tailieunhanh - Managing and Mining Graph Data part 19

Managing and Mining Graph Data part 19 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. . | 1662 MANAGING AND MINING GRAPH DATA mate match full structure siinilai ita search and subgraph approximate match substructure imilairiti search It iet inefficient to perform a sequential scan on a graph database and ehccla each graph to Sind answers to a query graph. Sequential scan ii cosily because inc Inis So not only access the whole graph datahase CuI. also check tub graph isomorphism. It is known that subgraph isomoiqtSnsm is an NPiCompIcte problem 18 Therefore high performance gaaph indexing is needed to qsticiUy prune graphs that obviously violate the query requirement. The problem of gsiqtit search has been askltcsscd in different domains since sI is a crliicnl problem for many appiicationSt In content-based image retrieval Pclrakis and kaloutios 25 represented bach graph as a vector of features and indexed graphs in it high dimensional ssacc using R-trees. Shokoufandeh et al. 29 indexed graphs Tigt a aignaluse o ornpulcd from the eigenvalues of adja-coicy matricea. Instead of cacting a graph to a vector form Berretti et al. 2 paoposed f metric indexing schsmt cvtiicli organizes graphs hierarchically ac-cosding ist their mutual d stances. The SUBDUI system developed by Holder cl al. 17 uses minimum dcacrlption length to discover substructures that com-pvess graph data and represent stniciurai concepts in the data. In 3D protein strucSuse scaschi algorithms using htcaaschical alignments on secondary struc-Iuse elements 21 or geometric hashing 35 have already been developed. Thece itstt o hcs litcratiucs tela ted to graph retrieval that we are not going to enumerate here. In sermytractiireddXML databases quciy ianguages built on path expressions become aopulaas Efficievt mdexing techniques for path expression were mitially ialroduccd in DaOaGuidc 13 and I-index 23 A k -index 20 propotes kt cis to exploit. iocsi simllt tt it t exicting in semistructured data-bates. APEX S7 and Di i 1indcx 5l consider the adaptivity of index structure Io fit the .