XML. Light-weight acceleration for streaming XML document filtering. Shuichi MITARAI, Akira ISHINO, and Masayuki TAKEDA

Σχετικά έγγραφα
A data structure based on grammatical compression to detect long pattern

Re-Pair n. Re-Pair. Re-Pair. Re-Pair. Re-Pair. (Re-Merge) Re-Merge. Sekine [4, 5, 8] (highly repetitive text) [2] Re-Pair. Blocked-Repair-VF [7]

Quick algorithm f or computing core attribute

Indexing Methods for Encrypted Vector Databases

A Method for Creating Shortcut Links by Considering Popularity of Contents in Structured P2P Networks

[4] 1.2 [5] Bayesian Approach min-max min-max [6] UCB(Upper Confidence Bound ) UCT [7] [1] ( ) Amazons[8] Lines of Action(LOA)[4] Winands [4] 1

GPGPU. Grover. On Large Scale Simulation of Grover s Algorithm by Using GPGPU

FX10 SIMD SIMD. [3] Dekker [4] IEEE754. a.lo. (SpMV Sparse matrix and vector product) IEEE754 IEEE754 [5] Double-Double Knuth FMA FMA FX10 FMA SIMD

IPSJ SIG Technical Report Vol.2014-CE-127 No /12/6 CS Activity 1,a) CS Computer Science Activity Activity Actvity Activity Dining Eight-He

GPU. CUDA GPU GeForce GTX 580 GPU 2.67GHz Intel Core 2 Duo CPU E7300 CUDA. Parallelizing the Number Partitioning Problem for GPUs

Vol. 31,No JOURNAL OF CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY Feb


Bundle Adjustment for 3-D Reconstruction: Implementation and Evaluation

Probabilistic Approach to Robust Optimization

{takasu, Conditional Random Field

n 1 n 3 choice node (shelf) choice node (rough group) choice node (representative candidate)

3. Επερώτηση XML Εγγράφων: Η Γλώσσα XPath

Buried Markov Model Pairwise

2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems

Maude 6. Maude [1] UIUC J. Meseguer. Maude. Maude SRI SRI. Maude. AC (Associative-Commutative) Maude. Maude Meseguer OBJ LTL SPIN

Κβαντική Επεξεργασία Πληροφορίας

MIDI [8] MIDI. [9] Hsu [1], [2] [10] Salamon [11] [5] Song [6] Sony, Minato, Tokyo , Japan a) b)

Japanese Fuzzy String Matching in Cooking Recipes

H/Y Ε-07: Κατανεµηµένα Συστήµατα Εαρινό Εξάµηνο Ακ. Έτους ιδάσκουσα: Παναγιώτα Φατούρου Προγραµµατιστικές Εργασίες

Math 6 SL Probability Distributions Practice Test Mark Scheme

Stabilization of stock price prediction by cross entropy optimization

ER-Tree (Extended R*-Tree)

HIV HIV HIV HIV AIDS 3 :.1 /-,**1 +332

Schedulability Analysis Algorithm for Timing Constraint Workflow Models

Homomorphism in Intuitionistic Fuzzy Automata

Advanced Subsidiary Unit 1: Understanding and Written Response

Πρόβλημα 1: Αναζήτηση Ελάχιστης/Μέγιστης Τιμής

Web 論 文. Performance Evaluation and Renewal of Department s Official Web Site. Akira TAKAHASHI and Kenji KAMIMURA

Capacitors - Capacitance, Charge and Potential Difference

Ψηφιακή ανάπτυξη. Course Unit #1 : Κατανοώντας τις βασικές σύγχρονες ψηφιακές αρχές Thematic Unit #1 : Τεχνολογίες Web και CMS

3: A convolution-pooling layer in PS-CNN 1: Partially Shared Deep Neural Network 2.2 Partially Shared Convolutional Neural Network 2: A hidden layer o

ΚΥΠΡΙΑΚΗ ΕΤΑΙΡΕΙΑ ΠΛΗΡΟΦΟΡΙΚΗΣ CYPRUS COMPUTER SOCIETY ΠΑΓΚΥΠΡΙΟΣ ΜΑΘΗΤΙΚΟΣ ΔΙΑΓΩΝΙΣΜΟΣ ΠΛΗΡΟΦΟΡΙΚΗΣ 19/5/2007

Δομές Δεδομένων. Δημήτρης Μιχαήλ. Συμβολοσειρές. Τμήμα Πληροφορικής και Τηλεματικής Χαροκόπειο Πανεπιστήμιο

