1. Selective Dissemination of Streaming XML By Hyun Jin Moon, Hetal Thakkar

2. Overview  Introduction  Background  XFilter Architecture Implementation Optimizations Experiments/Analysis Conclusion  Related Work: XTrie

3. Introduction  Information Dissemination Enormous Amount of Data Lots of Users User Profiles  Bag of Keywords Selective Distribution of Data  Applications Stocks, Sports, Traffic, Electronic Personalized Newspapers, Entertainment, etc.

4. Introduction (Cont’d)  Emergence of XML as Standard of Information Exchange on Internet Utilize Structure of XML for Better Dissemination Use XPath(s) for User Profile  Optimizations for Searching a Streaming XML Document for Many XPaths XFilter XTrie Structure

5. Background:  SDI Structure  XPath  XML Parsers DOM SAX

6. Background: SDI Architecture

7. Background: XPath  Query Structure and Data  Enough Complexity for Dissemination  Constructs ‘*’ Relative Path  //product[price/msrp<300]/name

8. Background: XML Parser  DOM: Document Object Model  SAX: Simple API for XML (SAX) Standard Interface for Event-Based XML Parsing Suitable for Streaming XML Example:

9. XFilter  Architecture  Implementation  Optimizations List Balancing Prefiltering  Experiments/Analysis  Conclusions

10. XFilter: Architecture

11. XFilter: Implementation  Filter Engine Brute Force Approach Instead,  Decompose Queries into Path Nodes  Create a Query Index from Path Nodes  Build a Finite State Machine on the Query Index  As a Document Arrives Traverse the FSM for All Queries (In One Pass)

12. XFilter: Implementation  Path Nodes: QueryId Position:  Sequence Number for Path Node in the Query (XPath) RelativePos:  Relative Distance in the Document Level (Can be Updated During Evaluation):  Absolute Level in the XML Document, at Which the Path Node should be Checked

13. XFilter: Implmentation  Query Index: Hash Table  Key: Element Names that Appear in XPath Expressions  Data: 2 Lists Containing Path Nodes Candidate List: “Current Node” of Each Query Representing Current State of the Query Wait List: Path Nodes Representing Future States

14. XFilter: Implementation

15.  Start Element Handler: Inputs: Name, Level, and Attribute-Values of the Element Action:  Look-up Element Name in Query Index  Examine Nodes in Candidate List Check Level, etc.  If All Checks Succeed AND Final Path Node of Query Then the Document is Deemed to Match the Query  Else If All Checks Succeed Then Move the Query to its Next State  Else Do Nothing

16. XFilter: Implementation  End Element Handler Input: Element Name Action:  Delete the Corresponding Path Nodes from the Candidate List (for Restoring Purpose)  Element Character Handler Input: Data Action: Similar to Start Element Handler

17. XFilter: Implementation  Example: Start Document Start Element: aLevel: 1 Start Element: bLevel: 2 Start Element: cLevel: 3 End Element: c End Element: b End Element: a

18. XFilter: Implementation

19.  Advanced Features Attribute Filter  Start Element Event Handler Content Filter  Element Character Handler Nested Path Expression  Treat Nested Sub-Queries as Another Query

20. XFilter: Optimizations  List Balancing (LB) Basic Approach: First Path Node for Each Query in the Candidate List  Low Selectivity Instead, Apply Candidate List Balancing  When Adding a New Query to Query Index the Path Node Who has the Shortest Candidate List is Chosen as the “Pivot” Node Prefix

21. XFilter: Optimizations  Prefiltering Eliminate Queries, which have Element Name(s) that are not Present in the Document Yan and Garcia-Molina’s Key Based Algorithm Assign Key Element of the Queries Create Occurrence Table for Each Arriving Document  Occurrence Table: Hash Table Key: Element Name Data: Queries, Whose Key is this Element Only Queries in Occurrence Table are Checked Further Thus, Each Input Document is Parsed Twice

