and m2 bits set to 1, respectively. • How to determine the length of S? • l - the number of 1s in s • d = l - m’, where m’ = max(m1, m2). • length(s) = F + cd, where c is a constant and should be tuned for different • applications. • The value of d can be estimated as follows. • l - random variable representing the number of positions, in which both • s1 and s2 have 1s.

  19. Integration of Signatures into Top-down Inclusion • - Determine the length of signatures • El = 1 p(l = 1) + 2  p(l = 2) + ... + m’’  p(l = m’’) (2) • m’’ = min(m1, m2) and p(l = i) represents the probability that l is equal to i. • p(l = i) = (3) • d = l - m = m1 + m2 - l - max(m1, m2).

  20. Evaluation of Path-Oriented Queries • - Procedure for calculating signature length • 1) Identify the key words in a document, which can be done by using • Connexor-analyzer (http://www.connexor.com/demos/index.html.) • 2) Determine the length of the signatures for the nodes of a document tree, • which can be done in two steps: • - First, use formula (1) to determine the initial length of the signatures • according to the number of the chosen key words and their distribution • - Secondly, use formula (2) and (3) to determine the length of the • signatures for each document according to the initial length set for • signatures.

  21. Evaluation of Path-Oriented Queries • - Determine Procedure for calculating signature length In the figure, F stands for the initial length of the signatures and m for the initial number of bits set to 1.

  22. Experiment Results • - Test Platform • Computer - DELL desktop PC equipped with Pentium III 864Ghz processor, • 512MB RAM and 20GB hard disk. • Database system - Oracle-9i Enterprise Edition, The default buffer cache of • Oracle-9i is of size 4MB. • Language - Oracle PL/SQL language. • Data - all the 37 Shakespeare’s plays in a database

  23. Experiment Results • - Storage of XML documents in databases • All the documents are stored in three tables. • The relation Element has the following structure: • {DocID: <integer>, ID: <integer>, Ename: <string>, • firstChildID: <integer>, siblingID: <integer>, attributeID: <integer>}

  24. Experiment Results • - Storage of XML documents in databases • The relation Text is of a simpler structure: • {DocID: <integer>, textID: <integer>, value: <string>}, • where “textID” is for the identifiers of texts as the values of the corresponding elements • in the original document. One should notice that a text takes always an element as the • parent node. See the following table for illustration.

  25. Experiment Results • - Storage of XML documents in databases • The relation Attribute has five data fields: • {DocID: <integer>, att-ID: <integer>, parentID: <integer>, att-name: <string>, • att-value: <string>}.

  26. Experiment Results - Tested queries Group I - for testing path length impact • Group II - for testing node degree impact

  27. Experiment Results - Tested queries Group III - for testing impact of matching at higher level • Group IV - for testing impact of matching at middle level

  28. Experiment Results - Tested queries Group V- for testing impact of matching at lower level

  29. Experiment Results • - Tested methods • Inversion on Elements and Words (IEW) • (C. Zhang, J. Naughton, D. DeWitt, Q. Luo and G. Lohman, “On Supporting • Containment Queries in Relational Database Management Systems, in Proc. of ACM • SIGMOD Intl. Conf. On Management of Data, California, USA, 2001.) • Inversion on Paths and Words (IPW) • (C. Seo, S. Lee, and H. Kim, An Efficient Index Technique for XML Documents • Using RDBMS, Information and Software Technology 45(2003) 11-22, Elsevier • Science B.V.) • Tree Inclusion Algorithm (TIA) • Tree Inclusion with Signatures (TIS)

  30. E-index: (1, <1, 45>, 0) ... hotel-room-reservation (1, <2, 4>, 1) ... name (1, <5, 28>, 2) ... location ... ... ... ... ... T-index: (1, 3, 2) ... Travel-lodge (1, 7, 3) ... Winnipeg (1, 10, 3) ... Manitoba ... ... ... ... ... Experiment Results • - Tested methods • Inversion on Elements and Words (IEW) • - (Dno, Wposition, level) for a text word • - (Dno, Eposition, level) for an element Example:

  31. Experiment Results • - Tested methods • To evaluate the query: /hotel-room-reservation/location/address [street = Portage Ave.], • four joins are performed: • self-joins on E-index relation to connect ‘hotel-room-reservation’ and ‘location’, • ‘location’ and ‘address’, • ‘address’ and ‘street’, • the join between E-index and T-index relations to connect ‘street’ and ‘Portage Ave.’

  32. Experiment Results • - Tested methods • Inversion on Paths and Words (IPW) • - Path(path, pathID), • - PathIndex(pathID, docno, begin, end) • - Word(word, wordID) • - WordIndex(wordID, docno, pathID, position)

  33. Experiment Results • - Tested methods • In order to process the same query: • /hotel-room-reservation/location/address [street = Portage Ave.], • two joins are needed. • First join between Path and WordIndex relations with the following join condition: • Path.path = ‘hotel-room-reservation/location/address/street’ and • Path.pathID = WordIndex.pathID. • The second join between the result R of the first join and the Word relation with the • join condition: • R.wordID = Word.wordID and Word.word = ‘Portage Ave.’.

  34. IPW      * + + * * * + + TIS TIA IEW 12 IPW IPW + + + TIS TIS TIA TIA Execution time (sec.) 6 • • • * * Q1 Q2 Q3 Q4 Q5 Results of Group III Experiment Results - Tested results 2 1000 IPW TIS TIA Execution time (sec.) Execution time (sec.) 1 100  • • • • + * + * * * + + Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Results of Group I Results of Group II 12 + + + Execution time (sec.) 6 • • • * * Q1 Q2 Q3 Q4 Q5 Results of Group IV

  35. * + IPW TIS TIA Experiment Results - Tested results 12 + + + Execution time (sec.) 6 • • • * * Q1 Q2 Q3 Q4 Q5 Results of Group V

  36. Summary and Future Work • Path-oriented queries in document databases • Evaluation of path-oriented queries • - top-down algorithm for tree inclusion problem signatures- Integration of signatures into top-down tree inclusion • Future work: • document recognition using • tree inclusion • probabilistic analysis • Benford low • Zipf low

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