Adaptive grouping difference variation wolf pack algorithm

Problem Set 3: Solutions

Speeding up the Detection of Scale-Space Extrema in SIFT Based on the Complex First Order System

Text Mining using Linguistic Information

EE434 ASIC & Digital Systems Arithmetic Circuits

Gemini, FastMap, Applications. Εαρινό Εξάμηνο Τμήμα Μηχανικών Η/Υ και Πληροϕορικής Πολυτεχνική Σχολή, Πανεπιστήμιο Πατρών

Nov Journal of Zhengzhou University Engineering Science Vol. 36 No FCM. A doi /j. issn

Development of a Tiltmeter with a XY Magnetic Detector (Part +)


Assalamu `alaikum wr. wb.

* ** *** *** Jun S HIMADA*, Kyoko O HSUMI**, Kazuhiko O HBA*** and Atsushi M ARUYAMA***

Παλεπηζηήκην Πεηξαηώο Τκήκα Πιεξνθνξηθήο Πξόγξακκα Μεηαπηπρηαθώλ Σπνπδώλ «Πξνεγκέλα Σπζηήκαηα Πιεξνθνξηθήο»

C++ 78 (478) A Parallel Skeleton Library in C++ with Optimization

Test Data Management in Practice

(C) 2010 Pearson Education, Inc. All rights reserved.

Anomaly Detection with Neighborhood Preservation Principle

Hancock. Ζωγραφάκης Ιωάννης Εξαρχάκος Νικόλαος. ΕΠΛ 428 Προγραμματισμός Συστημάτων

Fractional Colorings and Zykov Products of graphs

Overview. Transition Semantics. Configurations and the transition relation. Executions and computation

Physical DB Design. B-Trees Index files can become quite large for large main files Indices on index files are possible.

Fourier transform, STFT 5. Continuous wavelet transform, CWT STFT STFT STFT STFT [1] CWT CWT CWT STFT [2 5] CWT STFT STFT CWT CWT. Griffin [8] CWT CWT

Yoshifumi Moriyama 1,a) Ichiro Iimura 2,b) Tomotsugu Ohno 1,c) Shigeru Nakayama 3,d)

College of Life Science, Dalian Nationalities University, Dalian , PR China.

, Evaluation of a library against injection attacks

Review Test 3. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Special edition of the Technical Chamber of Greece on Video Conference Services on the Internet, 2000 NUTWBCAM

Congruence Classes of Invertible Matrices of Order 3 over F 2

FPGA. Fast and Efficient Tsunami Propagation Simulation with FPGA and GPGPU

Simplex Crossover for Real-coded Genetic Algolithms

Development of a Seismic Data Analysis System for a Short-term Training for Researchers from Developing Countries

(Υπογραϕή) (Υπογραϕή) (Υπογραϕή)

Newman Modularity Newman [4], [5] Newman Q Q Q greedy algorithm[6] Newman Newman Q 1 Tabu Search[7] Newman Newman Newman Q Newman 1 2 Newman 3

c Key words: cultivation of blood, two-sets blood culture, detection rate of germ Vol. 18 No

Κατανεμημένα Συστήματα. Javascript LCR example

An Efficient Calculation of Set Expansion using Zero-Suppressed Binary Decision Diagrams

Optimization, PSO) DE [1, 2, 3, 4] PSO [5, 6, 7, 8, 9, 10, 11] (P)

ΓΡΑΜΜΙΚΟΣ & ΔΙΚΤΥΑΚΟΣ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΣ

CONFIOUS: The Conference Nous Σύστημα Διαχείρισης Επιστημονικών & Ακαδημαϊκών Συνεδρίων. (

BMI/CS 776 Lecture #14: Multiple Alignment - MUSCLE. Colin Dewey

Τοποθέτηση τοπωνυµίων και άλλων στοιχείων ονοµατολογίας στους χάρτες

10. XML Αποθήκευση Δεδομένων: Relational vs. Native

Architecture οf Integrated Ιnformation Systems (ARIS)

Ανάκτηση Πληροφορίας. Διδάσκων: Φοίβος Μυλωνάς. Διάλεξη #03

Χρήση οντολογιών στη χαρτογράφηση γνώσης: Μελέτη περίπτωσης σε μία ακαδημαϊκή βιβλιοθήκη

1.575 GHz GPS Ceramic Chip Antenna Ground cleared under antenna, clearance area 4.00 x 4.25 mm / 6.25 mm. Pulse Part Number: W3011 / W3011A

the total number of electrons passing through the lamp.

