01890nas a2200193   4500008004100000245007900041210006900120260000900189300001400198490000700212520125000219653001401469653002001483653002001503653002601523100001601549700001401565856011701579       2012                        eng d00aDistributed weighted clustering of evolving sensor data streams with noise0 aDistributed weighted clustering of evolving sensor data streams   c2012  a410 - 4200 v103 aCollecting data from sensor nodes is the ultimate goal of Wireless Sensor Networks. This is performed by transmitting the sensed measurements to some data collecting station. In sensor nodes, radio communication is the dominating consumer of the energy resources which are usually limited. Summarizing the sensed data internally on sensor nodes and sending only the summaries will considerably save energy. Clustering is an established data mining technique for grouping objects based on similarity. For sensor networks, k-center clustering aims at grouping sensor measurements in groups, each contains similar measurements. In this paper we propose a novel resource-aware -center clustering algorithm called: SenClu. Our algorithm immediately detects new trends in the drifting sensor data stream and follows them. SenClu powerfully uses a lightweighted decaying technique that gives lower influence to old data. As sensor data are usually noisy, our algorithm is also outlier-aware. In thorough experiments on drifting synthetic and real world data sets, we show that SenClu outperforms two state-of-the-art algorithms by producing higher clustering quality and following trends in the stream, while consuming nearly the same amount of energy.10aAlgorithm10aData clustering10aData Processing10aWireless Sensor Nodes1 aHassani, M.1 aSeidl, T.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84874869330&partnerID=40&md5=29927101aa08b9c82193c09bb9c5030401374nas a2200205   4500008004100000245005100041210004800092260000900140300001400149490000600163520075400169653002000923653001900943653002100962653002800983653000801011100001501019700001701034856011701051       2006                        eng d00aTrends in data management: 'High availability'0 aTrends in data management High availability  c2006  a150 - 1540 v43 aIn today's highly competitive business environment, for most companies it is imperative that their business data is always accessible from their data servers in a seamlessly fault tolerant manner. DBMSs, which constitute the core of most business information systems, are attempting to implement this requirement through a feature often referred to in the data management literature as 'high availability'. Against the backdrop of a leading DBMS, Microsoft SQL Server 2005, we define the concept of high availability and examine the various approaches to high availability, namely, mirroring, clustering, replication, and log shipping, which should be of interest to database administrators and the students of database management system technology.10aData clustering10aData mirroring10aData replication10aDatabase administration10aSQL1 aBock, D.B.1 aBordoloi, B.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-44649115858&partnerID=40&md5=c675fe3325e4aad3ae202a7a0cd0b784