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Managing and Mining Graph Data part 28

Managing and Mining Graph Data part 28 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. . | 2554 MANAGING AND MINING GRAPH DATA search on XML documents. Consider Figure 8.1 again. If we remove node C and tire two keyword nodes under Ct the remaining tree is still an answer to the query. Clearly tbis answet is independent of the answer C E SLCA x y yet is is nett cepresented by the SLCA semantics. XRank i3 . for exympte adopts different query semantics for keyword search. The set of answers to a query Q k1 kn is defined as ELCA k1 kn v Vkj 3c c is a chitd node of v A 3c E LCA k1 kn ard c A c A 8.2 c contains ki dtrectly err indirectl y ELCA ki kn contfins the tet of nodes thet contain at least one oc-cuttcnec of th of Hitt query ke t rsntrt ls. atlrtt excluding the sub-nodes that already centain ah oft the query keywords. Clear y. in Figure 8.1 we have A E ELCA k1 kn . More generally we have SLCA k1 kn C ELCA ki kn C LCA k1 kn ultKit yr semantics has a idrrici. smpaci on the complexity of query processing. For example anei aerirnr a keywoed query according to the ELCA query semantics is more cdmputationelly challenging than according to the SLCA query scmantice. iti the latter. Hie moment we know a node I hai a child c that contains all the keywords eve can immediately determine that node I is not an SI.CA node Howcvcti wt cannot determine that I is itot an IILCA node because I may contair keyword insiarccs that are not under c ard arc not under anti nolle that contains all key words 28 29 . 2.2 Answer Ranking It is clear llia i. accordiee to the kowest common ancestor LCA query se-manticr. potentiatty many antweos will be teturned for a keyword query. It is atoo ttaoy to tec that due to tint diffcrctcc of tfin nested XML structure where the keyword. arc embedded. nrot all ansit eso are equal. Thus it is important to devioe a mechanism to rank the aneevc s hated on their relevance to the query. In other w ordt fot cvcty given answer tree T o otniahtlett ah itrti keywords we evant to aasign a numerical score to T. Many approaches lor keyword search on XML .