02206nas a2200229   4500008004100000245003900041210003900080260000900119300001400128490000600142520154200148653002501690653002701715653001701742653003301759653002001792100001201812700001101824700001201835700001201847856011701859       2007                        eng d00aClustering frequent graph patterns0 aClustering frequent graph patterns  c2007  a335 - 3460 v53 aIn recent years, graph mining has attracted much attention in the data mining community. Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summarize frequent graph patterns by a much smaller number of representative graph patterns. Several novel concepts such as δ-cover graph, jump value and δ-jump pattern are proposed for efficiently summarizing frequent graph patterns. Based on the property of all δ-jump patterns being representative graph patterns, we propose two efficient algorithms for summarizing frequent graph patterns, RP-FP and RP-GD. The RP-FP algorithm computes representative graph patterns from a set of closed frequent graph patterns, whereas the RP-GD algorithm directly mines representative graph patterns from graph databases. The RP-FP has tight approximation (summarization) bound but higher computational complexity. The RP-GD has no approximation bound guarantee but is far more efficient. We conducted extensive experimental studies using various real and synthetic datasels. Experimental results show that RP-FP and RP-GD are able to obtain compact summarization in both real and synthetic graph databases. When the number of closed graph patterns is very large, RP-GD is much more efficient than RP-FP, while achieving comparable summarization quality.10aClosed graph pattern10aFrequent graph pattern10aGraph mining10aRepresentative graph pattern10aδ-jump pattern1 aLiu, Y.1 aLi, J.1 aZhu, J.1 aGao, H.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-49149118250&partnerID=40&md5=cae15cbd3bb5b1a9e3f57a95dbe295b501708nas a2200217   4500008004100000245007200041210006900113260000900182300001200191490000600203520099800209653002701207653002901234653001601263653002101279653003401300100001401334700001301348700001201361856011701373       2007                        eng d00aEnergy efficient and QoS-aware tasks allocation for sensor networks0 aEnergy efficient and QoSaware tasks allocation for sensor networ  c2007  a88 - 940 v53 aCollaborative processing among sensors to fuffill given tasks is a promising solution to save significant energy in resource-limited wireless sensor networks (WSN). Quality-of-Service (QoS) such as lifetime and latency is largely affected by how tasks are mapped to sensors in network. Tasks allocation is a well-defined problem in the area of high performance computing and has been extensively studied in the past. Due to the limitations of WSN, existing algorithms cannot be directly used. In this paper, a novel nested optimization technique based on genetic algorithm is proposed to assign tasks onto sensors with minimal cost while meeting application's QoS requirements. Optimal solution can be achieved by incorporating task mapping, routing path allocation, communication scheduling, dynamic voltage scaling. Performance is evaluated through experiments with randomly generated Directed Acyclic Graphs (DAG) and experiments results show better solution compared with existing methods.10aGenetic algorithm (GA)10aQuality-of-service (QoS)10aTasks graph10aTasks scheduling10aWireless sensor network (WSN)1 aZhu, J.H.1 aLi, J.Z.1 aGao, H.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-44849126976&partnerID=40&md5=62663602655dc452190f9dd9b2a26fc5