ΟΙ Υ ΡΟΓΕΩΛΟΓΙΚΕΣ ΣΥΝΘΗΚΕΣ ΣΤΗΝ ΛΕΚΑΝΗ ΠΟΤΑΜΙΑΣ ΚΑΙ Η ΑΛΛΗΛΟΕΠΙ ΡΑΣΗ ΤΟΥ Υ ΑΤΙΚΟΥ ΚΑΘΕΣΤΩΤΟΣ ΜΕ ΤΗ ΜΕΛΛΟΝΤΙΚΗ ΛΙΓΝΙΤΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΣΤΗΝ ΕΛΑΣΣΟΝΑ

ΣΧΕΔΙΑΣΜΟΣ ΔΙΚΤΥΩΝ ΔΙΑΝΟΜΗΣ. Η εργασία υποβάλλεται για τη μερική κάλυψη των απαιτήσεων με στόχο. την απόκτηση του διπλώματος

Models for Probabilistic Programs with an Adversary

MnZn. MnZn Ferrites with Low Loss and High Flux Density for Power Supply Transformer. Abstract:

Supporting information. An unusual bifunctional Tb-MOF for highly sensing of Ba 2+ ions and remarkable selectivities of CO 2 /N 2 and CO 2 /CH 4

SCITECH Volume 13, Issue 2 RESEARCH ORGANISATION Published online: March 29, 2018

Γλωσσική Τεχνολογία. HTML/XML Processing HTTP Services

60W AC-DC High Reliability Slim Wall-mounted Adaptor. SGA60E series. File Name:SGA60E-SPEC

Study on Re-adhesion control by monitoring excessive angular momentum in electric railway traction

Technical Research Report, Earthquake Research Institute, the University of Tokyo, No. +-, pp. 0 +3,,**1. No ,**1

Mining Syntactic Structures from Text Database

Supplementary Material for The Cusp Catastrophe Model as Cross-Sectional and Longitudinal Mixture Structural Equation Models

Approximation of distance between locations on earth given by latitude and longitude

Retrieval of Seismic Data Recorded on Open-reel-type Magnetic Tapes (MT) by Using Existing Devices

1 (forward modeling) 2 (data-driven modeling) e- Quest EnergyPlus DeST 1.1. {X t } ARMA. S.Sp. Pappas [4]

Practical Implementation of Compressed Suffix Array on Modern Processors

Transcript:

DEWS2007 L5-4 XML E-mail: {mitarai,takea}@i.kyushu-u.ac.jp, ishino@ecei.tohoku.ac.jp XML XML XML XML XML XML XML XML XML XMLTK 4 6 1/6, XML, XML,, Astract Light-weight acceleration for streaming XML ocument filtering Shuichi MITARAI, Akira ISHINO, an Masayuki TAKEDA Department of Informatics, Kyushu University Department of System Information Sciences, Tohoku University E-mail: {mitarai,takea}@i.kyushu-u.ac.jp, ishino@ecei.tohoku.ac.jp This paper proposes a scalale XML filtering asing on preprocessing of XML ata. XML ata is preprocesse an transforme into a pair of a path trie an a inary XML ata. The path trie is the trie representing the set of strings of tag names along with root-to-leaf paths, an the inary XML ata is otaine from the original XML ata y replacing every start tag an the corresponing en tag with special yte coes, respectively. Each occurrence of the special yte coe for a start tag is followe y ID of the corresponing noe of the path trie. Path pattern matching is performe against the path trie. Since the path trie is much smaller than the XML ata, a rastic speeup is possile. Query pattern processing is one y comining the keywor occurrences foun in scanning of the inary XML ata with the information ae to path trie noe implie y noe IDs emee in the inary XML ata. Experimental results show that, the processing time an memory requirement of our metho are, respectively, 1/6 1/4 an 1/6 compare with XMLTK, a state-of-the-art streaming XML processor. Key wors stream processing, XML, XML filtering, Path-trie, DFA 1. SDI (Selective Dissemination of Information) [6 pulish/suscrie ag-of-wors XML (extensile Markup Language) XPath [20 XPath W3C XML ()