22. XFilter: Experimental Setup ParameterRangeDescription P1,000 to 100,000Number of Profiles D1 to 10Maximum Depth of the XML Document and Queries W20% to 80%Probability of a Wildcard (‘*’) in the Element Nodes of the Queries F0 to 3Level of the Element Node Filter in the Queries. 0 Means There is No Element Node Filter. S1% to 100%Selectivity of the Element Node Filter θ0 and 1Skewedness of Element Names in Query Generation

23. Experiment 1.1: The Effect of Number of Profiles  Number of Profiles (Standing XPath Queries) Changes  Basic Algorithm Gives the Worst Performance  List Balance Improves  Prefiltering Leads to a Greater Speed-Up Than LB 2.6 % of Profiles Match a Given Document Basic Algorithm Examines 12% of Profiles Prefiltering Examines Only 3.5 % of Profiles

24. Experiment 1.2: The Effect of Number of Profiles  Number of Profiles Changes – Same as Before  Skewed Selection of Elements – Leads to Unbalanced Query Index (Hash Table) in Basic Algorithm  List Balance is Effective in Balancing the Hash Table

25. Experiment 2.1: The Effect of Depth  Maximum Depth of XML Documents and Queries Change  More Depth -> More Checking -> Greater Filtering Time  List Balance and Prefiltering Graphs cross at Depth 8. With Higher Depth, Less Prefiltering LB Benefits with More Choices of Pivot Elements

26. Experiment 2.2: The Effect of Depth  Maximum Depth of XML Documents and Queries Change  Skewed Selection of Elements  LB Effectively Balances the Skewed Hash Table  After Level 4, the Presence of Element Names in the Queries does not Change Much Due to Skewed Distribution. Workload Characteristics Remain Similar.

27. Experiment 3: The Effect of Wildcard  Wildcard (‘*’) Usage Probability in Queries Change  Prefiltering is Slower with More Wildcards Prefiltering Takes Extra Time Trying Filtering, but Prefiltering cannot Filter Out the Wildcards  However, it is Unlikely that Many Profiles will have such a High Proportion of Wildcards.

28. Experiment 4.1: The Effect of Filter  Injected a New Fixed Attribute Named dummy into the Documents with Certain Probability  Created a Simple Element Node Filter Containing Only that Fixed Attribute (e.g. [@dummy=“true”])  In this Experiment, a Single Element Node Filter is Placed in Different Levels of the Query with Fixed Query Selectivity of 10%  The Deeper the Filter, the Longer it Takes to Test

29. Experiment 4.2: The Effect of Filter  Filters are Placed at Level 2, with Varying Selectivity.  Logarithmic Scale on Selectivity  For All Algorithms, Performance is not Heavily Affected by Filter Selectivity

30. Summary of Results  These Experiments Demonstrate that, XFilter approach is scalable The Extensions Provide Substantial Improvements  List Balance is Effective When the Distribution of Elements in Queries is Highly Skewed  Prefiltering is Effective in Reducing the Number of Profiles to Examine  Combination of LB-Prefiltering Provides the Best Performance in All Cases Considering that Distribution of Elements in Queries of SDI Applications is Highly Skewed, and Prefiltering Requires a Space Overhead, Simple LB is Preferable in Many Practical Cases

31. Conclusions  XML Document Filtering System XFilter for Selective Dissemination of Information (SDI) Expressive Profiles in XPath Query Language Profile Indexing and Matching Algorithms Based on a FSM Approach Optimization Techniques  List Balancing  Prefiltering

32. Related Work: XTrie  Efficient Filtering of XML Documents with XPath Expression– ICDE 2002 Supports Complex XPath Expressions (As Opposed to Simple, Single-Path Specifications)  e.g. /a/b[c/d//e][g//e/f]//*/*/e/f Supports Both Ordered and Unordered Matching of XML Data  Ordered Matching: //a//b/*[following-sibling::d]/c Substring-Based Query Indexing 2 to 4 Times Faster Than XFilter