tailieunhanh - Managing and Mining Graph Data part 18

Managing and Mining Graph Data part 18 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. . | 1552 MANAGING AND MINING GRAPH DATA aichicved by careful and other optimizations the results show that query processing in the graph alornain has clear advantages. 6. Related Work Graph Query Languages A nurnhsr of graph query iangtaagac is haive been historically available for liiloiirisr niinar arid manipuiatisg geaphr. GraphLog 12 represents both data and qucrica gaaphicaliy. Noder and edges are iabeled with one or more attributes. Edges in clic queries are matched itr either edges or paths in the data graphs. The paths can be regular expressionr with possibly negation. A query graph is a graph wcth a distinpuirhed edge. The distiaguished edge introduces a new neiation t m nodes. Tha query graph can he naturally translated into a Datalog program where the dietinguished edge corresponds to a new predicate . Sd graphical qutry consists of one or more query graphs each of winch can use defined in qupry graphs. The predicates among them tbus tocm a dependence arraph of the graphical query. GraphLog queries aiic graphical queriei ie which the dcpcntipncc graph must be acyclic. In terms rtf expresrive powep GraphLog wns shown to lee equivalent to strati lied linear Datalog fS . GraphLog ciocrs eiati provide any algebraic operations on graphs which ir imposiani for pnacttcai evaluation of queries. In catccory of7 ob cct-oricntcd daaabaseSi GOOD 16 is a graph-oriented object daia modci. GOOD models an object database instance by a directed la-beied graph. where obfecar ii the database rnd attributes on the objects are both represented an nodes of the graph. GOOD does not distinguish between anomic compote- and set obcectSi There arc only printable nodes and non-ptinnibic nodeSi Thi prrntahlc nodes are uccd tor graphical interfaces. As for edges tfaere art only fsinciionsti edges ind nonifunctional edges. The functional edges romt to unique nodes in that graph Both nodes and edges can have labels. which arc defined by ami .