XML DOM DOM 50 200MB XML ( [3, [4, [8, [15) XML DOM XML YFilter [8 XMLTK [4 XPath (forwar axes) (ancestor axes), (siling axes) [5 XML YFilter XMLTK XML YFilter XFilter [3 (NFA) XFilter NFA (DFA) NFA DFA XMLTK XML NFA DFA Lazy-DFA [4 XMLTK DFA XML XML [7 XML [2 XMLTK (Stream Inex; SIX) [4 XML XML XML XML XML XML XML XML XML XML 1 2 XML SIX DataGuie [9 XML DataGuie 2. XML 3. YFilter, XMLTK SIX XMLTK SIX 4. preicatecount 5. 2. [22, [23 XQuery DFA XML // * DFA 2. 1 Σ N XML N Σ () 1 N * / // / // - π XML 1 name=value value @name

x y e=true 1 XML (x, y) XPattern π 1 [π 2 : e * // π π * π π π 1 π 2 π 1 [π 2 XML x y x = y π 1 [π 2 (x, y) π 1 π 2 x x y x y x e = e(w 1,..., w m ) Σ w 1,..., w m Σ e w 1,..., w m (truth assignment) e XPattern π 1 [π 2 e π 1 [π 2 : e XPattern π 1 [π 2 : e XML x x y π 1 [π 2 (x, y) y e π 1 [π 2 XPattern π 1 [π 2 : true 1 XPattern π 1 [π 2 : e (x, y) 1 Given: XML T. Query: XPattern P 1,..., P l. Answer: i = 1,..., l T XML P i x XML T XML XML XML x 4. XPattern x [19 2. 2 XML XML XML XML π1 π2 2 Input XML file <a> <> <c>...</c> </> <> <>...</> </> <> <c> <>...</> <> <>...</> </> </c> </> </a> Binary XML file [1 [2 [3... [2 [4... [2 [3 [5... [6 [7... c a 5 1 a 2 c Path trie 3 4 c 6 7 XML XML 2 2 XML XML XML ID XML [ [ XML O(n log N + T ) 2 n XML T N T XML XML 1, 2

1 DBLP [11 xmlgen [17 XML XML (MB) DBLP 352 8,632,812 35 ranom 111 1,666,310 74 2 XML XML CPU (MB) (sec) DBLP 208 138 100.41 ranom 84 515 73.26 1 Hit! a Hit! 1 2 4 6 2 2 2 3 5 7 c c Hit! 3 4 3 8 9 5 6 10 7 1 a 2 3 4 c <P1, 1> 5 6 <P1, 2> 7 <P1, 1>, <P1, 3> 3 XML P 1 = a//[// XML XML 76% 2. 3 XML 2 XML XML P 1 = a//[// 3 P 1 = a//[// XML (x, y) = (4, 5), (6, 8), (6, 10), (9, 10) (x, y) = (2, 5), (2, 7), (6, 7), (2, 4) (x, y) y x, y XML XML (6, 8) P 1 5 P 1, 2 XML XML y = 8 5 5 P 1, 2 y 2 x = 6 (x, y) [21 XML (NFA) NFA NFA [14 π NFA π + 1 NFA 32 64 NFA O( N π ) n, h O(h n) (NFA ) π 1 [π 2 π 1 π 2 NFA M 12 f π 2 π R 2 NFA M 2 M 12 f M 2 XML NFA Mf 12, M 2 P i = π 1 [π 2 (x, y) (x, y) y P i, x, y XML XML YFilter XMLTK 2. 4 XPath (i)/a/[containts(name, mickey ) (ii)/a//name[./text()= mickey mouse (iii)/a/[@month=decemer (i) /a//name mickey (ii) name mickey mouse (iii) /a/ Decemer XML Aho-Corasick(AC) AC AC

