• Title/Summary/Keyword: ordered labeled tree

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A REFINEMENT FOR ORDERED LABELED TREES

  • Seo, Seunghyun;Shin, Heesung
    • Korean Journal of Mathematics
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    • v.20 no.2
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    • pp.255-261
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    • 2012
  • Let $\mathcal{O}_n$ be the set of ordered labeled trees on $\{0,\;{\ldots},\;n\}$. A maximal decreasing subtree of an ordered labeled tree is defined by the maximal ordered subtree from the root with all edges being decreasing. In this paper, we study a new refinement $\mathcal{O}_{n,k}$ of $\mathcal{O}_n$, which is the set of ordered labeled trees whose maximal decreasing subtree has $k+1$ vertices.

Mining of Frequent Structures over Streaming XML Data (스트리밍 XML 데이터의 빈발 구조 마이닝)

  • Hwang, Jeong-Hee
    • The KIPS Transactions:PartD
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    • v.15D no.1
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    • pp.23-30
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    • 2008
  • The basic research of context aware in ubiquitous environment is an internet technique and XML. The XML data of continuous stream type are popular in network application through the internet. And also there are researches related to query processing for streaming XML data. As a basic research to efficiently query, we propose not only a labeled ordered tree model representing the XML but also a mining method to extract frequent structures from streaming XML data. That is, XML data to continuously be input are modeled by a stream tree which is called by XFP_tree and we exactly extract the frequent structures from the XFP_tree of current window to mine recent data. The proposed method can be applied to the basis of the query processing and index method for XML stream data.

X-tree Diff: An Efficient Change Detection Algorithm for Tree-structured Data (X-tree Diff: 트리 기반 데이터를 위한 효율적인 변화 탐지 알고리즘)

  • Lee, Suk-Kyoon;Kim, Dong-Ah
    • The KIPS Transactions:PartC
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    • v.10C no.6
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    • pp.683-694
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    • 2003
  • We present X-tree Diff, a change detection algorithm for tree-structured data. Our work is motivated by need to monitor massive volume of web documents and detect suspicious changes, called defacement attack on web sites. From this context, our algorithm should be very efficient in speed and use of memory space. X-tree Diff uses a special ordered labeled tree, X-tree, to represent XML/HTML documents. X-tree nodes have a special field, tMD, which stores a 128-bit hash value representing the structure and data of subtrees, so match identical subtrees form the old and new versions. During this process, X-tree Diff uses the Rule of Delaying Ambiguous Matchings, implying that it perform exact matching where a node in the old version has one-to one corrspondence with the corresponding node in the new, by delaying all the others. It drastically reduces the possibility of wrong matchings. X-tree Diff propagates such exact matchings upwards in Step 2, and obtain more matchings downwsards from roots in Step 3. In step 4, nodes to ve inserted or deleted are decided, We aldo show thst X-tree Diff runs on O(n), woere n is the number of noses in X-trees, in worst case as well as in average case, This result is even better than that of BULD Diff algorithm, which is O(n log(n)) in worst case, We experimented X-tree Diff on reat data, which are about 11,000 home pages from about 20 wev sites, instead of synthetic documets manipulated for experimented for ex[erimentation. Currently, X-treeDiff algorithm is being used in a commeercial hacking detection system, called the WIDS(Web-Document Intrusion Detection System), which is to find changes occured in registered websites, and report suspicious changes to users.