tailieunhanh - Managing and Mining Graph Data part 24

Managing and Mining Graph Data part 24 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. . | 2112 MANAGING AND MINING GRAPH DATA Cheng et al. in 11 12 consider AhD as a R-join like 0-join . and process a graph pattern matching as a sequence of R-joins. The issue isi how to select join ordci. They propose a dynamic programming algorithm to determine the R-join ordci in ill . They also propose an R-join -semijom approach in 12 The hasic idea ic tsi divide join-i ndcx based approach into two steps namely filter ant fetch. The fPSgep steps shares lite similasity with semijoin and the tench rItpp is io joim Cfieng et ah study how to select R-join -semijoin order by interleaving R-joint wilt R-semijoins uaing dynamic programming in 12 Wang nt al. in 35 propose a query graph Gq bared on the hash join appnoach. and considci how to ehare the ptocessing cost when it needs to process several Alist add Dlist stii iultitn j lye Waaa els al. propose three banic coin opcratiiis namely tT-HGtoin T-HGJoin and Bi-HGJoin. The IT-HGJoin pcocecses a subgraph of ci query nvith one descendant and multiple ancestors fon example AhD A BhD Tht process a sub-gcapf of si query with one anccrtor and muCtiple descendants for example AhC A AhD The BtiHGJoin processes tt compictc bipartite subgraph of a. query with multiple ancestor. and muitiple descendants for example A -C A A -D A B C a BhD. A geceral query grap h Gq will be processed by a sot of euhgtaph queries using ITeHGJoin T-HGJoin and Bi-HGJoin. 11. Conclusions and Summary In thin chapter we .resented ns turvey on reachability queries. We dis-cuised cevera- codingehased agpioachcs ilting iraversal dual-labeling tree cover. chain aovei path-tree cover. 2-hop cover and 3-hop cover approaches. We also addressed how ini xuppoti distance-aware queries such as to find the shortetl disliince between asvo nodes in a large directed graph using the 2-hop cover. and how to support gsaph pattern maechmg using the existing graph-bared codino seftnma. An future wonk. it hccomcs important how to use the gaaphihared codiag .