2. 5 XML XML XPattern P 1,..., P l e W = w 1,..., w m W AC M XML offset XML epth 2 Occ Q Occ w i Occ[[i Q XPattern P q Q[[q XML 1 c c [ v (v, offset) S epth 1 Occ[epth[1... m Q[epth[1... l M c S (v, offset) v q, XPattern P q e Occ[epth[1... m e Q[epth [q epth 1 M c M c Occ[epth[1... m 3. ReHat Linux Avance Server 2.1, CPU 2.4GHz Intel Pentium4, 2.0GB RAM XML xmlgen [17 111MB XMLTK (location step) YFilter pathgenerator [1 // * (1%, 1%) (10%, 10%) 3. 1 3. 2 XML Processing time (msec) Path patterns Simple path patterns Numer of queries 4 3. 3 YFilter XMLTK 3. 4 SIX 3. 5 AC 3. 1 4 NFA XML ( 5) XML 1,666,310 515 2 NFA 3. 2 XML XML 3 ( ) XML XML [21 3 (sec) 1 0.51 0.86 Pro(//)=Pro(*)=0.01 10 0.52 1.29 100 0.54 4.30 1,000 0.53 21.81 1 0.51 0.86 Pro(//)=Pro(*)=0.01 10 0.52 1.36 100 0.52 5.51 1,000 0.54 34.50

l NFA NFA l 3. 3 XMLTK YFilter 4 10,000, 100,000 (//, * (1%, 1%), (10%, 10%) 2 ) Linux RSS (Resient Set Size) 100,000 YFilter 10,000 (//, * 10%) 4,925KB XMLTK 34,412KB, YFilter 1,494,845KB 6 5 1 100,000 YFilter XMLTK 100,000 XMLTK 4 (KB) YFilter XMLTK 10,000 3,320 1,169,975 30,288 1% 100,000 18,632 285,328 10,000 4,952 1,494,845 34,412 10% 100,000 28,543 318,560 5 (sec) YFilter XMLTK 1 0.57 39.24 2.27 10 0.57 42.54 2.57 100 0.57 45.22 3.09 1, 000 0.67 61.30 4.14 10, 000 1.85 155.30 11.83 100, 000 142.04 270.81 3. 4 SIX Stream IneX (SIX) [4 SIX 6 5 SIX 1.6 7.2 5 XMLTK SIX XMLTK // * 6 SIX (sec) XMLTK 1 0.00 0.14 10 0.07 1.53 100 0.18 2.49 1, 000 0.39 4.37 10, 000 1.81 12.25 100, 000 139.62 275.74 3. 5 AC AC (SA) [13 (CSA) [10, [16 5Nyte N 8 () (1) (2) (3)

7 occ m D CSA, l CSA N (, l) = (8, 128) CSA, SA http://pizzachili.cc.uchile.cl/texts/nlang 100MB 100 ( ) 50 ( ) 100 ( ) AC 2 AC 7 AC, SA, CSA AC O(m + N + occ). SA CSA (1) O(mlogN + occ) O(m log N + occ log ϵ N) (2) O(mlogN + occ) O(m log N + occ log ϵ N) (3) O(mlogN + occ log D) O(m log N + occ(log ϵ N + log D)) 5N (yte) N (yte) 8 AC CSA( = 8, l = 128) SA 39,670,500 2,532,290 2,159 (sec) AC CSA SA 100 1.31800 0.00450 0.00161 100 0.81000 0.00525 0.00156 100 0.36300 0.00630 0.00051 (sec) AC CSA SA 100 1.31800 445.866 0.00161 100 0.81000 31.027 0.00156 100 0.36300 0.0394 0.00051 (sec) AC CSA SA 100 1.59500 517.113 22.880 100 0.91300 36.364 1.460 100 0.44900 0.0429 0.002 4. 4. 1 XPattern P 1 = π1[π 1 2 1 : e 1. P l = π l 1[π l 2 : e l e i w 1,..., w m e i w 1,..., w m P 1,..., P i 1 e i 2. 5 epth Occ[epth[1..m Q[epth[1..i 1 a ) π[π 1 an π 2 XPath P 1 = π[π 1 : true P 2 = π[π 2 : true P 3 = π[ε : P 1 P 2 XPath π[π 1 an π 2 Q[epth[3 XPath π[π 1 or π 2 π[π 1 an π 2 or π 3 ) XPath π 1 [π 2 [π 3 an π 4 P 1 = π 1 π 2 [π 3 : true P 2 = π 1 π 2 [π 4 : true P 3 = π 1 [π 2 : (P 1 P 2 ) XPath π 1 [π 2 [π 3 an π 4 Q[epth[3 P 4 = π 1 [π 2 π 3 : true P 5 = π 1 [π 2 π 4 : true P 6 = π 1 [ε : (P 4 P 5 ) XPath π 1 [π 2 [π 3 an π 4 Q[epth[6 4. 2 P i XML 1 e i true, false 1,0,, an 2 P i = π 1[π 2 : e i x, P i (x, y) P i y e i x P i x 0 XPath π 1[count(π 2) > 1 count sum, max, min, an avg (average) 5. AC XPath 3. YFilter XMLTK

