1. hfli@csie.nctu.edu.tw1 Online Mining of Frequent Query Trees over XML Data Streams Hua-Fu Li*, Man-Kwan Shan and Suh-Yin Lee Department of Computer Science National Chiao-Tung University Hsinchu, Taiwan 300, R.O.C. http://www.csie.nctu.edu.tw/~hfli/ *: corresponding author

2. hfli@csie.nctu.edu.tw2 Outline Introduction Mining of Data Streams, Tree Mining Problem Definition Online Mining of Frequent Query Trees over XML Data Streams The Proposed Algorithm FQT-Stream (Frequent Query Trees of Streams) Conclusions and Future Work

3. hfli@csie.nctu.edu.tw3 Mining of Data Streams: Motivations Many Applications generate data streams Day to day business (credit card, ATM transactions, etc) Hot Web services (XML data, record and click streams) Telecommunication (call records) Financial market (stock exchange) Surveillance (sensor network, audio/video) System management (network events) Application characteristics Massive volumes of data (several terabytes) Records arrive at a rapid rate Data distribution changes on the fly What do we want to get from data streams ? Real time query answering, Statistics, and Pattern discovery

4. hfli@csie.nctu.edu.tw4 Mining of Data Streams: Computation Model Requirements of Mining Data Streams Single pass: each record is examined at most once Bounded storage: Limited Memory for storing synopsis Real-time: Per record processing time (to maintain synopsis) must be low Stream Mining Processor Synopsis in Memory Buffer (Approximate) Results Data Streams

5. hfli@csie.nctu.edu.tw5 Problem Definition of Frequent Query Tree Mining (1/2) XML Query Tree Stream (XQTS) A sequence of query trees (QTs) QT 1, QT 2, …, QT N N is tree id the latest incoming query tree Support of a Query Tree QT i sup(QT i ): the number of QTs in XQTS containing QT i as a subtree

6. hfli@csie.nctu.edu.tw6 Problem Definition of Frequent Query Tree Mining (2/2) A QT i is a Frequent Query Tree (FQT) if and only if sup(QTi) sN s is a user-defined minimum support threshold in the range of [0, 1] Our Task To mine the set of all frequent query trees (FQTs) by one scan of the XQTS Using as smaller memory as possible

7. hfli@csie.nctu.edu.tw7 Proposed Algorithm FQT-Stream (Frequent Query Trees of Streams) FQT-Stream consists of 5 phases 1. read a QT (Query Tree) from the buffer in the main memory 2. transform the QT into a new NQTS (Normalized Query Tree Sequence) representation 3. construct a in-memory summary data structure called FQT-forest (a forest of Frequent Query Trees) by projecting the NQTSs 4. prune the infrequent query trees from FQT-forest 5. find the set of all FQTs (Frequent Query Trees) from current FQT-forest Since phase 1 is straightforward, We focus on phases 2-5

8. hfli@csie.nctu.edu.tw8 Phase 2 of FQT-Stream: NQTS Transformation NQTS Transformation of QT Using DFS on the QT A sequence of triple (node-id, level, order) level: the level of the QT order: sequence order of the NQTS For example (5-NQTS in Figure 1)

9. hfli@csie.nctu.edu.tw9 Phase 3 of FQT-Stream: FQT- forest Construction (1/4) For each NQTS, 2 steps are performed to construct the FQT- forest Step 1: enumerate each NQTS into a set of sub-sequences using Order-Break (OB) technique OB is a level-wise method

10. hfli@csie.nctu.edu.tw10 Phase 3 of FQT-Stream: Step 1 of FQT-forest Construction (2/4) For example, a 5-NQTS = First, the 5-NQTS is broken into three 4- NQTSs These sequences are 1-OB (One Order Break) 1-OB sequences have one order break in the sequence order The original 5-NQTS is called 0-OB

11. hfli@csie.nctu.edu.tw11 Phase 3 of FQT-Stream: Step 1 of FQT-forest Construction (3/4) After delete the duplicates Three 4-NQTSs Two 3-NQTSs with One Order Break Two 3-NQTSs One 2-NQTS, Finally, the set of 1-OB contains 8 NQTSs

12. hfli@csie.nctu.edu.tw12 Phase 3 of FQT-Stream: Step 1 of FQT-forest Construction (4/4) Set of 2-OB is generated from the set of 1-OB For example 2-OB is generated from 1-OB Repeat this process until no candidate k- OB Property 1 The maximum size of order break is k-3, i.e., (k- 3)-OB, if the query tree has k nodes

13. hfli@csie.nctu.edu.tw13 Phase 3 of FQT-Stream: Step 2 of FQT-forest Construction (1/3) The OBs (0-OB, 1-OB, 2-OB) are projected and inserted into a FQT- forest using Incremental Projection (IP) technique A NQTS,, with i nodes is projected into i sub-NQTSs (also called node-suffix NQTSs),, …,, We use one field node-id to represent the fields (node-id, level, order) for simplicity

14. hfli@csie.nctu.edu.tw14 Phase 3 of FQT-Stream: Step 2 of FQT-forest Construction (2/3) Example of IP 1-OB: is projected into 4 node-suffix NQTSs as follows After projection, a tree structure checking is preformed If the level of the first node in a node-suffix NQTS is not the smallest level the node-suffix NQTS is deleted

15. hfli@csie.nctu.edu.tw15 Phase 3 of FQT-Stream: Step 2 of FQT-forest Construction (3/3) After tree structure checking The node-suffix NQTSs are inserted into FQT-forest Update the corresponding nodes supports FQT-forest consists of 2 parts FN-list A list of Frequent Nodes Each node X i in FN-list has a NQTS-tree (X i.NQTS-tree) NQTS-trees (trees of Normalized Query Tree Sequences) A sequence (NQTS) is represented by a path And its appearance frequent is maintained in the last of node of the path

16. hfli@csie.nctu.edu.tw16 Phase 4 of FQT-Stream: Infrequent Information Pruning In order to guarantee the limited space requirement Pruning Infrequent Information Pruning steps Check each node X i in the FN-list of FQT-forest If its sup(X i ) < sN delete X i and its NQTS-tree Check other NQTS-trees to prune these infrequent nodes

17. hfli@csie.nctu.edu.tw17 Phase 4 of FQT-Stream: Frequent Query Tree Mining Assume that there are k frequent nodes,, in the FN-list FQT-Stream traverses the X i.NQTS-tree ( i, i = 1, 2, …, k) to find the sequences with prefix X i whose estimated support is greater than or equal to sN in a DFS manner These frequent query trees are stored into a temporal list, called FQT-List

18. hfli@csie.nctu.edu.tw18 Conclusions and Future Work We propose an efficient one-pass algorithm FQT-Stream (Frequent Query Trees of Streams) To find the set of all frequent query trees over the entire history of online XML data streams Future Work Online Mining of Frequent Query Trees over Sliding Windows