1. Suffix Trees with Applications to Text Inexing an String SDI Matching, STOC 00, pp. 397 406 (2000). [11 Ley, M.: DBLP Computer Science Biliography, http://lp.unitrier.e/. SDI SIX XML [12 M. Takea, et al.: Speeing up string pattern matching y text compression: The awn of a new era, Trans. Information Processing Society of Japan, Vol. 42, No. 3, pp. 370 384 (2001). Special issue for IPSJ 40th anniversary awar papers. XML [13 Maner, U. an Myers, G.: Suffix arrays: A new metho for on-line string searches, SIAM J. Computing, Vol. 22, No. 5, pp. 935 948 (1993). [14 Navarro, G. an Raffinot, M.: Flexile pattern matching in strings: Practical on-line search algorithms for texts an iological sequences, Camrige University Press (2002). (1) [15 Peng, F. an Chawathe, S. S.: XPath queries on streaming ata, SIGMOD 03, pp. 431 442 (2003). (2) [16 Saakane, K.: Compresse Text Dataases with Efficient XML Query Algorithms Base on the Compresse Suffix Array, (3) (twig pattern) Proc. of 11th International Symposium on Algorithms an Computation (ISAAC 00), LNCS 1969, pp. 410 421 (2000). [17 Schmit, A., Waas, F., Kersten, M. L., Carey, M. J., (1) Manolescu, I. an Busse, R.: XMark: A enchmark for [12, [18 (2) XML XML ata management, VLDB 02, pp. 974 985 (2002). [18 Shiata, Y., Kia, T., Fukamachi, S., Takea, M., Shinohara, A., Shinohara, T. an Arikawa, S.: Speeing up pattern Matching y Text Compression, CIAC 00, LNCS 1767, XML Query Processor for a Large Numer of Structural an Textual Patterns, Technical Report DOI-TR-CS-226, pp. 306 315 (2000). [19 Takea, M., Ishino, A. an Mitarai, S.: A Light-Weight Department of Informatics, Kyushu University (2006). [22, [23 DFA [20 W3C: XQuery 1.0 an XPath 2.0 Full-Text Use Cases, XMLTK http://www.w3.org/tr/xmlquery-full-text-use-cases. [21 (3) 4. XPattern XML DBWe2003 (2003). [22 XQuery DBWe2005 (2005). [23 XQuery [1 : Filtering an Transformation for High-Volume XML Message Brokering, http://yfilter.cs.erkeley.eu/coe release.htm. Letters Vol. 4, No. 4 (2006). [2 : Report From the W3C Workshop on Binary Interchange of XML Information Item Sets, http://www.w3.org/2003/08/inaryinterchange-workshop/report.html (2003). [3 Altinel, M. an Franklin, M.: Efficient filtering of XML ocuments for selective issemination, VLDB 00, pp. 53 64 (2000). [4 Avila-Campillo, I., Green, T. J., Gupta, A., Onizuka, M., Raven, D. an Suciu, D.: XMLTK: An XML Toolkit for Scalale XML Processing, PLANX 02 (2002). [5 Barton, C., Charles, P., Goyal, D., Raghavachari, M., Josifovski, V. an Fontoura, M.: Streaming XPath processing with forwar an ackwar axes, ICDE, pp. 455 466 (2003). [6 Carzaniga, A., Rosenlum, D. an Wolf, A.: Design an Evaluation of a Wie-Area Event Notification Servie, Vol. 19, No. 3, pp. 332 383 (2000). [7 Chen, Y., Mihaila, G. A., Davison, S. B. an Pamanahan, S.: EXPeite: A System for Encoe XML Processing, CIKM 04, pp. 108 117 (2004). [8 Diao, Y., Altinel, H., Franklin, M. J., Zhang, H. an Fischer, P. M.: Path Sharing an Preicate Evaluation for HighPerformance, ACMTOD (2003). [9 Golman, R. an Wiom, J.: DataGuies: Enaling Query Formulation an Optimization in Semistructure Dataases, VLDB 97, pp. 436 445 (1997). [10 Grossi, R. an Vitter, J. S.: Compresse Suffix Arrays an